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MobileCLIP: Fast Image-Text Models through Multi-Modal Reinforced Training | Pavan Kumar Anasosalu Vasu, Hadi Pouransari, Fartash Faghri, Raviteja Vemulapalli, Oncel Tuzel | Contrastive pre-training of image-text foundation models such as CLIP demonstrated excellent zero-shot performance and improved robustness on a wide range of downstream tasks. However these models utilize large transformer-based encoders with significant memory and latency overhead which pose challenges for deployment on mobile devices. In this work we introduce MobileCLIP - a new family of efficient image-text models optimized for runtime performance along with a novel and efficient training approach namely multi-modal reinforced training. The proposed training approach leverages knowledge transfer from an image captioning model and an ensemble of strong CLIP encoders to improve the accuracy of efficient models. Our approach avoids train-time compute overhead by storing the additional knowledge in a reinforced dataset. MobileCLIP sets a new state-of-the-art latency-accuracy tradeoff for zero-shot classification and retrieval tasks on several datasets. Our MobileCLIP-S2 variant is 2.3x faster while more accurate compared to previous best CLIP model based on ViT-B/16. We further demonstrate the effectiveness of our multi-modal reinforced training by training a CLIP model based on ViT-B/16 image backbone and achieving +2.9% average performance improvement on 38 evaluation benchmarks compared to the previous best. Moreover we show that the proposed approach achieves 10x-1000x improved learning efficiency when compared with non- reinforced CLIP training. Code and models are available at https://github.com/apple/ml-mobileclip | https://openaccess.thecvf.com/content/CVPR2024/papers/Vasu_MobileCLIP_Fast_Image-Text_Models_through_Multi-Modal_Reinforced_Training_CVPR_2024_paper.pdf | http://arxiv.org/abs/2311.17049 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Vasu_MobileCLIP_Fast_Image-Text_Models_through_Multi-Modal_Reinforced_Training_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Vasu_MobileCLIP_Fast_Image-Text_Models_through_Multi-Modal_Reinforced_Training_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Vasu_MobileCLIP_Fast_Image-Text_CVPR_2024_supplemental.pdf | null |
Drag Your Noise: Interactive Point-based Editing via Diffusion Semantic Propagation | Haofeng Liu, Chenshu Xu, Yifei Yang, Lihua Zeng, Shengfeng He | Point-based interactive editing serves as an essential tool to complement the controllability of existing generative models. A concurrent work DragDiffusion updates the diffusion latent map in response to user inputs causing global latent map alterations. This results in imprecise preservation of the original content and unsuccessful editing due to gradient vanishing. In contrast we present DragNoise offering robust and accelerated editing without retracing the latent map. The core rationale of DragNoise lies in utilizing the predicted noise output of each U-Net as a semantic editor. This approach is grounded in two critical observations: firstly the bottleneck features of U-Net inherently possess semantically rich features ideal for interactive editing; secondly high-level semantics established early in the denoising process show minimal variation in subsequent stages. Leveraging these insights DragNoise edits diffusion semantics in a single denoising step and efficiently propagates these changes ensuring stability and efficiency in diffusion editing. Comparative experiments reveal that DragNoise achieves superior control and semantic retention reducing the optimization time by over 50% compared to DragDiffusion. Our codes are available at https://github.com/haofengl/DragNoise. | https://openaccess.thecvf.com/content/CVPR2024/papers/Liu_Drag_Your_Noise_Interactive_Point-based_Editing_via_Diffusion_Semantic_Propagation_CVPR_2024_paper.pdf | http://arxiv.org/abs/2404.01050 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Liu_Drag_Your_Noise_Interactive_Point-based_Editing_via_Diffusion_Semantic_Propagation_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Liu_Drag_Your_Noise_Interactive_Point-based_Editing_via_Diffusion_Semantic_Propagation_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Liu_Drag_Your_Noise_CVPR_2024_supplemental.zip | null |
CDMAD: Class-Distribution-Mismatch-Aware Debiasing for Class-Imbalanced Semi-Supervised Learning | Hyuck Lee, Heeyoung Kim | Pseudo-label-based semi-supervised learning (SSL) algorithms trained on a class-imbalanced set face two cascading challenges: 1) Classifiers tend to be biased towards majority classes and 2) Biased pseudo-labels are used for training. It is difficult to appropriately re-balance the classifiers in SSL because the class distribution of an unlabeled set is often unknown and could be mismatched with that of a labeled set. We propose a novel class-imbalanced SSL algorithm called class-distribution-mismatch-aware debiasing (CDMAD). For each iteration of training CDMAD first assesses the classifier's biased degree towards each class by calculating the logits on an image without any patterns (e.g. solid color image) which can be considered irrelevant to the training set. CDMAD then refines biased pseudo-labels of the base SSL algorithm by ensuring the classifier's neutrality. CDMAD uses these refined pseudo-labels during the training of the base SSL algorithm to improve the quality of the representations. In the test phase CDMAD similarly refines biased class predictions on test samples. CDMAD can be seen as an extension of post-hoc logit adjustment to address a challenge of incorporating the unknown class distribution of the unlabeled set for re-balancing the biased classifier under class distribution mismatch. CDMAD ensures Fisher consistency for the balanced error. Extensive experiments verify the effectiveness of CDMAD. | https://openaccess.thecvf.com/content/CVPR2024/papers/Lee_CDMAD_Class-Distribution-Mismatch-Aware_Debiasing_for_Class-Imbalanced_Semi-Supervised_Learning_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.10391 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Lee_CDMAD_Class-Distribution-Mismatch-Aware_Debiasing_for_Class-Imbalanced_Semi-Supervised_Learning_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Lee_CDMAD_Class-Distribution-Mismatch-Aware_Debiasing_for_Class-Imbalanced_Semi-Supervised_Learning_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Lee_CDMAD_Class-Distribution-Mismatch-Aware_Debiasing_CVPR_2024_supplemental.pdf | null |
VideoCon: Robust Video-Language Alignment via Contrast Captions | Hritik Bansal, Yonatan Bitton, Idan Szpektor, Kai-Wei Chang, Aditya Grover | Despite being (pre)trained on a massive amount of data state-of-the-art video-language alignment models are not robust to semantically-plausible contrastive changes in the video captions. Our work addresses this by identifying a broad spectrum of contrast misalignments such as replacing entities actions and flipping event order which alignment models should be robust against. To this end we introduce the VideoCon a video-language alignment dataset constructed by a large language model that generates plausible contrast video captions and explanations for differences between original and contrast video captions. Then a generative video-language model is finetuned with VideoCon to assess video-language entailment and generate explanations. Our VideoCon-based alignment model significantly outperforms current models. It exhibits a 12-point increase in AUC for the video-language alignment task on human-generated contrast captions. Finally our model sets new state of the art zero-shot performance in temporally-extensive video-language tasks such as text-to-video retrieval (SSv2-Temporal) and video question answering (ATP-Hard). Moreover our model shows superior performance on novel videos and human-crafted captions and explanations. | https://openaccess.thecvf.com/content/CVPR2024/papers/Bansal_VideoCon_Robust_Video-Language_Alignment_via_Contrast_Captions_CVPR_2024_paper.pdf | http://arxiv.org/abs/2311.10111 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Bansal_VideoCon_Robust_Video-Language_Alignment_via_Contrast_Captions_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Bansal_VideoCon_Robust_Video-Language_Alignment_via_Contrast_Captions_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Bansal_VideoCon_Robust_Video-Language_CVPR_2024_supplemental.pdf | null |
PanoPose: Self-supervised Relative Pose Estimation for Panoramic Images | Diantao Tu, Hainan Cui, Xianwei Zheng, Shuhan Shen | Scaled relative pose estimation i.e. estimating relative rotation and scaled relative translation between two images has always been a major challenge in global Structure-from-Motion (SfM). This difficulty arises because the two-view relative translation computed by traditional geometric vision methods e.g. the five-point algorithm is scaleless. Many researchers have proposed diverse translation averaging methods to solve this problem. Instead of solving the problem in the motion averaging phase we focus on estimating scaled relative pose with the help of panoramic cameras and deep neural networks. In this paper a novel network namely PanoPose is proposed to estimate the relative motion in a fully self-supervised manner and a global SfM pipeline is built for panorama images. The proposed PanoPose comprises a depth-net and a pose-net with self-supervision achieved by reconstructing the reference image from its neighboring images based on the estimated depth and relative pose. To maintain precise pose estimation under large viewing angle differences we randomly rotate the panoramic images and pre-train the pose-net with images before and after the rotation. To enhance scale accuracy a fusion block is introduced to incorporate depth information into pose estimation. Extensive experiments on panoramic SfM datasets demonstrate the effectiveness of PanoPose compared with state-of-the-arts. | https://openaccess.thecvf.com/content/CVPR2024/papers/Tu_PanoPose_Self-supervised_Relative_Pose_Estimation_for_Panoramic_Images_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Tu_PanoPose_Self-supervised_Relative_Pose_Estimation_for_Panoramic_Images_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Tu_PanoPose_Self-supervised_Relative_Pose_Estimation_for_Panoramic_Images_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Tu_PanoPose_Self-supervised_Relative_CVPR_2024_supplemental.pdf | null |
ContextSeg: Sketch Semantic Segmentation by Querying the Context with Attention | Jiawei Wang, Changjian Li | Sketch semantic segmentation is a well-explored and pivotal problem in computer vision involving the assignment of predefined part labels to individual strokes. This paper presents ContextSeg - a simple yet highly effective approach to tackling this problem with two stages. In the first stage to better encode the shape and positional information of strokes we propose to predict an extra dense distance field in an autoencoder network to reinforce structural information learning. In the second stage we treat an entire stroke as a single entity and label a group of strokes within the same semantic part using an autoregressive Transformer with the default attention mechanism. By group-based labeling our method can fully leverage the context information when making decisions for the remaining groups of strokes. Our method achieves the best segmentation accuracy compared with state-of-the-art approaches on two representative datasets and has been extensively evaluated demonstrating its superior performance. Additionally we offer insights into solving part imbalance in training data and the preliminary experiment on cross-category training which can inspire future research in this field. | https://openaccess.thecvf.com/content/CVPR2024/papers/Wang_ContextSeg_Sketch_Semantic_Segmentation_by_Querying_the_Context_with_Attention_CVPR_2024_paper.pdf | http://arxiv.org/abs/2311.16682 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Wang_ContextSeg_Sketch_Semantic_Segmentation_by_Querying_the_Context_with_Attention_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Wang_ContextSeg_Sketch_Semantic_Segmentation_by_Querying_the_Context_with_Attention_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Wang_ContextSeg_Sketch_Semantic_CVPR_2024_supplemental.pdf | null |
Describing Differences in Image Sets with Natural Language | Lisa Dunlap, Yuhui Zhang, Xiaohan Wang, Ruiqi Zhong, Trevor Darrell, Jacob Steinhardt, Joseph E. Gonzalez, Serena Yeung-Levy | How do two sets of images differ? Discerning set-level differences is crucial for understanding model behaviors and analyzing datasets yet manually sifting through thousands of images is impractical. To aid in this discovery process we explore the task of automatically describing the differences between two sets of images which we term Set Difference Captioning. This task takes in image sets \mathcal D _A and \mathcal D _B and outputs a description that is more often true on \mathcal D _A than \mathcal D _B. We outline a two-stage approach that first proposes candidate difference descriptions from image sets and then re-ranks the candidates by checking how well they can differentiate the two sets. We introduce VisDiff which first captions the images and prompts a language model to propose candidate descriptions then re-ranks these descriptions using CLIP. To evaluate VisDiff we collect VisDiffBench a dataset with 187 paired image sets with ground truth difference descriptions. We apply VisDiff to various domains such as comparing datasets (e.g. ImageNet vs. ImageNetV2) comparing classification models (e.g. zero-shot CLIP vs. supervised ResNet) characterizing differences between generative models (e.g. StableDiffusionV1 and V2) and discovering what makes images memorable. Using VisDiff we are able to find interesting and previously unknown differences in datasets and models demonstrating its utility in revealing nuanced insights. | https://openaccess.thecvf.com/content/CVPR2024/papers/Dunlap_Describing_Differences_in_Image_Sets_with_Natural_Language_CVPR_2024_paper.pdf | http://arxiv.org/abs/2312.02974 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Dunlap_Describing_Differences_in_Image_Sets_with_Natural_Language_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Dunlap_Describing_Differences_in_Image_Sets_with_Natural_Language_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Dunlap_Describing_Differences_in_CVPR_2024_supplemental.pdf | null |
Discovering and Mitigating Visual Biases through Keyword Explanation | Younghyun Kim, Sangwoo Mo, Minkyu Kim, Kyungmin Lee, Jaeho Lee, Jinwoo Shin | Addressing biases in computer vision models is crucial for real-world AI deployments. However mitigating visual biases is challenging due to their unexplainable nature often identified indirectly through visualization or sample statistics which necessitates additional human supervision for interpretation. To tackle this issue we propose the Bias-to-Text (B2T) framework which interprets visual biases as keywords. Specifically we extract common keywords from the captions of mispredicted images to identify potential biases in the model. We then validate these keywords by measuring their similarity to the mispredicted images using a vision-language scoring model. The keyword explanation form of visual bias offers several advantages such as a clear group naming for bias discovery and a natural extension for debiasing using these group names. Our experiments demonstrate that B2T can identify known biases such as gender bias in CelebA background bias in Waterbirds and distribution shifts in ImageNet-R/C. Additionally B2T uncovers novel biases in larger datasets such as Dollar Street and ImageNet. For example we discovered a contextual bias between \keyword bee and \keyword flower in ImageNet. We also highlight various applications of B2T keywords including debiased training CLIP prompting and model comparison. | https://openaccess.thecvf.com/content/CVPR2024/papers/Kim_Discovering_and_Mitigating_Visual_Biases_through_Keyword_Explanation_CVPR_2024_paper.pdf | http://arxiv.org/abs/2301.11104 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Kim_Discovering_and_Mitigating_Visual_Biases_through_Keyword_Explanation_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Kim_Discovering_and_Mitigating_Visual_Biases_through_Keyword_Explanation_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Kim_Discovering_and_Mitigating_CVPR_2024_supplemental.pdf | null |
Robust Emotion Recognition in Context Debiasing | Dingkang Yang, Kun Yang, Mingcheng Li, Shunli Wang, Shuaibing Wang, Lihua Zhang | Context-aware emotion recognition (CAER) has recently boosted the practical applications of affective computing techniques in unconstrained environments. Mainstream CAER methods invariably extract ensemble representations from diverse contexts and subject-centred characteristics to perceive the target person's emotional state. Despite advancements the biggest challenge remains due to context bias interference. The harmful bias forces the models to rely on spurious correlations between background contexts and emotion labels in likelihood estimation causing severe performance bottlenecks and confounding valuable context priors. In this paper we propose a counterfactual emotion inference (CLEF) framework to address the above issue. Specifically we first formulate a generalized causal graph to decouple the causal relationships among the variables in CAER. Following the causal graph CLEF introduces a non-invasive context branch to capture the adverse direct effect caused by the context bias. During the inference we eliminate the direct context effect from the total causal effect by comparing factual and counterfactual outcomes resulting in bias mitigation and robust prediction. As a model-agnostic framework CLEF can be readily integrated into existing methods bringing consistent performance gains. | https://openaccess.thecvf.com/content/CVPR2024/papers/Yang_Robust_Emotion_Recognition_in_Context_Debiasing_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.05963 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Yang_Robust_Emotion_Recognition_in_Context_Debiasing_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Yang_Robust_Emotion_Recognition_in_Context_Debiasing_CVPR_2024_paper.html | CVPR 2024 | null | null |
Fully Geometric Panoramic Localization | Junho Kim, Jiwon Jeong, Young Min Kim | We introduce a lightweight and accurate localization method that only utilizes the geometry of 2D-3D lines. Given a pre-captured 3D map our approach localizes a panorama image taking advantage of the holistic 360 degree view. The system mitigates potential privacy breaches or domain discrepancies by avoiding trained or hand-crafted visual descriptors. However as lines alone can be ambiguous we express distinctive yet compact spatial contexts from relationships between lines namely the dominant directions of parallel lines and the intersection between non-parallel lines. The resulting representations are efficient in processing time and memory compared to conventional visual descriptor-based methods. Given the groups of dominant line directions and their intersections we accelerate the search process to test thousands of pose candidates in less than a millisecond without sacrificing accuracy. We empirically show that the proposed 2D-3D matching can localize panoramas for challenging scenes with similar structures dramatic domain shifts or illumination changes. Our fully geometric approach does not involve extensive parameter tuning or neural network training making it a practical algorithm that can be readily deployed in the real world. Project page including the code is available through this link: https://82magnolia.github.io/fgpl/. | https://openaccess.thecvf.com/content/CVPR2024/papers/Kim_Fully_Geometric_Panoramic_Localization_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.19904 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Kim_Fully_Geometric_Panoramic_Localization_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Kim_Fully_Geometric_Panoramic_Localization_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Kim_Fully_Geometric_Panoramic_CVPR_2024_supplemental.pdf | null |
CAPE: CAM as a Probabilistic Ensemble for Enhanced DNN Interpretation | Townim Faisal Chowdhury, Kewen Liao, Vu Minh Hieu Phan, Minh-Son To, Yutong Xie, Kevin Hung, David Ross, Anton van den Hengel, Johan W. Verjans, Zhibin Liao | Deep Neural Networks (DNNs) are widely used for visual classification tasks but their complex computation process and black-box nature hinder decision transparency and interpretability. Class activation maps (CAMs) and recent variants provide ways to visually explain the DNN decision-making process by displaying 'attention' heatmaps of the DNNs. Nevertheless the CAM explanation only offers relative attention information that is on an attention heatmap we can interpret which image region is more or less important than the others. However these regions cannot be meaningfully compared across classes and the contribution of each region to the model's class prediction is not revealed. To address these challenges that ultimately lead to better DNN Interpretation in this paper we propose CAPE a novel reformulation of CAM that provides a unified and probabilistically meaningful assessment of the contributions of image regions. We quantitatively and qualitatively compare CAPE with state-of-the-art CAM methods on CUB and ImageNet benchmark datasets to demonstrate enhanced interpretability. We also test on a cytology imaging dataset depicting a challenging Chronic Myelomonocytic Leukemia (CMML) diagnosis problem. Code is available at:https://github.com/AIML-MED/CAPE. | https://openaccess.thecvf.com/content/CVPR2024/papers/Chowdhury_CAPE_CAM_as_a_Probabilistic_Ensemble_for_Enhanced_DNN_Interpretation_CVPR_2024_paper.pdf | http://arxiv.org/abs/2404.02388 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Chowdhury_CAPE_CAM_as_a_Probabilistic_Ensemble_for_Enhanced_DNN_Interpretation_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Chowdhury_CAPE_CAM_as_a_Probabilistic_Ensemble_for_Enhanced_DNN_Interpretation_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Chowdhury_CAPE_CAM_as_CVPR_2024_supplemental.pdf | null |
NeRF Director: Revisiting View Selection in Neural Volume Rendering | Wenhui Xiao, Rodrigo Santa Cruz, David Ahmedt-Aristizabal, Olivier Salvado, Clinton Fookes, Leo Lebrat | Neural Rendering representations have significantly contributed to the field of 3D computer vision. Given their potential considerable efforts have been invested to improve their performance. Nonetheless the essential question of selecting training views is yet to be thoroughly investigated. This key aspect plays a vital role in achieving high-quality results and aligns with the well-known tenet of deep learning: "garbage in garbage out". In this paper we first illustrate the importance of view selection by demonstrating how a simple rotation of the test views within the most pervasive NeRF dataset can lead to consequential shifts in the performance rankings of state-of-the-art techniques. To address this challenge we introduce a unified framework for view selection methods and devise a thorough benchmark to assess its impact. Significant improvements can be achieved without leveraging error or uncertainty estimation but focusing on uniform view coverage of the reconstructed object resulting in a training-free approach. Using this technique we show that high-quality renderings can be achieved faster by using fewer views. We conduct extensive experiments on both synthetic datasets and realistic data to demonstrate the effectiveness of our proposed method compared with random conventional error-based and uncertainty-guided view selection. | https://openaccess.thecvf.com/content/CVPR2024/papers/Xiao_NeRF_Director_Revisiting_View_Selection_in_Neural_Volume_Rendering_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Xiao_NeRF_Director_Revisiting_View_Selection_in_Neural_Volume_Rendering_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Xiao_NeRF_Director_Revisiting_View_Selection_in_Neural_Volume_Rendering_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Xiao_NeRF_Director_Revisiting_CVPR_2024_supplemental.pdf | null |
Taming the Tail in Class-Conditional GANs: Knowledge Sharing via Unconditional Training at Lower Resolutions | Saeed Khorram, Mingqi Jiang, Mohamad Shahbazi, Mohamad H. Danesh, Li Fuxin | Despite extensive research on training generative adversarial networks (GANs) with limited training data learning to generate images from long-tailed training distributions remains fairly unexplored. In the presence of imbalanced multi-class training data GANs tend to favor classes with more samples leading to the generation of low quality and less diverse samples in tail classes. In this study we aim to improve the training of class-conditional GANs with long-tailed data. We propose a straightforward yet effective method for knowledge sharing allowing tail classes to borrow from the rich information from classes with more abundant training data. More concretely we propose modifications to existing class-conditional GAN architectures to ensure that the lower-resolution layers of the generator are trained entirely unconditionally while reserving class-conditional generation for the higher-resolution layers. Experiments on several long-tail benchmarks and GAN architectures demonstrate a significant improvement over existing methods in both the diversity and fidelity of the generated images. The code is available at https://github.com/khorrams/utlo. | https://openaccess.thecvf.com/content/CVPR2024/papers/Khorram_Taming_the_Tail_in_Class-Conditional_GANs_Knowledge_Sharing_via_Unconditional_CVPR_2024_paper.pdf | http://arxiv.org/abs/2402.17065 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Khorram_Taming_the_Tail_in_Class-Conditional_GANs_Knowledge_Sharing_via_Unconditional_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Khorram_Taming_the_Tail_in_Class-Conditional_GANs_Knowledge_Sharing_via_Unconditional_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Khorram_Taming_the_Tail_CVPR_2024_supplemental.pdf | null |
VideoSwap: Customized Video Subject Swapping with Interactive Semantic Point Correspondence | Yuchao Gu, Yipin Zhou, Bichen Wu, Licheng Yu, Jia-Wei Liu, Rui Zhao, Jay Zhangjie Wu, David Junhao Zhang, Mike Zheng Shou, Kevin Tang | Current diffusion-based video editing primarily focuses on structure-preserved editing by utilizing various dense correspondences to ensure temporal consistency and motion alignment. However these approaches are often ineffective when the target edit involves a shape change. To embark on video editing with shape change we explore customized video subject swapping in this work where we aim to replace the main subject in a source video with a target subject having a distinct identity and potentially different shape. In contrast to previous methods that rely on dense correspondences we introduce the VideoSwap framework that exploits semantic point correspondences inspired by our observation that only a small number of semantic points are necessary to align the subject's motion trajectory and modify its shape. We also introduce various user-point interactions (e.g. removing points and dragging points) to address various semantic point correspondence. Extensive experiments demonstrate state-of-the-art video subject swapping results across a variety of real-world videos. | https://openaccess.thecvf.com/content/CVPR2024/papers/Gu_VideoSwap_Customized_Video_Subject_Swapping_with_Interactive_Semantic_Point_Correspondence_CVPR_2024_paper.pdf | http://arxiv.org/abs/2312.02087 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Gu_VideoSwap_Customized_Video_Subject_Swapping_with_Interactive_Semantic_Point_Correspondence_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Gu_VideoSwap_Customized_Video_Subject_Swapping_with_Interactive_Semantic_Point_Correspondence_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Gu_VideoSwap_Customized_Video_CVPR_2024_supplemental.pdf | null |
SonicVisionLM: Playing Sound with Vision Language Models | Zhifeng Xie, Shengye Yu, Qile He, Mengtian Li | There has been a growing interest in the task of generating sound for silent videos primarily because of its practicality in streamlining video post-production. However existing methods for video-sound generation attempt to directly create sound from visual representations which can be challenging due to the difficulty of aligning visual representations with audio representations. In this paper we present SonicVisionLM a novel framework aimed at generating a wide range of sound effects by leveraging vision-language models(VLMs). Instead of generating audio directly from video we use the capabilities of powerful VLMs. When provided with a silent video our approach first identifies events within the video using a VLM to suggest possible sounds that match the video content. This shift in approach transforms the challenging task of aligning image and audio into more well-studied sub-problems of aligning image-to-text and text-to-audio through the popular diffusion models. To improve the quality of audio recommendations with LLMs we have collected an extensive dataset that maps text descriptions to specific sound effects and developed a time-controlled audio adapter. Our approach surpasses current state-of-the-art methods for converting video to audio enhancing synchronization with the visuals and improving alignment between audio and video components. Project page: https://yusiissy.github.io/SonicVisionLM.github.io/ | https://openaccess.thecvf.com/content/CVPR2024/papers/Xie_SonicVisionLM_Playing_Sound_with_Vision_Language_Models_CVPR_2024_paper.pdf | http://arxiv.org/abs/2401.04394 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Xie_SonicVisionLM_Playing_Sound_with_Vision_Language_Models_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Xie_SonicVisionLM_Playing_Sound_with_Vision_Language_Models_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Xie_SonicVisionLM_Playing_Sound_CVPR_2024_supplemental.pdf | null |
Multi-Space Alignments Towards Universal LiDAR Segmentation | Youquan Liu, Lingdong Kong, Xiaoyang Wu, Runnan Chen, Xin Li, Liang Pan, Ziwei Liu, Yuexin Ma | A unified and versatile LiDAR segmentation model with strong robustness and generalizability is desirable for safe autonomous driving perception. This work presents M3Net a one-of-a-kind framework for fulfilling multi-task multi-dataset multi-modality LiDAR segmentation in a universal manner using just a single set of parameters. To better exploit data volume and diversity we first combine large-scale driving datasets acquired by different types of sensors from diverse scenes and then conduct alignments in three spaces namely data feature and label spaces during the training. As a result M3Net is capable of taming heterogeneous data for training state-of-the-art LiDAR segmentation models. Extensive experiments on twelve LiDAR segmentation datasets verify our effectiveness. Notably using a shared set of parameters M3Net achieves 75.1% 83.1% and 72.4% mIoU scores respectively on the official benchmarks of SemanticKITTI nuScenes and Waymo Open. | https://openaccess.thecvf.com/content/CVPR2024/papers/Liu_Multi-Space_Alignments_Towards_Universal_LiDAR_Segmentation_CVPR_2024_paper.pdf | http://arxiv.org/abs/2405.01538 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Liu_Multi-Space_Alignments_Towards_Universal_LiDAR_Segmentation_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Liu_Multi-Space_Alignments_Towards_Universal_LiDAR_Segmentation_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Liu_Multi-Space_Alignments_Towards_CVPR_2024_supplemental.pdf | null |
DiffuScene: Denoising Diffusion Models for Generative Indoor Scene Synthesis | null | null | null | null | null | https://openaccess.thecvf.com/content/CVPR2024/html/Tang_DiffuScene_Denoising_Diffusion_Models_for_Generative_Indoor_Scene_Synthesis_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Tang_DiffuScene_Denoising_Diffusion_Models_for_Generative_Indoor_Scene_Synthesis_CVPR_2024_paper.html | CVPR 2024 | null | null |
Hierarchical Histogram Threshold Segmentation - Auto-terminating High-detail Oversegmentation | Thomas V. Chang, Simon Seibt, Bartosz von Rymon Lipinski | Superpixels play a crucial role in image processing by partitioning an image into clusters of pixels with similar visual attributes. This facilitates subsequent image processing tasks offering computational advantages over the manipulation of individual pixels. While numerous oversegmentation techniques have emerged in recent years many rely on predefined initialization and termination criteria. In this paper a novel top-down superpixel segmentation algorithm called Hierarchical Histogram Threshold Segmentation (HHTS) is introduced. It eliminates the need for initialization and implements auto-termination outperforming state-of-the-art methods w.r.t boundary recall. This is achieved by iteratively partitioning individual pixel segments into foreground and background and applying intensity thresholding across multiple color channels. The underlying iterative process constructs a superpixel hierarchy that adapts to local detail distributions until color information exhaustion. Experimental results demonstrate the superiority of the proposed approach in terms of boundary adherence while maintaining competitive runtime performance on the BSDS500 and NYUV2 datasets. Furthermore an application of HHTS in refining machine learning-based semantic segmentation masks produced by the Segment Anything Foundation Model (SAM) is presented. | https://openaccess.thecvf.com/content/CVPR2024/papers/Chang_Hierarchical_Histogram_Threshold_Segmentation_-_Auto-terminating_High-detail_Oversegmentation_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Chang_Hierarchical_Histogram_Threshold_Segmentation_-_Auto-terminating_High-detail_Oversegmentation_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Chang_Hierarchical_Histogram_Threshold_Segmentation_-_Auto-terminating_High-detail_Oversegmentation_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Chang_Hierarchical_Histogram_Threshold_CVPR_2024_supplemental.pdf | null |
Once for Both: Single Stage of Importance and Sparsity Search for Vision Transformer Compression | Hancheng Ye, Chong Yu, Peng Ye, Renqiu Xia, Yansong Tang, Jiwen Lu, Tao Chen, Bo Zhang | Recent Vision Transformer Compression (VTC) works mainly follow a two-stage scheme where the importance score of each model unit is first evaluated or preset in each submodule followed by the sparsity score evaluation according to the target sparsity constraint. Such a separate evaluation process induces the gap between importance and sparsity score distributions thus causing high search costs for VTC. In this work for the first time we investigate how to integrate the evaluations of importance and sparsity scores into a single stage searching the optimal subnets in an efficient manner. Specifically we present OFB a cost-efficient approach that simultaneously evaluates both importance and sparsity scores termed Once for Both (OFB) for VTC. First a bi-mask scheme is developed by entangling the importance score and the differentiable sparsity score to jointly determine the pruning potential (prunability) of each unit. Such a bi-mask search strategy is further used together with a proposed adaptive one-hot loss to realize the progressive-and-efficient search for the most important subnet. Finally Progressive Masked Image Modeling (PMIM) is proposed to regularize the feature space to be more representative during the search process which may be degraded by the dimension reduction. Extensive experiments demonstrate that OFB can achieve superior compression performance over state-of-the-art searching-based and pruning-based methods under various Vision Transformer architectures meanwhile promoting search efficiency significantly e.g. costing one GPU search day for the compression of DeiT-S on ImageNet-1K. | https://openaccess.thecvf.com/content/CVPR2024/papers/Ye_Once_for_Both_Single_Stage_of_Importance_and_Sparsity_Search_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.15835 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Ye_Once_for_Both_Single_Stage_of_Importance_and_Sparsity_Search_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Ye_Once_for_Both_Single_Stage_of_Importance_and_Sparsity_Search_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Ye_Once_for_Both_CVPR_2024_supplemental.pdf | null |
As-Plausible-As-Possible: Plausibility-Aware Mesh Deformation Using 2D Diffusion Priors | Seungwoo Yoo, Kunho Kim, Vladimir G. Kim, Minhyuk Sung | We present As-Plausible-as-Possible (APAP) mesh deformation technique that leverages 2D diffusion priors to preserve the plausibility of a mesh under user-controlled deformation. Our framework uses per-face Jacobians to represent mesh deformations where mesh vertex coordinates are computed via a differentiable Poisson Solve. The deformed mesh is rendered and the resulting 2D image is used in the Score Distillation Sampling (SDS) process which enables extracting meaningful plausibility priors from a pretrained 2D diffusion model. To better preserve the identity of the edited mesh we fine-tune our 2D diffusion model with LoRA. Gradients extracted by SDS and a user-prescribed handle displacement are then backpropagated to the per-face Jacobians and we use iterative gradient descent to compute the final deformation that balances between the user edit and the output plausibility. We evaluate our method with 2D and 3D meshes and demonstrate qualitative and quantitative improvements when using plausibility priors over geometry-preservation or distortion-minimization priors used by previous techniques. Our project page is at: https://as-plausible-aspossible.github.io/ | https://openaccess.thecvf.com/content/CVPR2024/papers/Yoo_As-Plausible-As-Possible_Plausibility-Aware_Mesh_Deformation_Using_2D_Diffusion_Priors_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Yoo_As-Plausible-As-Possible_Plausibility-Aware_Mesh_Deformation_Using_2D_Diffusion_Priors_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Yoo_As-Plausible-As-Possible_Plausibility-Aware_Mesh_Deformation_Using_2D_Diffusion_Priors_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Yoo_As-Plausible-As-Possible_Plausibility-Aware_Mesh_CVPR_2024_supplemental.pdf | null |
MCNet: Rethinking the Core Ingredients for Accurate and Efficient Homography Estimation | Haokai Zhu, Si-Yuan Cao, Jianxin Hu, Sitong Zuo, Beinan Yu, Jiacheng Ying, Junwei Li, Hui-Liang Shen | We propose Multiscale Correlation searching homography estimation Network namely MCNet an iterative deep homography estimation architecture. Different from previous approaches that achieve iterative refinement by correlation searching within a single scale MCNet combines the multiscale strategy with correlation searching incurring nearly ignored computational overhead. Moreover MCNet adopts a Fine-Grained Optimization loss function named FGO loss to further boost the network training at the convergent stage which can improve the estimation accuracy without additional computational overhead. According to our experiments using the above two simple strategies can produce significant homography estimation accuracy with considerable efficiency. We show that MCNet achieves state-of-the-art performance on a variety of datasets including common scene MSCOCO cross-modal scene GoogleEarth and GoogleMap and dynamic scene SPID. Compared to the previous SOTA method 2-scale RHWF our MCNet reduces inference time FLOPs parameter cost and memory cost by 78.9% 73.5% 34.1% and 33.2% respectively while achieving 20.5% (MSCOCO) 43.4% (GoogleEarth) and 41.1% (GoogleMap) mean average corner error (MACE) reduction. Source code is available at https://github.com/zjuzhk/MCNet. | https://openaccess.thecvf.com/content/CVPR2024/papers/Zhu_MCNet_Rethinking_the_Core_Ingredients_for_Accurate_and_Efficient_Homography_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Zhu_MCNet_Rethinking_the_Core_Ingredients_for_Accurate_and_Efficient_Homography_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Zhu_MCNet_Rethinking_the_Core_Ingredients_for_Accurate_and_Efficient_Homography_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Zhu_MCNet_Rethinking_the_CVPR_2024_supplemental.zip | null |
ECLIPSE: Efficient Continual Learning in Panoptic Segmentation with Visual Prompt Tuning | Beomyoung Kim, Joonsang Yu, Sung Ju Hwang | Panoptic segmentation combining semantic and instance segmentation stands as a cutting-edge computer vision task. Despite recent progress with deep learning models the dynamic nature of real-world applications necessitates continual learning where models adapt to new classes (plasticity) over time without forgetting old ones (catastrophic forgetting). Current continual segmentation methods often rely on distillation strategies like knowledge distillation and pseudo-labeling which are effective but result in increased training complexity and computational overhead. In this paper we introduce a novel and efficient method for continual panoptic segmentation based on Visual Prompt Tuning dubbed ECLIPSE. Our approach involves freezing the base model parameters and fine-tuning only a small set of prompt embeddings addressing both catastrophic forgetting and plasticity and significantly reducing the trainable parameters. To mitigate inherent challenges such as error propagation and semantic drift in continual segmentation we propose logit manipulation to effectively leverage common knowledge across the classes. Experiments on ADE20K continual panoptic segmentation benchmark demonstrate the superiority of ECLIPSE notably its robustness against catastrophic forgetting and its reasonable plasticity achieving a new state-of-the-art. The code is available at https://github.com/clovaai/ECLIPSE. | https://openaccess.thecvf.com/content/CVPR2024/papers/Kim_ECLIPSE_Efficient_Continual_Learning_in_Panoptic_Segmentation_with_Visual_Prompt_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.20126 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Kim_ECLIPSE_Efficient_Continual_Learning_in_Panoptic_Segmentation_with_Visual_Prompt_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Kim_ECLIPSE_Efficient_Continual_Learning_in_Panoptic_Segmentation_with_Visual_Prompt_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Kim_ECLIPSE_Efficient_Continual_CVPR_2024_supplemental.pdf | null |
Boosting Continual Learning of Vision-Language Models via Mixture-of-Experts Adapters | Jiazuo Yu, Yunzhi Zhuge, Lu Zhang, Ping Hu, Dong Wang, Huchuan Lu, You He | Continual learning can empower vision-language models to continuously acquire new knowledge without the need for access to the entire historical dataset. However mitigating the performance degradation in large-scale models is non-trivial due to (i) parameter shifts throughout lifelong learning and (ii) significant computational burdens associated with full-model tuning. In this work we present a parameter-efficient continual learning framework to alleviate long-term forgetting in incremental learning with vision-language models. Our approach involves the dynamic expansion of a pre-trained CLIP model through the integration of Mixture-of-Experts (MoE) adapters in response to new tasks. To preserve the zero-shot recognition capability of vision-language models we further introduce a Distribution Discriminative Auto-Selector (DDAS) that automatically routes in-distribution and out-of-distribution inputs to the MoE Adapter and the original CLIP respectively. Through extensive experiments across various settings our proposed method consistently outperforms previous state-of-the-art approaches while concurrently reducing parameter training burdens by 60%. | https://openaccess.thecvf.com/content/CVPR2024/papers/Yu_Boosting_Continual_Learning_of_Vision-Language_Models_via_Mixture-of-Experts_Adapters_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.11549 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Yu_Boosting_Continual_Learning_of_Vision-Language_Models_via_Mixture-of-Experts_Adapters_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Yu_Boosting_Continual_Learning_of_Vision-Language_Models_via_Mixture-of-Experts_Adapters_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Yu_Boosting_Continual_Learning_CVPR_2024_supplemental.pdf | null |
MaGGIe: Masked Guided Gradual Human Instance Matting | Chuong Huynh, Seoung Wug Oh, Abhinav Shrivastava, Joon-Young Lee | Human matting is a foundation task in image and video processing where human foreground pixels are extracted from the input. Prior works either improve the accuracy by additional guidance or improve the temporal consistency of a single instance across frames. We propose a new framework MaGGIe Masked Guided Gradual Human Instance Matting which predicts alpha mattes progressively for each human instances while maintaining the computational cost precision and consistency. Our method leverages modern architectures including transformer attention and sparse convolution to output all instance mattes simultaneously without exploding memory and latency. Although keeping constant inference costs in the multiple-instance scenario our framework achieves robust and versatile performance on our proposed synthesized benchmarks. With the higher quality image and video matting benchmarks the novel multi-instance synthesis approach from publicly available sources is introduced to increase the generalization of models in real-world scenarios. Our code and datasets are available at https://maggie-matt.github.io | https://openaccess.thecvf.com/content/CVPR2024/papers/Huynh_MaGGIe_Masked_Guided_Gradual_Human_Instance_Matting_CVPR_2024_paper.pdf | http://arxiv.org/abs/2404.16035 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Huynh_MaGGIe_Masked_Guided_Gradual_Human_Instance_Matting_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Huynh_MaGGIe_Masked_Guided_Gradual_Human_Instance_Matting_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Huynh_MaGGIe_Masked_Guided_CVPR_2024_supplemental.pdf | null |
FlowDiffuser: Advancing Optical Flow Estimation with Diffusion Models | Ao Luo, Xin Li, Fan Yang, Jiangyu Liu, Haoqiang Fan, Shuaicheng Liu | Optical flow estimation a process of predicting pixel-wise displacement between consecutive frames has commonly been approached as a regression task in the age of deep learning. Despite notable advancements this de facto paradigm unfortunately falls short in generalization performance when trained on synthetic or constrained data. Pioneering a paradigm shift we reformulate optical flow estimation as a conditional flow generation challenge unveiling FlowDiffuser --- a new family of optical flow models that could have stronger learning and generalization capabilities. FlowDiffuser estimates optical flow through a `noise-to-flow' strategy progressively eliminating noise from randomly generated flows conditioned on the provided pairs. To optimize accuracy and efficiency our FlowDiffuser incorporates a novel Conditional Recurrent Denoising Decoder (Conditional-RDD) streamlining the flow estimation process. It incorporates a unique Hidden State Denoising (HSD) paradigm effectively leveraging the information from previous time steps. Moreover FlowDiffuser can be easily integrated into existing flow networks leading to significant improvements in performance metrics compared to conventional implementations. Experiments on challenging benchmarks including Sintel and KITTI demonstrate the effectiveness of our FlowDiffuser with superior performance to existing state-of-the-art models. Code is available at https://github.com/LA30/FlowDiffuser. | https://openaccess.thecvf.com/content/CVPR2024/papers/Luo_FlowDiffuser_Advancing_Optical_Flow_Estimation_with_Diffusion_Models_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Luo_FlowDiffuser_Advancing_Optical_Flow_Estimation_with_Diffusion_Models_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Luo_FlowDiffuser_Advancing_Optical_Flow_Estimation_with_Diffusion_Models_CVPR_2024_paper.html | CVPR 2024 | null | null |
Benchmarking Implicit Neural Representation and Geometric Rendering in Real-Time RGB-D SLAM | Tongyan Hua, Lin Wang | Implicit neural representation (INR) in combination with geometric rendering has recently been employed in real-time dense RGB-D SLAM. Despite active research endeavors being made there lacks a unified protocol for fair evaluation impeding the evolution of this area. In this work we establish to our knowledge the first open-source benchmark framework to evaluate the performance of a wide spectrum of commonly used INRs and rendering functions for mapping and localization. The goal of our benchmark is to 1) gain an intuition of how different INRs and rendering functions impact mapping and localization and 2) establish a unified evaluation protocol w.r.t. the design choices that may impact the mapping and localization. With the framework we conduct a large suite of experiments offering various insights in choosing the INRs and geometric rendering functions: for example the dense feature grid outperforms other INRs (e.g. tri-plane and hash grid) even when geometric and color features are jointly encoded for memory efficiency. To extend the findings into the practical scenario a hybrid encoding strategy is proposed to bring the best of the accuracy and completion from the grid-based and decomposition-based INRs. We further propose explicit hybrid encoding for high-fidelity dense grid mapping to comply with the RGB-D SLAM system that puts the premise on robustness and computation efficiency. | https://openaccess.thecvf.com/content/CVPR2024/papers/Hua_Benchmarking_Implicit_Neural_Representation_and_Geometric_Rendering_in_Real-Time_RGB-D_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Hua_Benchmarking_Implicit_Neural_Representation_and_Geometric_Rendering_in_Real-Time_RGB-D_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Hua_Benchmarking_Implicit_Neural_Representation_and_Geometric_Rendering_in_Real-Time_RGB-D_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Hua_Benchmarking_Implicit_Neural_CVPR_2024_supplemental.pdf | null |
Free3D: Consistent Novel View Synthesis without 3D Representation | Chuanxia Zheng, Andrea Vedaldi | We introduce Free3D a simple accurate method for monocular open-set novel view synthesis (NVS). Similar to Zero-1-to-3 we start from a pre-trained 2D image generator for generalization and fine-tune it for NVS. Compared to other works that took a similar approach we obtain significant improvements without resorting to an explicit 3D representation which is slow and memory-consuming and without training an additional network for 3D reconstruction. Our key contribution is to improve the way the target camera pose is encoded in the network which we do by introducing a new ray conditioning normalization (RCN) layer. The latter injects pose information in the underlying 2D image generator by telling each pixel its viewing direction. We further improve multi-view consistency by using light-weight multi-view attention layers and by sharing generation noise between the different views. We train Free3D on the Objaverse dataset and demonstrate excellent generalization to new categories in new datasets including OmniObject3D and GSO. The project page is available at https://chuanxiaz.com/free3d/. | https://openaccess.thecvf.com/content/CVPR2024/papers/Zheng_Free3D_Consistent_Novel_View_Synthesis_without_3D_Representation_CVPR_2024_paper.pdf | http://arxiv.org/abs/2312.04551 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Zheng_Free3D_Consistent_Novel_View_Synthesis_without_3D_Representation_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Zheng_Free3D_Consistent_Novel_View_Synthesis_without_3D_Representation_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Zheng_Free3D_Consistent_Novel_CVPR_2024_supplemental.pdf | null |
SuperSVG: Superpixel-based Scalable Vector Graphics Synthesis | Teng Hu, Ran Yi, Baihong Qian, Jiangning Zhang, Paul L. Rosin, Yu-Kun Lai | SVG (Scalable Vector Graphics) is a widely used graphics format that possesses excellent scalability and editability. Image vectorization that aims to convert raster images to SVGs is an important yet challenging problem in computer vision and graphics. Existing image vectorization methods either suffer from low reconstruction accuracy for complex images or require long computation time. To address this issue we propose SuperSVG a superpixel-based vectorization model that achieves fast and high-precision image vectorization. Specifically we decompose the input image into superpixels to help the model focus on areas with similar colors and textures. Then we propose a two-stage self-training framework where a coarse-stage model is employed to reconstruct the main structure and a refinement-stage model is used for enriching the details. Moreover we propose a novel dynamic path warping loss to help the refinement-stage model to inherit knowledge from the coarse-stage model. Extensive qualitative and quantitative experiments demonstrate the superior performance of our method in terms of reconstruction accuracy and inference time compared to state-of-the-art approaches. The code is available in https://github.com/sjtuplayer/SuperSVG. | https://openaccess.thecvf.com/content/CVPR2024/papers/Hu_SuperSVG_Superpixel-based_Scalable_Vector_Graphics_Synthesis_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Hu_SuperSVG_Superpixel-based_Scalable_Vector_Graphics_Synthesis_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Hu_SuperSVG_Superpixel-based_Scalable_Vector_Graphics_Synthesis_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Hu_SuperSVG_Superpixel-based_Scalable_CVPR_2024_supplemental.pdf | null |
AV2AV: Direct Audio-Visual Speech to Audio-Visual Speech Translation with Unified Audio-Visual Speech Representation | Jeongsoo Choi, Se Jin Park, Minsu Kim, Yong Man Ro | This paper proposes a novel direct Audio-Visual Speech to Audio-Visual Speech Translation (AV2AV) framework where the input and output of the system are multimodal (i.e. audio and visual speech). With the proposed AV2AV two key advantages can be brought: 1) We can perform real-like conversations with individuals worldwide in a virtual meeting by utilizing our own primary languages. In contrast to Speech-to-Speech Translation (A2A) which solely translates between audio modalities the proposed AV2AV directly translates between audio-visual speech. This capability enhances the dialogue experience by presenting synchronized lip movements along with the translated speech. 2) We can improve the robustness of the spoken language translation system. By employing the complementary information of audio-visual speech the system can effectively translate spoken language even in the presence of acoustic noise showcasing robust performance. To mitigate the problem of the absence of a parallel AV2AV translation dataset we propose to train our spoken language translation system with the audio-only dataset of A2A. This is done by learning unified audio-visual speech representations through self-supervised learning in advance to train the translation system. Moreover we propose an AV-Renderer that can generate raw audio and video in parallel. It is designed with zero-shot speaker modeling thus the speaker in source audio-visual speech can be maintained at the target translated audio-visual speech. The effectiveness of AV2AV is evaluated with extensive experiments in a many-to-many language translation setting. Demo page is available on choijeongsoo.github.io/av2av. | https://openaccess.thecvf.com/content/CVPR2024/papers/Choi_AV2AV_Direct_Audio-Visual_Speech_to_Audio-Visual_Speech_Translation_with_Unified_CVPR_2024_paper.pdf | http://arxiv.org/abs/2312.02512 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Choi_AV2AV_Direct_Audio-Visual_Speech_to_Audio-Visual_Speech_Translation_with_Unified_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Choi_AV2AV_Direct_Audio-Visual_Speech_to_Audio-Visual_Speech_Translation_with_Unified_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Choi_AV2AV_Direct_Audio-Visual_CVPR_2024_supplemental.pdf | null |
Towards the Uncharted: Density-Descending Feature Perturbation for Semi-supervised Semantic Segmentation | Xiaoyang Wang, Huihui Bai, Limin Yu, Yao Zhao, Jimin Xiao | Semi-supervised semantic segmentation allows model to mine effective supervision from unlabeled data to complement label-guided training. Recent research has primarily focused on consistency regularization techniques exploring perturbation-invariant training at both the image and feature levels. In this work we proposed a novel feature-level consistency learning framework named Density-Descending Feature Perturbation (DDFP). Inspired by the low-density separation assumption in semi-supervised learning our key insight is that feature density can shed a light on the most promising direction for the segmentation classifier to explore which is the regions with lower density. We propose to shift features with confident predictions towards lower-density regions by perturbation injection. The perturbed features are then supervised by the predictions on the original features thereby compelling the classifier to explore less dense regions to effectively regularize the decision boundary. Central to our method is the estimation of feature density. To this end we introduce a lightweight density estimator based on normalizing flow allowing for efficient capture of the feature density distribution in an online manner. By extracting gradients from the density estimator we can determine the direction towards less dense regions for each feature. The proposed DDFP outperforms other designs on feature-level perturbations and shows state of the art performances on both Pascal VOC and Cityscapes dataset under various partition protocols. The project is available at https://github.com/Gavinwxy/DDFP. | https://openaccess.thecvf.com/content/CVPR2024/papers/Wang_Towards_the_Uncharted_Density-Descending_Feature_Perturbation_for_Semi-supervised_Semantic_Segmentation_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.06462 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Wang_Towards_the_Uncharted_Density-Descending_Feature_Perturbation_for_Semi-supervised_Semantic_Segmentation_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Wang_Towards_the_Uncharted_Density-Descending_Feature_Perturbation_for_Semi-supervised_Semantic_Segmentation_CVPR_2024_paper.html | CVPR 2024 | null | null |
WALT3D: Generating Realistic Training Data from Time-Lapse Imagery for Reconstructing Dynamic Objects Under Occlusion | Khiem Vuong, N Dinesh Reddy, Robert Tamburo, Srinivasa G. Narasimhan | Current methods for 2D and 3D object understanding struggle with severe occlusions in busy urban environments partly due to the lack of large-scale labeled ground-truth annotations for learning occlusion. In this work we introduce a novel framework for automatically generating a large realistic dataset of dynamic objects under occlusions using freely available time-lapse imagery. By leveraging off-the-shelf 2D (bounding box segmentation keypoint) and 3D (pose shape) predictions as pseudo-groundtruth unoccluded 3D objects are identified automatically and composited into the background in a clip-art style ensuring realistic appearances and physically accurate occlusion configurations. The resulting clip-art image with pseudo-groundtruth enables efficient training of object reconstruction methods that are robust to occlusions. Our method demonstrates significant improvements in both 2D and 3D reconstruction particularly in scenarios with heavily occluded objects like vehicles and people in urban scenes. | https://openaccess.thecvf.com/content/CVPR2024/papers/Vuong_WALT3D_Generating_Realistic_Training_Data_from_Time-Lapse_Imagery_for_Reconstructing_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.19022 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Vuong_WALT3D_Generating_Realistic_Training_Data_from_Time-Lapse_Imagery_for_Reconstructing_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Vuong_WALT3D_Generating_Realistic_Training_Data_from_Time-Lapse_Imagery_for_Reconstructing_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Vuong_WALT3D_Generating_Realistic_CVPR_2024_supplemental.pdf | null |
RTMO: Towards High-Performance One-Stage Real-Time Multi-Person Pose Estimation | Peng Lu, Tao Jiang, Yining Li, Xiangtai Li, Kai Chen, Wenming Yang | Real-time multi-person pose estimation presents significant challenges in balancing speed and precision. While two-stage top-down methods slow down as the number of people in the image increases existing one-stage methods often fail to simultaneously deliver high accuracy and real-time performance. This paper introduces RTMO a one-stage pose estimation framework that seamlessly integrates coordinate classification by representing keypoints using dual 1-D heatmaps within the YOLO architecture achieving accuracy comparable to top-down methods while maintaining high speed. We propose a dynamic coordinate classifier and a tailored loss function for heatmap learning specifically designed to address the incompatibilities between coordinate classification and dense prediction models. RTMO outperforms state-of-the-art one-stage pose estimators achieving 1.1% higher AP on COCO while operating about 9 times faster with the same backbone. Our largest model RTMO-l attains 74.8% AP on COCO val2017 and 141 FPS on a single V100 GPU demonstrating its efficiency and accuracy. The code and models are available at https://github.com/open-mmlab/mmpose/tree/main/projects/rtmo. | https://openaccess.thecvf.com/content/CVPR2024/papers/Lu_RTMO_Towards_High-Performance_One-Stage_Real-Time_Multi-Person_Pose_Estimation_CVPR_2024_paper.pdf | http://arxiv.org/abs/2312.07526 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Lu_RTMO_Towards_High-Performance_One-Stage_Real-Time_Multi-Person_Pose_Estimation_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Lu_RTMO_Towards_High-Performance_One-Stage_Real-Time_Multi-Person_Pose_Estimation_CVPR_2024_paper.html | CVPR 2024 | null | null |
Contrastive Mean-Shift Learning for Generalized Category Discovery | Sua Choi, Dahyun Kang, Minsu Cho | We address the problem of generalized category discovery (GCD) that aims to partition a partially labeled collection of images; only a small part of the collection is labeled and the total number of target classes is unknown. To address this generalized image clustering problem we revisit the mean-shift algorithm i.e. a classic powerful technique for mode seeking and incorporate it into a contrastive learning framework. The proposed method dubbed Contrastive Mean-Shift (CMS) learning trains an embedding network to produce representations with better clustering properties by an iterative process of mean shift and contrastive update. Experiments demonstrate that our method both in settings with and without the total number of clusters being known achieves state-of-the-art performance on six public GCD benchmarks without bells and whistles. | https://openaccess.thecvf.com/content/CVPR2024/papers/Choi_Contrastive_Mean-Shift_Learning_for_Generalized_Category_Discovery_CVPR_2024_paper.pdf | http://arxiv.org/abs/2404.09451 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Choi_Contrastive_Mean-Shift_Learning_for_Generalized_Category_Discovery_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Choi_Contrastive_Mean-Shift_Learning_for_Generalized_Category_Discovery_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Choi_Contrastive_Mean-Shift_Learning_CVPR_2024_supplemental.pdf | null |
Towards Language-Driven Video Inpainting via Multimodal Large Language Models | Jianzong Wu, Xiangtai Li, Chenyang Si, Shangchen Zhou, Jingkang Yang, Jiangning Zhang, Yining Li, Kai Chen, Yunhai Tong, Ziwei Liu, Chen Change Loy | We introduce a new task -- language-driven video inpainting which uses natural language instructions to guide the inpainting process. This approach overcomes the limitations of traditional video inpainting methods that depend on manually labeled binary masks a process often tedious and labor-intensive. We present the Remove Objects from Videos by Instructions (ROVI) dataset containing 5650 videos and 9091 inpainting results to support training and evaluation for this task. We also propose a novel diffusion-based language-driven video inpainting framework the first end-to-end baseline for this task integrating Multimodal Large Language Models to understand and execute complex language-based inpainting requests effectively. Our comprehensive results showcase the dataset's versatility and the model's effectiveness in various language-instructed inpainting scenarios. We have made datasets code and models publicly available at https://github.com/jianzongwu/Language-Driven-Video-Inpainting. | https://openaccess.thecvf.com/content/CVPR2024/papers/Wu_Towards_Language-Driven_Video_Inpainting_via_Multimodal_Large_Language_Models_CVPR_2024_paper.pdf | http://arxiv.org/abs/2401.10226 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Wu_Towards_Language-Driven_Video_Inpainting_via_Multimodal_Large_Language_Models_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Wu_Towards_Language-Driven_Video_Inpainting_via_Multimodal_Large_Language_Models_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Wu_Towards_Language-Driven_Video_CVPR_2024_supplemental.zip | null |
WaveFace: Authentic Face Restoration with Efficient Frequency Recovery | Yunqi Miao, Jiankang Deng, Jungong Han | Although diffusion models are rising as a powerful solution for blind face restoration they are criticized for two problems: 1) slow training and inference speed and 2) failure in preserving identity and recovering fine-grained facial details. In this work we propose WaveFace to solve the problems in the frequency domain where low- and high-frequency components decomposed by wavelet transformation are considered individually to maximize authenticity as well as efficiency. The diffusion model is applied to recover the low-frequency component only which presents general information of the original image but 1/16 in size. To preserve the original identity the generation is conditioned on the low-frequency component of low-quality images at each denoising step. Meanwhile high-frequency components at multiple decomposition levels are handled by a unified network which recovers complex facial details in a single step. Evaluations on four benchmark datasets show that: 1) WaveFace outperforms state-of-the-art methods in authenticity especially in terms of identity preservation and 2) authentic images are restored with the efficiency 10x faster than existing diffusion model-based BFR methods. | https://openaccess.thecvf.com/content/CVPR2024/papers/Miao_WaveFace_Authentic_Face_Restoration_with_Efficient_Frequency_Recovery_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.12760 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Miao_WaveFace_Authentic_Face_Restoration_with_Efficient_Frequency_Recovery_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Miao_WaveFace_Authentic_Face_Restoration_with_Efficient_Frequency_Recovery_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Miao_WaveFace_Authentic_Face_CVPR_2024_supplemental.pdf | null |
CLIP-KD: An Empirical Study of CLIP Model Distillation | Chuanguang Yang, Zhulin An, Libo Huang, Junyu Bi, Xinqiang Yu, Han Yang, Boyu Diao, Yongjun Xu | Contrastive Language-Image Pre-training (CLIP) has become a promising language-supervised visual pre-training framework. This paper aims to distill small CLIP models supervised by a large teacher CLIP model. We propose several distillation strategies including relation feature gradient and contrastive paradigms to examine the effectiveness of CLIP-Knowledge Distillation (KD). We show that a simple feature mimicry with Mean Squared Error loss works surprisingly well. Moreover interactive contrastive learning across teacher and student encoders is also effective in performance improvement. We explain that the success of CLIP-KD can be attributed to maximizing the feature similarity between teacher and student. The unified method is applied to distill several student models trained on CC3M+12M. CLIP-KD improves student CLIP models consistently over zero-shot ImageNet classification and cross-modal retrieval benchmarks. When using ViT-L/14 pretrained on Laion-400M as the teacher CLIP-KD achieves 57.5% and 55.4% zero-shot top-1 ImageNet accuracy over ViT-B/16 and ResNet-50 surpassing the original CLIP without KD by 20.5% and 20.1% margins respectively. Our code is released on https://github.com/winycg/CLIP-KD. | https://openaccess.thecvf.com/content/CVPR2024/papers/Yang_CLIP-KD_An_Empirical_Study_of_CLIP_Model_Distillation_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Yang_CLIP-KD_An_Empirical_Study_of_CLIP_Model_Distillation_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Yang_CLIP-KD_An_Empirical_Study_of_CLIP_Model_Distillation_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Yang_CLIP-KD_An_Empirical_CVPR_2024_supplemental.pdf | null |
UltrAvatar: A Realistic Animatable 3D Avatar Diffusion Model with Authenticity Guided Textures | Mingyuan Zhou, Rakib Hyder, Ziwei Xuan, Guojun Qi | Recent advances in 3D avatar generation have gained significant attention. These breakthroughs aim to produce more realistic animatable avatars narrowing the gap between virtual and real-world experiences. Most of existing works employ Score Distillation Sampling (SDS) loss combined with a differentiable renderer and text condition to guide a diffusion model in generating 3D avatars. However SDS often generates over-smoothed results with few facial details thereby lacking the diversity compared with ancestral sampling. On the other hand other works generate 3D avatar from a single image where the challenges of unwanted lighting effects perspective views and inferior image quality make them difficult to reliably reconstruct the 3D face meshes with the aligned complete textures. In this paper we propose a novel 3D avatar generation approach termed UltrAvatar with enhanced fidelity of geometry and superior quality of physically based rendering (PBR) textures without unwanted lighting. To this end the proposed approach presents a diffuse color extraction model and an authenticity guided texture diffusion model. The former removes the unwanted lighting effects to reveal true diffuse colors so that the generated avatars can be rendered under various lighting conditions. The latter follows two gradient-based guidances for generating PBR textures to render diverse face-identity features and details better aligning with 3D mesh geometry. We demonstrate the effectiveness and robustness of the proposed method outperforming the state-of-the-art methods by a large margin in the experiments. | https://openaccess.thecvf.com/content/CVPR2024/papers/Zhou_UltrAvatar_A_Realistic_Animatable_3D_Avatar_Diffusion_Model_with_Authenticity_CVPR_2024_paper.pdf | http://arxiv.org/abs/2401.11078 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Zhou_UltrAvatar_A_Realistic_Animatable_3D_Avatar_Diffusion_Model_with_Authenticity_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Zhou_UltrAvatar_A_Realistic_Animatable_3D_Avatar_Diffusion_Model_with_Authenticity_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Zhou_UltrAvatar_A_Realistic_CVPR_2024_supplemental.zip | null |
OneTracker: Unifying Visual Object Tracking with Foundation Models and Efficient Tuning | Lingyi Hong, Shilin Yan, Renrui Zhang, Wanyun Li, Xinyu Zhou, Pinxue Guo, Kaixun Jiang, Yiting Chen, Jinglun Li, Zhaoyu Chen, Wenqiang Zhang | Visual object tracking aims to localize the target object of each frame based on its initial appearance in the first frame. Depending on the input modility tracking tasks can be divided into RGB tracking and RGB+X (e.g. RGB+N and RGB+D) tracking. Despite the different input modalities the core aspect of tracking is the temporal matching. Based on this common ground we present a general framework to unify various tracking tasks termed as OneTracker. OneTracker first performs a large-scale pre-training on a RGB tracker called Foundation Tracker. This pretraining phase equips the Foundation Tracker with a stable ability to estimate the location of the target object. Then we regard other modality information as prompt and build Prompt Tracker upon Foundation Tracker. Through freezing the Foundation Tracker and only adjusting some additional trainable parameters Prompt Tracker inhibits the strong localization ability from Foundation Tracker and achieves parameter-efficient finetuning on downstream RGB+X tracking tasks. To evaluate the effectiveness of our general framework OneTracker which is consisted of Foundation Tracker and Prompt Tracker we conduct extensive experiments on 6 popular tracking tasks across 11 benchmarks and our OneTracker outperforms other models and achieves state-of-the-art performance. | https://openaccess.thecvf.com/content/CVPR2024/papers/Hong_OneTracker_Unifying_Visual_Object_Tracking_with_Foundation_Models_and_Efficient_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.09634 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Hong_OneTracker_Unifying_Visual_Object_Tracking_with_Foundation_Models_and_Efficient_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Hong_OneTracker_Unifying_Visual_Object_Tracking_with_Foundation_Models_and_Efficient_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Hong_OneTracker_Unifying_Visual_CVPR_2024_supplemental.pdf | null |
SC-Tune: Unleashing Self-Consistent Referential Comprehension in Large Vision Language Models | Tongtian Yue, Jie Cheng, Longteng Guo, Xingyuan Dai, Zijia Zhao, Xingjian He, Gang Xiong, Yisheng Lv, Jing Liu | Recent trends in Large Vision Language Models (LVLMs) research have been increasingly focusing on advancing beyond general image understanding towards more nuanced object-level referential comprehension. In this paper we present and delve into the self-consistency capability of LVLMs a crucial aspect that reflects the models' ability to both generate informative captions for specific objects and subsequently utilize these captions to accurately re-identify the objects in a closed-loop process. This capability significantly mirrors the precision and reliability of fine-grained visual-language understanding. Our findings reveal that the self-consistency level of existing LVLMs falls short of expectations posing limitations on their practical applicability and potential. To address this gap we introduce a novel fine-tuning paradigm named Self-Consistency Tuning (SC-Tune). It features the synergistic learning of a cyclic describer-locator system. This paradigm is not only data-efficient but also exhibits generalizability across multiple LVLMs. Through extensive experiments we demonstrate that SC-Tune significantly elevates performance across a spectrum of object-level vision-language benchmarks and maintains competitive or improved performance on image-level vision-language benchmarks. Both our model and code will be publicly available at https://github.com/ivattyue/SC-Tune. | https://openaccess.thecvf.com/content/CVPR2024/papers/Yue_SC-Tune_Unleashing_Self-Consistent_Referential_Comprehension_in_Large_Vision_Language_Models_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Yue_SC-Tune_Unleashing_Self-Consistent_Referential_Comprehension_in_Large_Vision_Language_Models_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Yue_SC-Tune_Unleashing_Self-Consistent_Referential_Comprehension_in_Large_Vision_Language_Models_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Yue_SC-Tune_Unleashing_Self-Consistent_CVPR_2024_supplemental.pdf | null |
Improving Depth Completion via Depth Feature Upsampling | Yufei Wang, Ge Zhang, Shaoqian Wang, Bo Li, Qi Liu, Le Hui, Yuchao Dai | The encoder-decoder network (ED-Net) is a commonly employed choice for existing depth completion methods but its working mechanism is ambiguous. In this paper we visualize the internal feature maps to analyze how the network densifies the input sparse depth. We find that the encoder feature of ED-Net focus on the areas with input depth points around. To obtain a dense feature and thus estimate complete depth the decoder feature tends to complement and enhance the encoder feature by skip-connection to make the fused encoder-decoder feature dense resulting in the decoder feature also exhibits sparse. However ED-Net obtains the sparse decoder feature from the dense fused feature at the previous stage where the "dense to sparse" process destroys the completeness of features and loses information. To address this issue we present a depth feature upsampling network (DFU) that explicitly utilizes these dense features to guide the upsampling of a low-resolution (LR) depth feature to a high-resolution (HR) one. The completeness of features is maintained throughout the upsampling process thus avoiding information loss. Furthermore we propose a confidence-aware guidance module (CGM) which is confidence-aware and performs guidance with adaptive receptive fields (GARF) to fully exploit the potential of these dense features as guidance. Experimental results show that our DFU a plug-and-play module can significantly improve the performance of existing ED-Net based methods with limited computational overheads and new SOTA results are achieved. Besides the generalization capability on sparser depth is also enhanced. Project page: https://npucvr.github.io/DFU. | https://openaccess.thecvf.com/content/CVPR2024/papers/Wang_Improving_Depth_Completion_via_Depth_Feature_Upsampling_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Wang_Improving_Depth_Completion_via_Depth_Feature_Upsampling_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Wang_Improving_Depth_Completion_via_Depth_Feature_Upsampling_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Wang_Improving_Depth_Completion_CVPR_2024_supplemental.pdf | null |
NeRSP: Neural 3D Reconstruction for Reflective Objects with Sparse Polarized Images | Yufei Han, Heng Guo, Koki Fukai, Hiroaki Santo, Boxin Shi, Fumio Okura, Zhanyu Ma, Yunpeng Jia | We present NeRSP a Neural 3D reconstruction technique for Reflective surfaces with Sparse Polarized images. Reflective surface reconstruction is extremely challenging as specular reflections are view-dependent and thus violate the multiview consistency for multiview stereo. On the other hand sparse image inputs as a practical capture setting commonly cause incomplete or distorted results due to the lack of correspondence matching. This paper jointly handles the challenges from sparse inputs and reflective surfaces by leveraging polarized images. We derive photometric and geometric cues from the polarimetric image formation model and multiview azimuth consistency which jointly optimize the surface geometry modeled via implicit neural representation. Based on the experiments on our synthetic and real datasets we achieve the state-of-the-art surface reconstruction results with only 6 views as input. | https://openaccess.thecvf.com/content/CVPR2024/papers/Han_NeRSP_Neural_3D_Reconstruction_for_Reflective_Objects_with_Sparse_Polarized_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Han_NeRSP_Neural_3D_Reconstruction_for_Reflective_Objects_with_Sparse_Polarized_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Han_NeRSP_Neural_3D_Reconstruction_for_Reflective_Objects_with_Sparse_Polarized_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Han_NeRSP_Neural_3D_CVPR_2024_supplemental.pdf | null |
Retrieval-Augmented Embodied Agents | Yichen Zhu, Zhicai Ou, Xiaofeng Mou, Jian Tang | Embodied agents operating in complex and uncertain environments face considerable challenges. While some advanced agents handle complex manipulation tasks with proficiency their success often hinges on extensive training data to develop their capabilities. In contrast humans typically rely on recalling past experiences and analogous situations to solve new problems. Aiming to emulate this human approach in robotics we introduce the Retrieval-Augmented Embodied Agent (RAEA). This innovative system equips robots with a form of shared memory significantly enhancing their performance. Our approach integrates a policy retriever allowing robots to access relevant strategies from an external policy memory bank based on multi-modal inputs. Additionally a policy generator is employed to assimilate these strategies into the learning process enabling robots to formulate effective responses to tasks. Extensive testing of RAEA in both simulated and real-world scenarios demonstrates its superior performance over traditional methods representing a major leap forward in robotic technology. | https://openaccess.thecvf.com/content/CVPR2024/papers/Zhu_Retrieval-Augmented_Embodied_Agents_CVPR_2024_paper.pdf | http://arxiv.org/abs/2404.11699 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Zhu_Retrieval-Augmented_Embodied_Agents_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Zhu_Retrieval-Augmented_Embodied_Agents_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Zhu_Retrieval-Augmented_Embodied_Agents_CVPR_2024_supplemental.pdf | null |
SAFDNet: A Simple and Effective Network for Fully Sparse 3D Object Detection | Gang Zhang, Junnan Chen, Guohuan Gao, Jianmin Li, Si Liu, Xiaolin Hu | LiDAR-based 3D object detection plays an essential role in autonomous driving. Existing high-performing 3D object detectors usually build dense feature maps in the backbone network and prediction head. However the computational costs introduced by the dense feature maps grow quadratically as the perception range increases making these models hard to scale up to long-range detection. Some recent works have attempted to construct fully sparse detectors to solve this issue; nevertheless the resulting models either rely on a complex multi-stage pipeline or exhibit inferior performance. In this work we propose a fully sparse adaptive feature diffusion network (SAFDNet) for LiDAR-based 3D object detection. In SAFDNet an adaptive feature diffusion strategy is designed to address the center feature missing problem. We conducted extensive experiments on Waymo Open nuScenes and Argoverse2 datasets. SAFDNet performed slightly better than the previous SOTA on the first two datasets but much better on the last dataset which features long-range detection verifying the efficacy of SAFDNet in scenarios where long-range detection is required. Notably on Argoverse2 SAFDNet surpassed the previous best hybrid detector HEDNet by 2.6% mAP while being 2.1x faster and yielded 2.1% mAP gains over the previous best sparse detector FSDv2 while being 1.3x faster. The code will be available at https://github.com/zhanggang001/HEDNet. | https://openaccess.thecvf.com/content/CVPR2024/papers/Zhang_SAFDNet_A_Simple_and_Effective_Network_for_Fully_Sparse_3D_CVPR_2024_paper.pdf | https://arxiv.org/abs/2403.05817 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Zhang_SAFDNet_A_Simple_and_Effective_Network_for_Fully_Sparse_3D_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Zhang_SAFDNet_A_Simple_and_Effective_Network_for_Fully_Sparse_3D_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Zhang_SAFDNet_A_Simple_CVPR_2024_supplemental.pdf | https://openaccess.thecvf.com |
Attention-Propagation Network for Egocentric Heatmap to 3D Pose Lifting | Taeho Kang, Youngki Lee | We present EgoTAP a heatmap-to-3D pose lifting method for highly accurate stereo egocentric 3D pose estimation. Severe self-occlusion and out-of-view limbs in egocentric camera views make accurate pose estimation a challenging problem. To address the challenge prior methods employ joint heatmaps-probabilistic 2D representations of the body pose but heatmap-to-3D pose conversion still remains an inaccurate process. We propose a novel heatmap-to-3D lifting method composed of the Grid ViT Encoder and the Propagation Network. The Grid ViT Encoder summarizes joint heatmaps into effective feature embedding using self-attention. Then the Propagation Network estimates the 3D pose by utilizing skeletal information to better estimate the position of obscure joints. Our method significantly outperforms the previous state-of-the-art qualitatively and quantitatively demonstrated by a 23.9% reduction of error in an MPJPE metric. Our source code is available on GitHub. | https://openaccess.thecvf.com/content/CVPR2024/papers/Kang_Attention-Propagation_Network_for_Egocentric_Heatmap_to_3D_Pose_Lifting_CVPR_2024_paper.pdf | http://arxiv.org/abs/2402.18330 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Kang_Attention-Propagation_Network_for_Egocentric_Heatmap_to_3D_Pose_Lifting_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Kang_Attention-Propagation_Network_for_Egocentric_Heatmap_to_3D_Pose_Lifting_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Kang_Attention-Propagation_Network_for_CVPR_2024_supplemental.zip | null |
OmniMotionGPT: Animal Motion Generation with Limited Data | Zhangsihao Yang, Mingyuan Zhou, Mengyi Shan, Bingbing Wen, Ziwei Xuan, Mitch Hill, Junjie Bai, Guo-Jun Qi, Yalin Wang | Our paper aims to generate diverse and realistic animal motion sequences from textual descriptions without a large-scale animal text-motion dataset. While the task of text-driven human motion synthesis is already extensively studied and benchmarked it remains challenging to transfer this success to other skeleton structures with limited data. In this work we design a model architecture that imitates Generative Pretraining Transformer (GPT) utilizing prior knowledge learned from human data to the animal domain. We jointly train motion autoencoders for both animal and human motions and at the same time optimize through the similarity scores among human motion encoding animal motion encoding and text CLIP embedding. Presenting the first solution to this problem we are able to generate animal motions with high diversity and fidelity quantitatively and qualitatively outperforming the results of training human motion generation baselines on animal data. Additionally we introduce AnimalML3D the first text-animal motion dataset with 1240 animation sequences spanning 36 different animal identities. We hope this dataset would mediate the data scarcity problem in text-driven animal motion generation providing a new playground for the research community. | https://openaccess.thecvf.com/content/CVPR2024/papers/Yang_OmniMotionGPT_Animal_Motion_Generation_with_Limited_Data_CVPR_2024_paper.pdf | http://arxiv.org/abs/2311.18303 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Yang_OmniMotionGPT_Animal_Motion_Generation_with_Limited_Data_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Yang_OmniMotionGPT_Animal_Motion_Generation_with_Limited_Data_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Yang_OmniMotionGPT_Animal_Motion_CVPR_2024_supplemental.zip | null |
SNI-SLAM: Semantic Neural Implicit SLAM | Siting Zhu, Guangming Wang, Hermann Blum, Jiuming Liu, Liang Song, Marc Pollefeys, Hesheng Wang | We propose SNI-SLAM a semantic SLAM system utilizing neural implicit representation that simultaneously performs accurate semantic mapping high-quality surface reconstruction and robust camera tracking. In this system we introduce hierarchical semantic representation to allow multi-level semantic comprehension for top-down structured semantic mapping of the scene. In addition to fully utilize the correlation between multiple attributes of the environment we integrate appearance geometry and semantic features through cross-attention for feature collaboration. This strategy enables a more multifaceted understanding of the environment thereby allowing SNI-SLAM to remain robust even when single attribute is defective. Then we design an internal fusion-based decoder to obtain semantic RGB Truncated Signed Distance Field (TSDF) values from multi-level features for accurate decoding. Furthermore we propose a feature loss to update the scene representation at the feature level. Compared with low-level losses such as RGB loss and depth loss our feature loss is capable of guiding the network optimization on a higher-level. Our SNI-SLAM method demonstrates superior performance over all recent NeRF-based SLAM methods in terms of mapping and tracking accuracy on Replica and ScanNet datasets while also showing excellent capabilities in accurate semantic segmentation and real-time semantic mapping. Codes will be available at https://github.com/IRMVLab/SNI-SLAM. | https://openaccess.thecvf.com/content/CVPR2024/papers/Zhu_SNI-SLAM_Semantic_Neural_Implicit_SLAM_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Zhu_SNI-SLAM_Semantic_Neural_Implicit_SLAM_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Zhu_SNI-SLAM_Semantic_Neural_Implicit_SLAM_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Zhu_SNI-SLAM_Semantic_Neural_CVPR_2024_supplemental.zip | null |
InstanceDiffusion: Instance-level Control for Image Generation | Xudong Wang, Trevor Darrell, Sai Saketh Rambhatla, Rohit Girdhar, Ishan Misra | Text-to-image diffusion models produce high quality images but do not offer control over individual instances in the image. We introduce InstanceDiffusion that adds precise instance-level control to text-to-image diffusion models. InstanceDiffusion supports free-form language conditions per instance and allows flexible ways to specify instance locations such as simple single points scribbles bounding boxes or intricate instance segmentation masks and combinations thereof. We propose three major changes to text-to-image models that enable precise instance-level control. Our UniFusion block enables instance-level conditions for text-to-image models the ScaleU block improves image fidelity and our Multi-instance Sampler improves generations for multiple instances. InstanceDiffusion significantly surpasses specialized state-of-the-art models for each location condition. Notably on the COCO dataset we outperform previous state-of-the-art by 20.4% AP50box for box inputs and 25.4% IoU for mask inputs. | https://openaccess.thecvf.com/content/CVPR2024/papers/Wang_InstanceDiffusion_Instance-level_Control_for_Image_Generation_CVPR_2024_paper.pdf | http://arxiv.org/abs/2402.03290 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Wang_InstanceDiffusion_Instance-level_Control_for_Image_Generation_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Wang_InstanceDiffusion_Instance-level_Control_for_Image_Generation_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Wang_InstanceDiffusion_Instance-level_Control_CVPR_2024_supplemental.pdf | null |
Unifying Top-down and Bottom-up Scanpath Prediction Using Transformers | Zhibo Yang, Sounak Mondal, Seoyoung Ahn, Ruoyu Xue, Gregory Zelinsky, Minh Hoai, Dimitris Samaras | Most models of visual attention aim at predicting either top-down or bottom-up control as studied using different visual search and free-viewing tasks. In this paper we propose the Human Attention Transformer (HAT) a single model that predicts both forms of attention control. HAT uses a novel transformer-based architecture and a simplified foveated retina that collectively create a spatio-temporal awareness akin to the dynamic visual working memory of humans. HAT not only establishes a new state-of-the-art in predicting the scanpath of fixations made during target-present and target-absent visual search and "taskless" free viewing but also makes human gaze behavior interpretable. Unlike previous methods that rely on a coarse grid of fixation cells and experience information loss due to fixation discretization HAT features a sequential dense prediction architecture and outputs a dense heatmap for each fixation thus avoiding discretizing fixations. HAT sets a new standard in computational attention which emphasizes effectiveness generality and interpretability. HAT's demonstrated scope and applicability will likely inspire the development of new attention models that can better predict human behavior in various attention-demanding scenarios. Code is available at https://github.com/cvlab-stonybrook/HAT. | https://openaccess.thecvf.com/content/CVPR2024/papers/Yang_Unifying_Top-down_and_Bottom-up_Scanpath_Prediction_Using_Transformers_CVPR_2024_paper.pdf | http://arxiv.org/abs/2303.09383 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Yang_Unifying_Top-down_and_Bottom-up_Scanpath_Prediction_Using_Transformers_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Yang_Unifying_Top-down_and_Bottom-up_Scanpath_Prediction_Using_Transformers_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Yang_Unifying_Top-down_and_CVPR_2024_supplemental.pdf | null |
HINTED: Hard Instance Enhanced Detector with Mixed-Density Feature Fusion for Sparsely-Supervised 3D Object Detection | Qiming Xia, Wei Ye, Hai Wu, Shijia Zhao, Leyuan Xing, Xun Huang, Jinhao Deng, Xin Li, Chenglu Wen, Cheng Wang | Current sparsely-supervised object detection methods largely depend on high threshold settings to derive high-quality pseudo labels from detector predictions. However hard instances within point clouds frequently display incomplete structures causing decreased confidence scores in their assigned pseudo-labels. Previous methods inevitably result in inadequate positive supervision for these instances. To address this problem we propose a novel Hard INsTance Enhanced Detector HINTED for sparsely-supervised 3D object detection. Firstly we design a self-boosting teacher SBT model to generate more potential pseudo-labels enhancing the effectiveness of information transfer. Then we introduce a mixed-density student MDS model to concentrate on hard instances during the training phase thereby improving detection accuracy. Our extensive experiments on the KITTI dataset validate our method's superior performance. Compared with leading sparsely-supervised methods HINTED significantly improves the detection performance on hard instances notably outperforming fully-supervised methods in detecting challenging categories like cyclists. HINTED also significantly outperforms the state-of-the-art semi-supervised method on challenging categories. The code is available at https://github.com/xmuqimingxia/HINTED. | https://openaccess.thecvf.com/content/CVPR2024/papers/Xia_HINTED_Hard_Instance_Enhanced_Detector_with_Mixed-Density_Feature_Fusion_for_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Xia_HINTED_Hard_Instance_Enhanced_Detector_with_Mixed-Density_Feature_Fusion_for_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Xia_HINTED_Hard_Instance_Enhanced_Detector_with_Mixed-Density_Feature_Fusion_for_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Xia_HINTED_Hard_Instance_CVPR_2024_supplemental.pdf | null |
Structured Gradient-based Interpretations via Norm-Regularized Adversarial Training | Shizhan Gong, Qi Dou, Farzan Farnia | Gradient-based saliency maps have been widely used to explain the decisions of deep neural network classifiers. However standard gradient-based interpretation maps including the simple gradient and integrated gradient algorithms often lack desired structures such as sparsity and connectedness in their application to real-world computer vision models. A common approach to induce sparsity-based structures into gradient-based saliency maps is to modify the simple gradient scheme using sparsification or norm-based regularization. However one drawback with such post-processing approaches is the potentially significant loss in fidelity to the original simple gradient map. In this work we propose to apply adversarial training as an in-processing scheme to train neural networks with structured simple gradient maps. We demonstrate an existing duality between the regularized norms of the adversarial perturbations and gradient-based maps whereby we design adversarial training schemes promoting sparsity and group-sparsity properties in simple gradient maps. We present comprehensive numerical results to show the influence of our proposed norm-based adversarial training methods on the standard gradient-based maps of standard neural network architectures on benchmark image datasets. | https://openaccess.thecvf.com/content/CVPR2024/papers/Gong_Structured_Gradient-based_Interpretations_via_Norm-Regularized_Adversarial_Training_CVPR_2024_paper.pdf | http://arxiv.org/abs/2404.04647 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Gong_Structured_Gradient-based_Interpretations_via_Norm-Regularized_Adversarial_Training_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Gong_Structured_Gradient-based_Interpretations_via_Norm-Regularized_Adversarial_Training_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Gong_Structured_Gradient-based_Interpretations_CVPR_2024_supplemental.pdf | null |
Building a Strong Pre-Training Baseline for Universal 3D Large-Scale Perception | Haoming Chen, Zhizhong Zhang, Yanyun Qu, Ruixin Zhang, Xin Tan, Yuan Xie | An effective pre-training framework with universal 3D representations is extremely desired in perceiving large-scale dynamic scenes. However establishing such an ideal framework that is both task-generic and label-efficient poses a challenge in unifying the representation of the same primitive across diverse scenes. The current contrastive 3D pre-training methods typically follow a frame-level consistency which focuses on the 2D-3D relationships in each detached image. Such inconsiderate consistency greatly hampers the promising path of reaching an universal pre-training framework: (1) The cross-scene semantic self-conflict \textit i.e. the intense collision between primitive segments of the same semantics from different scenes; (2) Lacking a globally unified bond that pushes the cross-scene semantic consistency into 3D representation learning. To address above challenges we propose a CSC framework that puts a scene-level semantic consistency in the heart bridging the connection of the similar semantic segments across various scenes. To achieve this goal we combine the coherent semantic cues provided by the vision foundation model and the knowledge-rich cross-scene prototypes derived from the complementary multi-modality information. These allow us to train a universal 3D pre-training model that facilitates various downstream tasks with less fine-tuning efforts. Empirically we achieve consistent improvements over SOTA pre-training approaches in semantic segmentation (+1.4% mIoU) object detection (+1.0% mAP) and panoptic segmentation (+3.0% PQ) using their task-specific 3D network on nuScenes. Code is released at \href https://github.com/chenhaomingbob/CSC https://github.com/chenhaomingbob/CSC hoping to inspire future research. | https://openaccess.thecvf.com/content/CVPR2024/papers/Chen_Building_a_Strong_Pre-Training_Baseline_for_Universal_3D_Large-Scale_Perception_CVPR_2024_paper.pdf | http://arxiv.org/abs/2405.07201 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Chen_Building_a_Strong_Pre-Training_Baseline_for_Universal_3D_Large-Scale_Perception_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Chen_Building_a_Strong_Pre-Training_Baseline_for_Universal_3D_Large-Scale_Perception_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Chen_Building_a_Strong_CVPR_2024_supplemental.pdf | null |
DS-NeRV: Implicit Neural Video Representation with Decomposed Static and Dynamic Codes | Hao Yan, Zhihui Ke, Xiaobo Zhou, Tie Qiu, Xidong Shi, Dadong Jiang | Implicit neural representations for video (NeRV) have recently become a novel way for high-quality video representation. However existing works employ a single network to represent the entire video which implicitly confuse static and dynamic information. This leads to an inability to effectively compress the redundant static information and lack the explicitly modeling of global temporal-coherent dynamic details. To solve above problems we propose DS-NeRV which decomposes videos into sparse learnable static codes and dynamic codes without the need for explicit optical flow or residual supervision. By setting different sampling rates for two codes and applying weighted sum and interpolation sampling methods DS-NeRV efficiently utilizes redundant static information while maintaining high-frequency details. Additionally we design a cross-channel attention-based (CCA) fusion module to efficiently fuse these two codes for frame decoding. Our approach achieves a high quality reconstruction of 31.2 PSNR with only 0.35M parameters thanks to separate static and dynamic codes representation and outperforms existing NeRV methods in many downstream tasks. Our project website is at https://haoyan14.github.io/DS-NeRV. | https://openaccess.thecvf.com/content/CVPR2024/papers/Yan_DS-NeRV_Implicit_Neural_Video_Representation_with_Decomposed_Static_and_Dynamic_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Yan_DS-NeRV_Implicit_Neural_Video_Representation_with_Decomposed_Static_and_Dynamic_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Yan_DS-NeRV_Implicit_Neural_Video_Representation_with_Decomposed_Static_and_Dynamic_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Yan_DS-NeRV_Implicit_Neural_CVPR_2024_supplemental.pdf | null |
3D-Aware Face Editing via Warping-Guided Latent Direction Learning | Yuhao Cheng, Zhuo Chen, Xingyu Ren, Wenhan Zhu, Zhengqin Xu, Di Xu, Changpeng Yang, Yichao Yan | 3D facial editing a longstanding task in computer vision with broad applications is expected to fast and intuitively manipulate any face from arbitrary viewpoints following the user's will. Existing works have limitations in terms of intuitiveness generalization and efficiency. To overcome these challenges we propose FaceEdit3D which allows users to directly manipulate 3D points to edit a 3D face achieving natural and rapid face editing. After one or several points are manipulated by users we propose the tri-plane warping to directly manipulate the view-independent 3D representation. To address the problem of distortion caused by tri-plane warping we train a warp-aware encoder to project the warped face onto a standardized latent space. In this space we further propose directional latent editing to mitigate the identity bias caused by the encoder and realize the disentangled editing of various attributes. Extensive experiments show that our method achieves superior results with rich facial details and nice identity preservation. Our approach also supports general applications like multi-attribute continuous editing and cat/car editing. The project website is https://cyh-sj.github.io/FaceEdit3D/. | https://openaccess.thecvf.com/content/CVPR2024/papers/Cheng_3D-Aware_Face_Editing_via_Warping-Guided_Latent_Direction_Learning_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Cheng_3D-Aware_Face_Editing_via_Warping-Guided_Latent_Direction_Learning_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Cheng_3D-Aware_Face_Editing_via_Warping-Guided_Latent_Direction_Learning_CVPR_2024_paper.html | CVPR 2024 | null | null |
3DFIRES: Few Image 3D REconstruction for Scenes with Hidden Surfaces | Linyi Jin, Nilesh Kulkarni, David F. Fouhey | This paper introduces 3DFIRES a novel system for scene-level 3D reconstruction from posed images. Designed to work with as few as one view 3DFIRES reconstructs the complete geometry of unseen scenes including hidden surfaces. With multiple view inputs our method produces full reconstruction within all camera frustums. A key feature of our approach is the fusion of multi-view information at the feature level enabling the production of coherent and comprehensive 3D reconstruction. We train our system on non-watertight scans from large-scale real scene dataset. We show it matches the efficacy of single-view reconstruction methods with only one input and surpasses existing techniques in both quantitative and qualitative measures for sparse-view 3D reconstruction. | https://openaccess.thecvf.com/content/CVPR2024/papers/Jin_3DFIRES_Few_Image_3D_REconstruction_for_Scenes_with_Hidden_Surfaces_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.08768 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Jin_3DFIRES_Few_Image_3D_REconstruction_for_Scenes_with_Hidden_Surfaces_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Jin_3DFIRES_Few_Image_3D_REconstruction_for_Scenes_with_Hidden_Surfaces_CVPR_2024_paper.html | CVPR 2024 | null | null |
CAT-Seg: Cost Aggregation for Open-Vocabulary Semantic Segmentation | Seokju Cho, Heeseong Shin, Sunghwan Hong, Anurag Arnab, Paul Hongsuck Seo, Seungryong Kim | Open-vocabulary semantic segmentation presents the challenge of labeling each pixel within an image based on a wide range of text descriptions. In this work we introduce a novel cost-based approach to adapt vision-language foundation models notably CLIP for the intricate task of semantic segmentation. Through aggregating the cosine similarity score i.e. the cost volume between image and text embeddings our method potently adapts CLIP for segmenting seen and unseen classes by fine-tuning its encoders addressing the challenges faced by existing methods in handling unseen classes. Building upon this we explore methods to effectively aggregate the cost volume considering its multi-modal nature of being established between image and text embeddings. Furthermore we examine various methods for efficiently fine-tuning CLIP. | https://openaccess.thecvf.com/content/CVPR2024/papers/Cho_CAT-Seg_Cost_Aggregation_for_Open-Vocabulary_Semantic_Segmentation_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Cho_CAT-Seg_Cost_Aggregation_for_Open-Vocabulary_Semantic_Segmentation_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Cho_CAT-Seg_Cost_Aggregation_for_Open-Vocabulary_Semantic_Segmentation_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Cho_CAT-Seg_Cost_Aggregation_CVPR_2024_supplemental.pdf | null |
Focus on Your Instruction: Fine-grained and Multi-instruction Image Editing by Attention Modulation | Qin Guo, Tianwei Lin | Recently diffusion-based methods like InstructPix2Pix (IP2P) have achieved effective instruction-based image editing requiring only natural language instructions from the user. However these methods often inadvertently alter unintended areas and struggle with multi-instruction editing resulting in compromised outcomes. To address these issues we introduce the Focus on Your Instruction (FoI) a method designed to ensure precise and harmonious editing across multiple instructions without extra training or test-time optimization. In the FoI we primarily emphasize two aspects: (1) precisely extracting regions of interest for each instruction and (2) guiding the denoising process to concentrate within these regions of interest. For the first objective we identify the implicit grounding capability of IP2P from the cross-attention between instruction and image then develop an effective mask extraction method. | https://openaccess.thecvf.com/content/CVPR2024/papers/Guo_Focus_on_Your_Instruction_Fine-grained_and_Multi-instruction_Image_Editing_by_CVPR_2024_paper.pdf | http://arxiv.org/abs/2312.10113 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Guo_Focus_on_Your_Instruction_Fine-grained_and_Multi-instruction_Image_Editing_by_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Guo_Focus_on_Your_Instruction_Fine-grained_and_Multi-instruction_Image_Editing_by_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Guo_Focus_on_Your_CVPR_2024_supplemental.pdf | null |
SDSTrack: Self-Distillation Symmetric Adapter Learning for Multi-Modal Visual Object Tracking | Xiaojun Hou, Jiazheng Xing, Yijie Qian, Yaowei Guo, Shuo Xin, Junhao Chen, Kai Tang, Mengmeng Wang, Zhengkai Jiang, Liang Liu, Yong Liu | Multimodal Visual Object Tracking (VOT) has recently gained significant attention due to its robustness. Early research focused on fully fine-tuning RGB-based trackers which was inefficient and lacked generalized representation due to the scarcity of multimodal data. Therefore recent studies have utilized prompt tuning to transfer pre-trained RGB-based trackers to multimodal data. However the modality gap limits pre-trained knowledge recall and the dominance of the RGB modality persists preventing the full utilization of information from other modalities. To address these issues we propose a novel symmetric multimodal tracking framework called SDSTrack. We introduce lightweight adaptation for efficient fine-tuning which directly transfers the feature extraction ability from RGB to other domains with a small number of trainable parameters and integrates multimodal features in a balanced symmetric manner. Furthermore we design a complementary masked patch distillation strategy to enhance the robustness of trackers in complex environments such as extreme weather poor imaging and sensor failure. Extensive experiments demonstrate that SDSTrack outperforms state-of-the-art methods in various multimodal tracking scenarios including RGB+Depth RGB+Thermal and RGB+Event tracking and exhibits impressive results in extreme conditions. Our source code is available at : https://github.com/hoqolo/SDSTrack. | https://openaccess.thecvf.com/content/CVPR2024/papers/Hou_SDSTrack_Self-Distillation_Symmetric_Adapter_Learning_for_Multi-Modal_Visual_Object_Tracking_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.16002 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Hou_SDSTrack_Self-Distillation_Symmetric_Adapter_Learning_for_Multi-Modal_Visual_Object_Tracking_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Hou_SDSTrack_Self-Distillation_Symmetric_Adapter_Learning_for_Multi-Modal_Visual_Object_Tracking_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Hou_SDSTrack_Self-Distillation_Symmetric_CVPR_2024_supplemental.pdf | null |
MCPNet: An Interpretable Classifier via Multi-Level Concept Prototypes | Bor-Shiun Wang, Chien-Yi Wang, Wei-Chen Chiu | Recent advancements in post-hoc and inherently interpretable methods have markedly enhanced the explanations of black box classifier models. These methods operate either through post-analysis or by integrating concept learning during model training. Although being effective in bridging the semantic gap between a model's latent space and human interpretation these explanation methods only partially reveal the model's decision-making process. The outcome is typically limited to high-level semantics derived from the last feature map. We argue that the explanations lacking insights into the decision processes at low and mid-level features are neither fully faithful nor useful. Addressing this gap we introduce the Multi-Level Concept Prototypes Classifier (MCPNet) an inherently interpretable model. MCPNet autonomously learns meaningful concept prototypes across multiple feature map levels using Centered Kernel Alignment (CKA) loss and an energy-based weighted PCA mechanism and it does so without reliance on predefined concept labels. Further we propose a novel classifier paradigm that learns and aligns multi-level concept prototype distributions for classification purposes via Class-aware Concept Distribution (CCD) loss. Our experiments reveal that our proposed MCPNet while being adaptable to various model architectures offers comprehensive multi-level explanations while maintaining classification accuracy. Additionally its concept distribution-based classification approach shows improved generalization capabilities in few-shot classification scenarios. | https://openaccess.thecvf.com/content/CVPR2024/papers/Wang_MCPNet_An_Interpretable_Classifier_via_Multi-Level_Concept_Prototypes_CVPR_2024_paper.pdf | http://arxiv.org/abs/2404.08968 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Wang_MCPNet_An_Interpretable_Classifier_via_Multi-Level_Concept_Prototypes_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Wang_MCPNet_An_Interpretable_Classifier_via_Multi-Level_Concept_Prototypes_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Wang_MCPNet_An_Interpretable_CVPR_2024_supplemental.pdf | null |
Semantic Shield: Defending Vision-Language Models Against Backdooring and Poisoning via Fine-grained Knowledge Alignment | Alvi Md Ishmam, Christopher Thomas | In recent years there has been enormous interest in vision-language models trained using self-supervised objectives. However the use of large-scale datasets scraped from the web for training also makes these models vulnerable to potential security threats such as backdooring and poisoning attacks. In this paper we propose a method for mitigating such attacks on contrastively trained vision-language models. Our approach leverages external knowledge extracted from a language model to prevent models from learning correlations between image regions which lack strong alignment with external knowledge. We do this by imposing constraints to enforce that attention paid by the model to visual regions is proportional to the alignment of those regions with external knowledge. We conduct extensive experiments using a variety of recent backdooring and poisoning attacks on multiple datasets and architectures. Our results clearly demonstrate that our proposed approach is highly effective at defending against such attacks across multiple settings while maintaining model utility and without requiring any changes at inference time. | https://openaccess.thecvf.com/content/CVPR2024/papers/Ishmam_Semantic_Shield_Defending_Vision-Language_Models_Against_Backdooring_and_Poisoning_via_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Ishmam_Semantic_Shield_Defending_Vision-Language_Models_Against_Backdooring_and_Poisoning_via_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Ishmam_Semantic_Shield_Defending_Vision-Language_Models_Against_Backdooring_and_Poisoning_via_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Ishmam_Semantic_Shield_Defending_CVPR_2024_supplemental.pdf | null |
AvatarGPT: All-in-One Framework for Motion Understanding Planning Generation and Beyond | Zixiang Zhou, Yu Wan, Baoyuan Wang | Large Language Models(LLMs) have shown remarkable emergent abilities in unifying almost all (if not every) NLP tasks. In the human motion-related realm however researchers still develop siloed models for each task. Inspired by InstuctGPT[??] and the generalist concept behind Gato [??] we introduce AvatarGPT an All-in-One framework for motion understanding planning generations as well as other tasks such as motion in-between synthesis. AvatarGPT treats each task as one type of instruction fine-tuned on the shared LLM. All the tasks are seamlessly interconnected with language as the universal interface constituting a closed-loop within the framework. To achieve this human motion sequences are first encoded as discrete tokens which serve as the extended vocabulary of LLM. Then an unsupervised pipeline to generate natural language descriptions of human action sequences from in-the-wild videos is developed. Finally all tasks are jointly trained. Extensive experiments show that AvatarGPT achieves SOTA on low-level tasks and promising results on high-level tasks demonstrating the effectiveness of our proposed All-in-One framework. Moreover for the first time AvatarGPT enables a principled approach by iterative traversal of the tasks within the closed-loop for unlimited long-motion synthesis. | https://openaccess.thecvf.com/content/CVPR2024/papers/Zhou_AvatarGPT_All-in-One_Framework_for_Motion_Understanding_Planning_Generation_and_Beyond_CVPR_2024_paper.pdf | http://arxiv.org/abs/2311.16468 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Zhou_AvatarGPT_All-in-One_Framework_for_Motion_Understanding_Planning_Generation_and_Beyond_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Zhou_AvatarGPT_All-in-One_Framework_for_Motion_Understanding_Planning_Generation_and_Beyond_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Zhou_AvatarGPT_All-in-One_Framework_CVPR_2024_supplemental.pdf | null |
Rethinking the Up-Sampling Operations in CNN-based Generative Network for Generalizable Deepfake Detection | Chuangchuang Tan, Yao Zhao, Shikui Wei, Guanghua Gu, Ping Liu, Yunchao Wei | Recently the proliferation of highly realistic synthetic images facilitated through a variety of GANs and Diffusions has significantly heightened the susceptibility to misuse. While the primary focus of deepfake detection has traditionally centered on the design of detection algorithms an investigative inquiry into the generator architectures has remained conspicuously absent in recent years. This paper contributes to this lacuna by rethinking the architectures of CNN-based generator thereby establishing a generalized representation of synthetic artifacts. Our findings illuminate that the up-sampling operator can beyond frequency-based artifacts produce generalized forgery artifacts. In particular the local interdependence among image pixels caused by upsampling operators is significantly demonstrated in synthetic images generated by GAN or diffusion. Building upon this observation we introduce the concept of Neighboring Pixel Relationships(NPR) as a means to capture and characterize the generalized structural artifacts stemming from up-sampling operations. A comprehensive analysis is conducted on an open-world dataset comprising samples generated by 28 distinct generative models. This analysis culminates in the establishment of a novel state-of-the-art performance showcasing a remarkable 12.8% improvement over existing methods. The code is available at https://github.com/chuangchuangtan/NPR-DeepfakeDetection. | https://openaccess.thecvf.com/content/CVPR2024/papers/Tan_Rethinking_the_Up-Sampling_Operations_in_CNN-based_Generative_Network_for_Generalizable_CVPR_2024_paper.pdf | http://arxiv.org/abs/2312.10461 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Tan_Rethinking_the_Up-Sampling_Operations_in_CNN-based_Generative_Network_for_Generalizable_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Tan_Rethinking_the_Up-Sampling_Operations_in_CNN-based_Generative_Network_for_Generalizable_CVPR_2024_paper.html | CVPR 2024 | null | null |
Co-Speech Gesture Video Generation via Motion-Decoupled Diffusion Model | Xu He, Qiaochu Huang, Zhensong Zhang, Zhiwei Lin, Zhiyong Wu, Sicheng Yang, Minglei Li, Zhiyi Chen, Songcen Xu, Xiaofei Wu | Co-speech gestures if presented in the lively form of videos can achieve superior visual effects in human-machine interaction. While previous works mostly generate structural human skeletons resulting in the omission of appearance information we focus on the direct generation of audio-driven co-speech gesture videos in this work. There are two main challenges: 1) A suitable motion feature is needed to describe complex human movements with crucial appearance information. 2) Gestures and speech exhibit inherent dependencies and should be temporally aligned even of arbitrary length. To solve these problems we present a novel motion-decoupled framework to generate co-speech gesture videos. Specifically we first introduce a well-designed nonlinear TPS transformation to obtain latent motion features preserving essential appearance information. Then a transformer-based diffusion model is proposed to learn the temporal correlation between gestures and speech and performs generation in the latent motion space followed by an optimal motion selection module to produce long-term coherent and consistent gesture videos. For better visual perception we further design a refinement network focusing on missing details of certain areas. Extensive experimental results show that our proposed framework significantly outperforms existing approaches in both motion and video-related evaluations. Our code demos and more resources are available at https://github.com/thuhcsi/S2G-MDDiffusion. | https://openaccess.thecvf.com/content/CVPR2024/papers/He_Co-Speech_Gesture_Video_Generation_via_Motion-Decoupled_Diffusion_Model_CVPR_2024_paper.pdf | http://arxiv.org/abs/2404.01862 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/He_Co-Speech_Gesture_Video_Generation_via_Motion-Decoupled_Diffusion_Model_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/He_Co-Speech_Gesture_Video_Generation_via_Motion-Decoupled_Diffusion_Model_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/He_Co-Speech_Gesture_Video_CVPR_2024_supplemental.pdf | null |
CDFormer: When Degradation Prediction Embraces Diffusion Model for Blind Image Super-Resolution | Qingguo Liu, Chenyi Zhuang, Pan Gao, Jie Qin | Existing Blind image Super-Resolution (BSR) methods focus on estimating either kernel or degradation information but have long overlooked the essential content details. In this paper we propose a novel BSR approach Content-aware Degradation-driven Transformer (CDFormer) to capture both degradation and content representations. However low-resolution images cannot provide enough content details and thus we introduce a diffusion-based module CDFormer_ diff to first learn Content Degradation Prior (CDP) in both low- and high-resolution images and then approximate the real distribution given only low-resolution information. Moreover we apply an adaptive SR network CDFormer_ SR that effectively utilizes CDP to refine features. Compared to previous diffusion-based SR methods we treat the diffusion model as an estimator that can overcome the limitations of expensive sampling time and excessive diversity. Experiments show that CDFormer can outperform existing methods establishing a new state-of-the-art performance on various benchmarks under blind settings. Codes and models will be available at https://github.com/I2-Multimedia-Lab/CDFormer. | https://openaccess.thecvf.com/content/CVPR2024/papers/Liu_CDFormer_When_Degradation_Prediction_Embraces_Diffusion_Model_for_Blind_Image_CVPR_2024_paper.pdf | http://arxiv.org/abs/2405.07648 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Liu_CDFormer_When_Degradation_Prediction_Embraces_Diffusion_Model_for_Blind_Image_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Liu_CDFormer_When_Degradation_Prediction_Embraces_Diffusion_Model_for_Blind_Image_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Liu_CDFormer_When_Degradation_CVPR_2024_supplemental.pdf | null |
HumanRef: Single Image to 3D Human Generation via Reference-Guided Diffusion | Jingbo Zhang, Xiaoyu Li, Qi Zhang, Yanpei Cao, Ying Shan, Jing Liao | Generating a 3D human model from a single reference image is challenging because it requires inferring textures and geometries in invisible views while maintaining consistency with the reference image. Previous methods utilizing 3D generative models are limited by the availability of 3D training data. Optimization-based methods that lift text-to-image diffusion models to 3D generation often fail to preserve the texture details of the reference image resulting in inconsistent appearances in different views. In this paper we propose HumanRef a 3D human generation framework from a single-view input. To ensure the generated 3D model is photorealistic and consistent with the input image HumanRef introduces a novel method called reference-guided score distillation sampling (Ref-SDS) which effectively incorporates image guidance into the generation process. Furthermore we introduce region-aware attention to Ref-SDS ensuring accurate correspondence between different body regions. Experimental results demonstrate that HumanRef outperforms state-of-the-art methods in generating 3D clothed humans with fine geometry photorealistic textures and view-consistent appearances. Code and model are available at https://eckertzhang.github.io/HumanRef.github.io/. | https://openaccess.thecvf.com/content/CVPR2024/papers/Zhang_HumanRef_Single_Image_to_3D_Human_Generation_via_Reference-Guided_Diffusion_CVPR_2024_paper.pdf | http://arxiv.org/abs/2311.16961 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Zhang_HumanRef_Single_Image_to_3D_Human_Generation_via_Reference-Guided_Diffusion_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Zhang_HumanRef_Single_Image_to_3D_Human_Generation_via_Reference-Guided_Diffusion_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Zhang_HumanRef_Single_Image_CVPR_2024_supplemental.pdf | null |
GlitchBench: Can Large Multimodal Models Detect Video Game Glitches? | Mohammad Reza Taesiri, Tianjun Feng, Cor-Paul Bezemer, Anh Nguyen | Large multimodal models (LMMs) have evolved from large language models (LLMs) to integrate multiple input modalities such as visual inputs. This integration augments the capacity of LLMs for tasks requiring visual comprehension and reasoning. However the extent and limitations of their enhanced abilities are not fully understood especially when it comes to real-world tasks. To address this gap we introduce GlitchBench a novel benchmark derived from video game quality assurance tasks to test and evaluate the reasoning capabilities of LMMs. Our benchmark is curated from a variety of unusual and glitched scenarios from video games and aims to challenge both the visual and linguistic reasoning powers of LMMs in detecting and interpreting out-of-the-ordinary events. We evaluate multiple state-of-the-art LMMs and we show that GlitchBench presents a new challenge for these models. Code and data are available at: https://glitchbench.github.io/ | https://openaccess.thecvf.com/content/CVPR2024/papers/Taesiri_GlitchBench_Can_Large_Multimodal_Models_Detect_Video_Game_Glitches_CVPR_2024_paper.pdf | http://arxiv.org/abs/2312.05291 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Taesiri_GlitchBench_Can_Large_Multimodal_Models_Detect_Video_Game_Glitches_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Taesiri_GlitchBench_Can_Large_Multimodal_Models_Detect_Video_Game_Glitches_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Taesiri_GlitchBench_Can_Large_CVPR_2024_supplemental.pdf | null |
Rethinking Interactive Image Segmentation with Low Latency High Quality and Diverse Prompts | Qin Liu, Jaemin Cho, Mohit Bansal, Marc Niethammer | The goal of interactive image segmentation is to delineate specific regions within an image via visual or language prompts. Low-latency and high-quality interactive segmentation with diverse prompts remain challenging for existing specialist and generalist models. Specialist models with their limited prompts and task-specific designs experience high latency because the image must be recomputed every time the prompt is updated due to the joint encoding of image and visual prompts. Generalist models exemplified by the Segment Anything Model (SAM) have recently excelled in prompt diversity and efficiency lifting image segmentation to the foundation model era. However for high-quality segmentations SAM still lags behind state-of-the-art specialist models despite SAM being trained with x100 more segmentation masks. In this work we delve deep into the architectural differences between the two types of models. We observe that dense representation and fusion of visual prompts are the key design choices contributing to the high segmentation quality of specialist models. In light of this we reintroduce this dense design into the generalist models to facilitate the development of generalist models with high segmentation quality. To densely represent diverse visual prompts we propose to use a dense map to capture five types: clicks boxes polygons scribbles and masks. Thus we propose SegNext a next-generation interactive segmentation approach offering low latency high quality and diverse prompt support. Our method outperforms current state-of-the-art methods on HQSeg-44K and DAVIS quantitatively and qualitatively. | https://openaccess.thecvf.com/content/CVPR2024/papers/Liu_Rethinking_Interactive_Image_Segmentation_with_Low_Latency_High_Quality_and_CVPR_2024_paper.pdf | http://arxiv.org/abs/2404.00741 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Liu_Rethinking_Interactive_Image_Segmentation_with_Low_Latency_High_Quality_and_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Liu_Rethinking_Interactive_Image_Segmentation_with_Low_Latency_High_Quality_and_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Liu_Rethinking_Interactive_Image_CVPR_2024_supplemental.zip | null |
ALGM: Adaptive Local-then-Global Token Merging for Efficient Semantic Segmentation with Plain Vision Transformers | Narges Norouzi, Svetlana Orlova, Daan de Geus, Gijs Dubbelman | This work presents Adaptive Local-then-Global Merging (ALGM) a token reduction method for semantic segmentation networks that use plain Vision Transformers. ALGM merges tokens in two stages: (1) In the first network layer it merges similar tokens within a small local window and (2) halfway through the network it merges similar tokens across the entire image. This is motivated by an analysis in which we found that in those situations tokens with a high cosine similarity can likely be merged without a drop in segmentation quality. With extensive experiments across multiple datasets and network configurations we show that ALGM not only significantly improves the throughput by up to 100% but can also enhance the mean IoU by up to +1.1 thereby achieving a better trade-off between segmentation quality and efficiency than existing methods. Moreover our approach is adaptive during inference meaning that the same model can be used for optimal efficiency or accuracy depending on the application. Code is available at https://tue-mps.github.io/ALGM. | https://openaccess.thecvf.com/content/CVPR2024/papers/Norouzi_ALGM_Adaptive_Local-then-Global_Token_Merging_for_Efficient_Semantic_Segmentation_with_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Norouzi_ALGM_Adaptive_Local-then-Global_Token_Merging_for_Efficient_Semantic_Segmentation_with_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Norouzi_ALGM_Adaptive_Local-then-Global_Token_Merging_for_Efficient_Semantic_Segmentation_with_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Norouzi_ALGM_Adaptive_Local-then-Global_CVPR_2024_supplemental.pdf | null |
DITTO: Dual and Integrated Latent Topologies for Implicit 3D Reconstruction | Jaehyeok Shim, Kyungdon Joo | We propose a novel concept of dual and integrated latent topologies (DITTO in short) for implicit 3D reconstruction from noisy and sparse point clouds. Most existing methods predominantly focus on single latent type such as point or grid latents. In contrast the proposed DITTO leverages both point and grid latents (i.e. dual latent) to enhance their strengths the stability of grid latents and the detail-rich capability of point latents. Concretely DITTO consists of dual latent encoder and integrated implicit decoder. In the dual latent encoder a dual latent layer which is the key module block composing the encoder refines both latents in parallel maintaining their distinct shapes and enabling recursive interaction. Notably a newly proposed dynamic sparse point transformer within the dual latent layer effectively refines point latents. Then the integrated implicit decoder systematically combines these refined latents achieving high-fidelity 3D reconstruction and surpassing previous state-of-the-art methods on object- and scene-level datasets especially in thin and detailed structures. | https://openaccess.thecvf.com/content/CVPR2024/papers/Shim_DITTO_Dual_and_Integrated_Latent_Topologies_for_Implicit_3D_Reconstruction_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.05005 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Shim_DITTO_Dual_and_Integrated_Latent_Topologies_for_Implicit_3D_Reconstruction_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Shim_DITTO_Dual_and_Integrated_Latent_Topologies_for_Implicit_3D_Reconstruction_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Shim_DITTO_Dual_and_CVPR_2024_supplemental.pdf | null |
Single-Model and Any-Modality for Video Object Tracking | Zongwei Wu, Jilai Zheng, Xiangxuan Ren, Florin-Alexandru Vasluianu, Chao Ma, Danda Pani Paudel, Luc Van Gool, Radu Timofte | In the realm of video object tracking auxiliary modalities such as depth thermal or event data have emerged as valuable assets to complement the RGB trackers. In practice most existing RGB trackers learn a single set of parameters to use them across datasets and applications. However a similar single-model unification for multi-modality tracking presents several challenges. These challenges stem from the inherent heterogeneity of inputs -- each with modality-specific representations the scarcity of multi-modal datasets and the absence of all the modalities at all times. In this work we introduce Un-Track a Unified Tracker of a single set of parameters for any modality. To handle any modality our method learns their common latent space through low-rank factorization and reconstruction techniques. More importantly we use only the RGB-X pairs to learn the common latent space. This unique shared representation seamlessly binds all modalities together enabling effective unification and accommodating any missing modality all within a single transformer-based architecture. Our Un-Track achieves +8.1 absolute F-score gain on the DepthTrack dataset by introducing only +2.14 (over 21.50) GFLOPs with +6.6M (over 93M) parameters through a simple yet efficient prompting strategy. Extensive comparisons on five benchmark datasets with different modalities show that Un-Track surpasses both SOTA unified trackers and modality-specific counterparts validating our effectiveness and practicality. The source code is publicly available at https://github.com/Zongwei97/UnTrack. | https://openaccess.thecvf.com/content/CVPR2024/papers/Wu_Single-Model_and_Any-Modality_for_Video_Object_Tracking_CVPR_2024_paper.pdf | http://arxiv.org/abs/2311.15851 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Wu_Single-Model_and_Any-Modality_for_Video_Object_Tracking_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Wu_Single-Model_and_Any-Modality_for_Video_Object_Tracking_CVPR_2024_paper.html | CVPR 2024 | null | null |
FlowTrack: Revisiting Optical Flow for Long-Range Dense Tracking | Seokju Cho, Jiahui Huang, Seungryong Kim, Joon-Young Lee | In the domain of video tracking existing methods often grapple with a trade-off between spatial density and temporal range. Current approaches in dense optical flow estimators excel in providing spatially dense tracking but are limited to short temporal spans. Conversely recent advancements in long-range trackers offer extended temporal coverage but at the cost of spatial sparsity. This paper introduces FlowTrack a novel framework designed to bridge this gap. FlowTrack combines the strengths of both paradigms by 1) chaining confident flow predictions to maximize efficiency and 2) automatically switching to an error compensation module in instances of flow prediction inaccuracies. This dual strategy not only offers efficient dense tracking over extended temporal spans but also ensures robustness against error accumulations and occlusions common pitfalls of naive flow chaining. Furthermore we demonstrate that chained flow itself can serve as an effective guide for an error compensation module even for occluded points. Our framework achieves state-of-the-art accuracy for long-range tracking on the DAVIS dataset and renders 50% speed-up when performing dense tracking. | https://openaccess.thecvf.com/content/CVPR2024/papers/Cho_FlowTrack_Revisiting_Optical_Flow_for_Long-Range_Dense_Tracking_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Cho_FlowTrack_Revisiting_Optical_Flow_for_Long-Range_Dense_Tracking_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Cho_FlowTrack_Revisiting_Optical_Flow_for_Long-Range_Dense_Tracking_CVPR_2024_paper.html | CVPR 2024 | null | null |
HIT: Estimating Internal Human Implicit Tissues from the Body Surface | Marilyn Keller, Vaibhav Arora, Abdelmouttaleb Dakri, Shivam Chandhok, Jürgen Machann, Andreas Fritsche, Michael J. Black, Sergi Pujades | The creation of personalized anatomical digital twins is important in the fields of medicine computer graphics sports science and biomechanics. To observe a subject's anatomy expensive medical devices (MRI or CT) are required and the creation of the digital model is often time-consuming and involves manual effort. Instead we leverage the fact that the shape of the body surface is correlated with the internal anatomy; e.g. from surface observations alone one can predict body composition and skeletal structure. In this work we go further and learn to infer the 3D location of three important anatomic tissues: subcutaneous adipose tissue (fat) lean tissue (muscles and organs) and long bones. To learn to infer these tissues we tackle several key challenges. We first create a dataset of human tissues by segmenting full-body MRI scans and registering the SMPL body mesh to the body surface. With this dataset we train HIT (Human Implicit Tissues) an implicit function that given a point inside a body predicts its tissue class. HIT leverages the SMPL body model shape and pose parameters to canonicalize the medical data. Unlike SMPL which is trained from upright 3D scans MRI scans are acquired with subjects lying on a table resulting in significant soft-tissue deformation. Consequently HIT uses a learned volumetric deformation field that undoes these deformations. Since HIT is parameterized by SMPL we can repose bodies or change the shape of subjects and the internal structures deform appropriately. We perform extensive experiments to validate HIT's ability to predict a plausible internal structure for novel subjects. The dataset and HIT model are available at https://hit.is.tue.mpg.de to foster future research in this direction. | https://openaccess.thecvf.com/content/CVPR2024/papers/Keller_HIT_Estimating_Internal_Human_Implicit_Tissues_from_the_Body_Surface_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Keller_HIT_Estimating_Internal_Human_Implicit_Tissues_from_the_Body_Surface_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Keller_HIT_Estimating_Internal_Human_Implicit_Tissues_from_the_Body_Surface_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Keller_HIT_Estimating_Internal_CVPR_2024_supplemental.pdf | null |
DanceCamera3D: 3D Camera Movement Synthesis with Music and Dance | Zixuan Wang, Jia Jia, Shikun Sun, Haozhe Wu, Rong Han, Zhenyu Li, Di Tang, Jiaqing Zhou, Jiebo Luo | Choreographers determine what the dances look like while cameramen determine the final presentation of dances. Recently various methods and datasets have showcased the feasibility of dance synthesis. However camera movement synthesis with music and dance remains an unsolved challenging problem due to the scarcity of paired data. Thus we present DCM a new multi-modal 3D dataset which for the first time combines camera movement with dance motion and music audio. This dataset encompasses 108 dance sequences (3.2 hours) of paired dance-camera-music data from the anime community covering 4 music genres. With this dataset we uncover that dance camera movement is multifaceted and human-centric and possesses multiple influencing factors making dance camera synthesis a more challenging task compared to camera or dance synthesis alone. To overcome these difficulties we propose DanceCamera3D a transformer-based diffusion model that incorporates a novel body attention loss and a condition separation strategy. For evaluation we devise new metrics measuring camera movement quality diversity and dancer fidelity. Utilizing these metrics we conduct extensive experiments on our DCM dataset providing both quantitative and qualitative evidence showcasing the effectiveness of our DanceCamera3D model. Code and video demos are available at https://github.com/ Carmenw1203/DanceCamera3D-Official. | https://openaccess.thecvf.com/content/CVPR2024/papers/Wang_DanceCamera3D_3D_Camera_Movement_Synthesis_with_Music_and_Dance_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.13667 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Wang_DanceCamera3D_3D_Camera_Movement_Synthesis_with_Music_and_Dance_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Wang_DanceCamera3D_3D_Camera_Movement_Synthesis_with_Music_and_Dance_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Wang_DanceCamera3D_3D_Camera_CVPR_2024_supplemental.pdf | null |
Synthesize Diagnose and Optimize: Towards Fine-Grained Vision-Language Understanding | Wujian Peng, Sicheng Xie, Zuyao You, Shiyi Lan, Zuxuan Wu | Vision language models (VLM) have demonstrated remarkable performance across various downstream tasks. However understanding fine-grained visual-linguistic concepts such as attributes and inter-object relationships remains a significant challenge. While several benchmarks aim to evaluate VLMs in finer granularity their primary focus remains on the linguistic aspect neglecting the visual dimension. Here we highlight the importance of evaluating VLMs from both a textual and visual perspective. We introduce a progressive pipeline to synthesize images that vary in a specific attribute while ensuring consistency in all other aspects. Utilizing this data engine we carefully design a benchmark SPEC to diagnose the comprehension of object size position existence and count. Subsequently we conduct a thorough evaluation of four leading VLMs on SPEC. Surprisingly their performance is close to random guess revealing significant limitations. With this in mind we propose a simple yet effective approach to optimize VLMs in fine-grained understanding achieving significant improvements on SPEC without compromising the zero-shot performance. Results on two additional fine-grained benchmarks also show consistent improvements further validating the transferability of our approach. Code and data are available at https://github.com/wjpoom/SPEC. | https://openaccess.thecvf.com/content/CVPR2024/papers/Peng_Synthesize_Diagnose_and_Optimize_Towards_Fine-Grained_Vision-Language_Understanding_CVPR_2024_paper.pdf | http://arxiv.org/abs/2312.00081 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Peng_Synthesize_Diagnose_and_Optimize_Towards_Fine-Grained_Vision-Language_Understanding_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Peng_Synthesize_Diagnose_and_Optimize_Towards_Fine-Grained_Vision-Language_Understanding_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Peng_Synthesize_Diagnose_and_CVPR_2024_supplemental.pdf | null |
Density-guided Translator Boosts Synthetic-to-Real Unsupervised Domain Adaptive Segmentation of 3D Point Clouds | Zhimin Yuan, Wankang Zeng, Yanfei Su, Weiquan Liu, Ming Cheng, Yulan Guo, Cheng Wang | 3D synthetic-to-real unsupervised domain adaptive segmentation is crucial to annotating new domains. Self-training is a competitive approach for this task but its performance is limited by different sensor sampling patterns (i.e. variations in point density) and incomplete training strategies. In this work we propose a density-guided translator (DGT) which translates point density between domains and integrates it into a two-stage self-training pipeline named DGT-ST. First in contrast to existing works that simultaneously conduct data generation and feature/output alignment within unstable adversarial training we employ the non-learnable DGT to bridge the domain gap at the input level. Second to provide a well-initialized model for self-training we propose a category-level adversarial network in stage one that utilizes the prototype to prevent negative transfer. Finally by leveraging the designs above a domain-mixed self-training method with source-aware consistency loss is proposed in stage two to narrow the domain gap further. Experiments on two synthetic-to-real segmentation tasks (SynLiDAR ? semanticKITTI and SynLiDAR ? semanticPOSS) demonstrate that DGT-ST outperforms state-of-the-art methods achieving 9.4% and 4.3% mIoU improvements respectively. Code is available at https://github.com/yuan-zm/DGT-ST. | https://openaccess.thecvf.com/content/CVPR2024/papers/Yuan_Density-guided_Translator_Boosts_Synthetic-to-Real_Unsupervised_Domain_Adaptive_Segmentation_of_3D_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.18469 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Yuan_Density-guided_Translator_Boosts_Synthetic-to-Real_Unsupervised_Domain_Adaptive_Segmentation_of_3D_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Yuan_Density-guided_Translator_Boosts_Synthetic-to-Real_Unsupervised_Domain_Adaptive_Segmentation_of_3D_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Yuan_Density-guided_Translator_Boosts_CVPR_2024_supplemental.pdf | null |
Cross Initialization for Face Personalization of Text-to-Image Models | Lianyu Pang, Jian Yin, Haoran Xie, Qiping Wang, Qing Li, Xudong Mao | Recently there has been a surge in face personalization techniques benefiting from the advanced capabilities of pretrained text-to-image diffusion models. Among these a notable method is Textual Inversion which generates personalized images by inverting given images into textual embeddings. However methods based on Textual Inversion still struggle with balancing the trade-off between reconstruction quality and editability. In this study we examine this issue through the lens of initialization. Upon closely examining traditional initialization methods we identified a significant disparity between the initial and learned embeddings in terms of both scale and orientation. The scale of the learned embedding can be up to 100 times greater than that of the initial embedding. Such a significant change in the embedding could increase the risk of overfitting thereby compromising the editability. Driven by this observation we introduce a novel initialization method termed Cross Initialization that significantly narrows the gap between the initial and learned embeddings. This method not only improves both reconstruction and editability but also reduces the optimization steps from 5000 to 320. Furthermore we apply a regularization term to keep the learned embedding close to the initial embedding. We show that when combined with Cross Initialization this regularization term can effectively improve editability. We provide comprehensive empirical evidence to demonstrate the superior performance of our method compared to the baseline methods. Notably in our experiments Cross Initialization is the only method that successfully edits an individual's facial expression. Additionally a fast version of our method allows for capturing an input image in roughly 26 seconds while surpassing the baseline methods in terms of both reconstruction and editability. Code is available at https://github.com/lyuPang/CrossInitialization. | https://openaccess.thecvf.com/content/CVPR2024/papers/Pang_Cross_Initialization_for_Face_Personalization_of_Text-to-Image_Models_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Pang_Cross_Initialization_for_Face_Personalization_of_Text-to-Image_Models_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Pang_Cross_Initialization_for_Face_Personalization_of_Text-to-Image_Models_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Pang_Cross_Initialization_for_CVPR_2024_supplemental.pdf | null |
LEDITS++: Limitless Image Editing using Text-to-Image Models | Manuel Brack, Felix Friedrich, Katharia Kornmeier, Linoy Tsaban, Patrick Schramowski, Kristian Kersting, Apolinario Passos | Text-to-image diffusion models have recently received increasing interest for their astonishing ability to produce high-fidelity images from solely text inputs. Subsequent research efforts aim to exploit and apply their capabilities to real image editing. However existing image-to-image methods are often inefficient imprecise and of limited versatility. They either require time-consuming fine-tuning deviate unnecessarily strongly from the input image and/or lack support for multiple simultaneous edits. To address these issues we introduce LEDITS++ an efficient yet versatile and precise textual image manipulation technique. LEDITS++'s novel inversion approach requires no tuning nor optimization and produces high-fidelity results with a few diffusion steps. Second our methodology supports multiple simultaneous edits and is architecture-agnostic. Third we use a novel implicit masking technique that limits changes to relevant image regions. We propose the novel TEdBench++ benchmark as part of our exhaustive evaluation. Our results demonstrate the capabilities of LEDITS++ and its improvements over previous methods. | https://openaccess.thecvf.com/content/CVPR2024/papers/Brack_LEDITS_Limitless_Image_Editing_using_Text-to-Image_Models_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Brack_LEDITS_Limitless_Image_Editing_using_Text-to-Image_Models_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Brack_LEDITS_Limitless_Image_Editing_using_Text-to-Image_Models_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Brack_LEDITS_Limitless_Image_CVPR_2024_supplemental.pdf | null |
Video Interpolation with Diffusion Models | Siddhant Jain, Daniel Watson, Eric Tabellion, Aleksander Ho?ynski, Ben Poole, Janne Kontkanen | We present VIDIM a generative model for video interpolation which creates short videos given a start and end frame. In order to achieve high fidelity and generate motions unseen in the input data VIDIM uses cascaded diffusion models to first generate the target video at low resolution and then generate the high-resolution video conditioned on the low-resolution generated video. We compare VIDIM to previous state-of-the-art methods on video interpolation and demonstrate how such works fail in most settings where the underlying motion is complex nonlinear or ambiguous while VIDIM can easily handle such cases. We additionally demonstrate how classifier-free guidance on the start and end frame and conditioning the superresolution model on the original high-resolution frames without additional parameters unlocks high-fidelity results. VIDIM is fast to sample from as it jointly denoises all the frames to be generated requires less than a billion parameters per diffusion model to produce compelling results and still enjoys scalability and improved quality at larger parameter counts. Please see our project page at vidiminterpolation.github.io. | https://openaccess.thecvf.com/content/CVPR2024/papers/Jain_Video_Interpolation_with_Diffusion_Models_CVPR_2024_paper.pdf | http://arxiv.org/abs/2404.01203 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Jain_Video_Interpolation_with_Diffusion_Models_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Jain_Video_Interpolation_with_Diffusion_Models_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Jain_Video_Interpolation_with_CVPR_2024_supplemental.pdf | null |
WildlifeMapper: Aerial Image Analysis for Multi-Species Detection and Identification | Satish Kumar, Bowen Zhang, Chandrakanth Gudavalli, Connor Levenson, Lacey Hughey, Jared A. Stabach, Irene Amoke, Gordon Ojwang, Joseph Mukeka, Stephen Mwiu, Joseph Ogutu, Howard Frederick, B.S. Manjunath | We introduce WildlifeMapper (WM) a flexible model designed to detect locate and identify multiple species in aerial imagery. It addresses the limitations of traditional labor-intensive wildlife population assessments that are central to advancing environmental conservation efforts worldwide. While a number of methods exist to automate this process they are often limited in their ability to generalize to different species or landscapes due to the dominance of homogeneous backgrounds and/or poorly captured local image structures. WM introduces two novel modules that help to capture the local structure and context of objects of interest to accurately localize and identify them achieving a state-of-the-art (SOTA) detection rate of 0.56 mAP. Further we introduce a large aerial imagery dataset with more than 11k Images and 28k annotations verified by trained experts. WM also achieves SOTA performance on 3 other publicly available aerial survey datasets collected across 4 different countries improving mAP by 42%. Source code and trained models are available at Github | https://openaccess.thecvf.com/content/CVPR2024/papers/Kumar_WildlifeMapper_Aerial_Image_Analysis_for_Multi-Species_Detection_and_Identification_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Kumar_WildlifeMapper_Aerial_Image_Analysis_for_Multi-Species_Detection_and_Identification_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Kumar_WildlifeMapper_Aerial_Image_Analysis_for_Multi-Species_Detection_and_Identification_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Kumar_WildlifeMapper_Aerial_Image_CVPR_2024_supplemental.pdf | null |
Learning Adaptive Spatial Coherent Correlations for Speech-Preserving Facial Expression Manipulation | Tianshui Chen, Jianman Lin, Zhijing Yang, Chunmei Qing, Liang Lin | Speech-preserving facial expression manipulation (SPFEM) aims to modify facial emotions while meticulously maintaining the mouth animation associated with spoken content. Current works depend on inaccessible paired training samples for the person where two aligned frames exhibit the same speech content yet differ in emotional expression limiting the SPFEM applications in real-world scenarios. In this work we discover that speakers who convey the same content with different emotions exhibit highly correlated local facial animations providing valuable supervision for SPFEM. To capitalize on this insight we propose a novel adaptive spatial coherent correlation learning (ASCCL) algorithm which models the aforementioned correlation as an explicit metric and integrates the metric to supervise manipulating facial expression and meanwhile better preserving the facial animation of spoken contents. To this end it first learns a spatial coherent correlation metric ensuring the visual disparities of adjacent local regions of the image belonging to one emotion are similar to those of the corresponding counterpart of the image belonging to another emotion. Recognizing that visual disparities are not uniform across all regions we have also crafted a disparity-aware adaptive strategy that prioritizes regions that present greater challenges. During SPFEM model training we construct the adaptive spatial coherent correlation metric between corresponding local regions of the input and output images as addition loss to supervise the generation process. We conduct extensive experiments on variant datasets and the results demonstrate the effectiveness of the proposed ASCCL algorithm. Code is publicly available at https://github.com/jianmanlincjx/ASCCL | https://openaccess.thecvf.com/content/CVPR2024/papers/Chen_Learning_Adaptive_Spatial_Coherent_Correlations_for_Speech-Preserving_Facial_Expression_Manipulation_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Chen_Learning_Adaptive_Spatial_Coherent_Correlations_for_Speech-Preserving_Facial_Expression_Manipulation_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Chen_Learning_Adaptive_Spatial_Coherent_Correlations_for_Speech-Preserving_Facial_Expression_Manipulation_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Chen_Learning_Adaptive_Spatial_CVPR_2024_supplemental.pdf | null |
Tune-An-Ellipse: CLIP Has Potential to Find What You Want | Jinheng Xie, Songhe Deng, Bing Li, Haozhe Liu, Yawen Huang, Yefeng Zheng, Jurgen Schmidhuber, Bernard Ghanem, Linlin Shen, Mike Zheng Shou | Visual prompting of large vision language models such as CLIP exhibits intriguing zero-shot capabilities. A manually drawn red circle commonly used for highlighting can guide CLIP's attention to the surrounding region to identify specific objects within an image. Without precise object proposals however it is insufficient for localization. Our novel simple yet effective approach i.e. Differentiable Visual Prompting enables CLIP to zero-shot localize: given an image and a text prompt describing an object we first pick a rendered ellipse from uniformly distributed anchor ellipses on the image grid via visual prompting then use three loss functions to tune the ellipse coefficients to encapsulate the target region gradually. This yields promising experimental results for referring expression comprehension without precisely specified object proposals. In addition we systematically present the limitations of visual prompting inherent in CLIP and discuss potential solutions. | https://openaccess.thecvf.com/content/CVPR2024/papers/Xie_Tune-An-Ellipse_CLIP_Has_Potential_to_Find_What_You_Want_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Xie_Tune-An-Ellipse_CLIP_Has_Potential_to_Find_What_You_Want_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Xie_Tune-An-Ellipse_CLIP_Has_Potential_to_Find_What_You_Want_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Xie_Tune-An-Ellipse_CLIP_Has_CVPR_2024_supplemental.pdf | null |
Neural Spline Fields for Burst Image Fusion and Layer Separation | Ilya Chugunov, David Shustin, Ruyu Yan, Chenyang Lei, Felix Heide | Each photo in an image burst can be considered a sample of a complex 3D scene: the product of parallax diffuse and specular materials scene motion and illuminant variation. While decomposing all of these effects from a stack of misaligned images is a highly ill-conditioned task the conventional align-and-merge burst pipeline takes the other extreme: blending them into a single image. In this work we propose a versatile intermediate representation: a two-layer alpha-composited image plus flow model constructed with neural spline fields -- networks trained to map input coordinates to spline control points. Our method is able to during test-time optimization jointly fuse a burst image capture into one high-resolution reconstruction and decompose it into transmission and obstruction layers. Then by discarding the obstruction layer we can perform a range of tasks including seeing through occlusions reflection suppression and shadow removal. Tested on complex in-the-wild captures we find that with no post-processing steps or learned priors our generalizable model is able to outperform existing dedicated single-image and multi-view obstruction removal approaches. | https://openaccess.thecvf.com/content/CVPR2024/papers/Chugunov_Neural_Spline_Fields_for_Burst_Image_Fusion_and_Layer_Separation_CVPR_2024_paper.pdf | http://arxiv.org/abs/2312.14235 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Chugunov_Neural_Spline_Fields_for_Burst_Image_Fusion_and_Layer_Separation_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Chugunov_Neural_Spline_Fields_for_Burst_Image_Fusion_and_Layer_Separation_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Chugunov_Neural_Spline_Fields_CVPR_2024_supplemental.pdf | null |
WHAM: Reconstructing World-grounded Humans with Accurate 3D Motion | Soyong Shin, Juyong Kim, Eni Halilaj, Michael J. Black | The estimation of 3D human motion from video has progressed rapidly but current methods still have several key limitations. First most methods estimate the human in camera coordinates. Second prior work on estimating humans in global coordinates often assumes a flat ground plane and produces foot sliding. Third the most accurate methods rely on computationally expensive optimization pipelines limiting their use to offline applications. Finally existing video-based methods are surprisingly less accurate than single-frame methods. We address these limitations with WHAM (World-grounded Humans with Accurate Motion) which accurately and efficiently reconstructs 3D human motion in a global coordinate system from video. WHAM learns to lift 2D keypoint sequences to 3D using motion capture data and fuses this with video features integrating motion context and visual information. WHAM exploits camera angular velocity estimated from a SLAM method together with human motion to estimate the body's global trajectory. We combine this with a contact-aware trajectory refinement method that lets WHAM capture human motion in diverse conditions such as climbing stairs. WHAM outperforms all existing 3D human motion recovery methods across multiple in-the-wild benchmarks. Code is available for research purposes at http://wham.is.tue.mpg.de/. | https://openaccess.thecvf.com/content/CVPR2024/papers/Shin_WHAM_Reconstructing_World-grounded_Humans_with_Accurate_3D_Motion_CVPR_2024_paper.pdf | http://arxiv.org/abs/2312.07531 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Shin_WHAM_Reconstructing_World-grounded_Humans_with_Accurate_3D_Motion_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Shin_WHAM_Reconstructing_World-grounded_Humans_with_Accurate_3D_Motion_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Shin_WHAM_Reconstructing_World-grounded_CVPR_2024_supplemental.zip | null |
NAPGuard: Towards Detecting Naturalistic Adversarial Patches | Siyang Wu, Jiakai Wang, Jiejie Zhao, Yazhe Wang, Xianglong Liu | Recently the emergence of naturalistic adversarial patch (NAP) which possesses a deceptive appearance and various representations underscores the necessity of developing robust detection strategies. However existing approaches fail to differentiate the deep-seated natures in adversarial patches i.e. aggressiveness and naturalness leading to unsatisfactory precision and generalization against NAPs. To tackle this issue we propose NAPGuard to provide strong detection capability against NAPs via the elaborated critical feature modulation framework. For improving precision we propose the aggressive feature aligned learning to enhance the model's capability in capturing accurate aggressive patterns. Considering the challenge of inaccurate model learning caused by deceptive appearance we align the aggressive features by the proposed pattern alignment loss during training. Since the model could learn more accurate aggressive patterns it is able to detect deceptive patches more precisely. To enhance generalization we design the natural feature suppressed inference to universally mitigate the disturbance from different NAPs. Since various representations arise in diverse disturbing forms to hinder generalization we suppress the natural features in a unified approach via the feature shield module. Therefore the models could recognize NAPs within less disturbance and activate the generalized detection ability. Extensive experiments show that our method surpasses state-of-the-art methods by large margins in detecting NAPs (improve 60.24% [email protected] on average). | https://openaccess.thecvf.com/content/CVPR2024/papers/Wu_NAPGuard_Towards_Detecting_Naturalistic_Adversarial_Patches_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Wu_NAPGuard_Towards_Detecting_Naturalistic_Adversarial_Patches_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Wu_NAPGuard_Towards_Detecting_Naturalistic_Adversarial_Patches_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Wu_NAPGuard_Towards_Detecting_CVPR_2024_supplemental.pdf | null |
DiffPerformer: Iterative Learning of Consistent Latent Guidance for Diffusion-based Human Video Generation | Chenyang Wang, Zerong Zheng, Tao Yu, Xiaoqian Lv, Bineng Zhong, Shengping Zhang, Liqiang Nie | Existing diffusion models for pose-guided human video generation mostly suffer from temporal inconsistency in the generated appearance and poses due to the inherent randomization nature of the generation process. In this paper we propose a novel framework DiffPerformer to synthesize high-fidelity and temporally consistent human video. Without complex architecture modification or costly training DiffPerformer finetunes a pretrained diffusion model on a single video of the target character and introduces an implicit video representation as a proxy to learn temporally consistent guidance for the diffusion model. The guidance is encoded into VAE latent space and an iterative optimization loop is constructed between the implicit video representation and the diffusion model allowing to harness the smooth property of the implicit video representation and the generative capabilities of the diffusion model in a mutually beneficial way. Moreover we propose 3D-aware human flow as a temporal constraint during the optimization to explicitly model the correspondence between driving poses and human appearance. This alleviates the misalignment between guided poses and target performer and therefore maintains the appearance coherence under various motions. Extensive experiments demonstrate that our method outperforms the state-of-the-art methods. | https://openaccess.thecvf.com/content/CVPR2024/papers/Wang_DiffPerformer_Iterative_Learning_of_Consistent_Latent_Guidance_for_Diffusion-based_Human_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Wang_DiffPerformer_Iterative_Learning_of_Consistent_Latent_Guidance_for_Diffusion-based_Human_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Wang_DiffPerformer_Iterative_Learning_of_Consistent_Latent_Guidance_for_Diffusion-based_Human_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Wang_DiffPerformer_Iterative_Learning_CVPR_2024_supplemental.pdf | null |
Unified Language-driven Zero-shot Domain Adaptation | Senqiao Yang, Zhuotao Tian, Li Jiang, Jiaya Jia | This paper introduces Unified Language-driven Zero-shot Domain Adaptation (ULDA) a novel task setting that enables a single model to adapt to diverse target domains without explicit domain-ID knowledge. We identify the constraints in the existing language-driven zero-shot domain adaptation task particularly the requirement for domain IDs and domain-specific models which may restrict flexibility and scalability. To overcome these issues we propose a new framework for ULDA consisting of Hierarchical Context Alignment (HCA) Domain Consistent Representation Learning (DCRL) and Text-Driven Rectifier (TDR). These components work synergistically to align simulated features with target text across multiple visual levels retain semantic correlations between different regional representations and rectify biases between simulated and real target visual features respectively. Our extensive empirical evaluations demonstrate that this framework achieves competitive performance in both settings surpassing even the model that requires domain-ID showcasing its superiority and generalization ability. The proposed method is not only effective but also maintains practicality and efficiency as it does not introduce additional computational costs during inference. The code is available on the project website. | https://openaccess.thecvf.com/content/CVPR2024/papers/Yang_Unified_Language-driven_Zero-shot_Domain_Adaptation_CVPR_2024_paper.pdf | http://arxiv.org/abs/2404.07155 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Yang_Unified_Language-driven_Zero-shot_Domain_Adaptation_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Yang_Unified_Language-driven_Zero-shot_Domain_Adaptation_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Yang_Unified_Language-driven_Zero-shot_CVPR_2024_supplemental.pdf | null |
Category-Level Multi-Part Multi-Joint 3D Shape Assembly | Yichen Li, Kaichun Mo, Yueqi Duan, He Wang, Jiequan Zhang, Lin Shao | Shape assembly composes complex shapes geometries by arranging simple part geometries and has wide applications in autonomous robotic assembly and CAD modeling. Existing works focus on geometry reasoning and neglect the actual physical assembly process of matching and fitting joints which are the contact surfaces connecting different parts. In this paper we consider contacting joints for the task of multi-part assembly. A successful joint-optimized assembly needs to satisfy the bilateral objectives of shape structure and joint alignment. We propose a hierarchical graph learning approach composed of two levels of graph representation learning. The part graph takes part geometries as input to build the desired shape structure. The joint-level graph uses part joints information and focuses on matching and aligning joints. The two kinds of information are combined to achieve the bilateral objectives. Extensive experiments demonstrate that our method outperforms previous methods achieving better shape structure and higher joint alignment accuracy. | https://openaccess.thecvf.com/content/CVPR2024/papers/Li_Category-Level_Multi-Part_Multi-Joint_3D_Shape_Assembly_CVPR_2024_paper.pdf | http://arxiv.org/abs/2303.06163 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Li_Category-Level_Multi-Part_Multi-Joint_3D_Shape_Assembly_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Li_Category-Level_Multi-Part_Multi-Joint_3D_Shape_Assembly_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Li_Category-Level_Multi-Part_Multi-Joint_CVPR_2024_supplemental.pdf | null |
Equivariant Multi-Modality Image Fusion | Zixiang Zhao, Haowen Bai, Jiangshe Zhang, Yulun Zhang, Kai Zhang, Shuang Xu, Dongdong Chen, Radu Timofte, Luc Van Gool | Multi-modality image fusion is a technique that combines information from different sensors or modalities enabling the fused image to retain complementary features from each modality such as functional highlights and texture details. However effective training of such fusion models is challenging due to the scarcity of ground truth fusion data. To tackle this issue we propose the Equivariant Multi-Modality imAge fusion (EMMA) paradigm for end-to-end self-supervised learning. Our approach is rooted in the prior knowledge that natural imaging responses are equivariant to certain transformations. Consequently we introduce a novel training paradigm that encompasses a fusion module a pseudo-sensing module and an equivariant fusion module. These components enable the net training to follow the principles of the natural sensing-imaging process while satisfying the equivariant imaging prior. Extensive experiments confirm that EMMA yields high-quality fusion results for infrared-visible and medical images concurrently facilitating downstream multi-modal segmentation and detection tasks. The code is available at https://github.com/Zhaozixiang1228/MMIF-EMMA. | https://openaccess.thecvf.com/content/CVPR2024/papers/Zhao_Equivariant_Multi-Modality_Image_Fusion_CVPR_2024_paper.pdf | http://arxiv.org/abs/2305.11443 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Zhao_Equivariant_Multi-Modality_Image_Fusion_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Zhao_Equivariant_Multi-Modality_Image_Fusion_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Zhao_Equivariant_Multi-Modality_Image_CVPR_2024_supplemental.pdf | null |
NeLF-Pro: Neural Light Field Probes for Multi-Scale Novel View Synthesis | Zinuo You, Andreas Geiger, Anpei Chen | We present NeLF-Pro a novel representation to model and reconstruct light fields in diverse natural scenes that vary in extent and spatial granularity. In contrast to previous fast reconstruction methods that represent the 3D scene globally we model the light field of a scene as a set of local light field feature probes parameterized with position and multi-channel 2D feature maps. Our central idea is to bake the scene's light field into spatially varying learnable representations and to query point features by weighted blending of probes close to the camera - allowing for mipmap representation and rendering. We introduce a novel vector-matrix-matrix (VMM) factorization technique that effectively represents the light field feature probes as products of core factors (i.e. VM) shared among local feature probes and a basis factor (i.e. M) - efficiently encoding internal relationships and patterns within the scene.Experimentally we demonstrate that NeLF-Pro significantly boosts the performance of feature grid-based representations and achieves fast reconstruction with better rendering quality while maintaining compact modeling. Project page: sinoyou.github.io/nelf-pro | https://openaccess.thecvf.com/content/CVPR2024/papers/You_NeLF-Pro_Neural_Light_Field_Probes_for_Multi-Scale_Novel_View_Synthesis_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/You_NeLF-Pro_Neural_Light_Field_Probes_for_Multi-Scale_Novel_View_Synthesis_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/You_NeLF-Pro_Neural_Light_Field_Probes_for_Multi-Scale_Novel_View_Synthesis_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/You_NeLF-Pro_Neural_Light_CVPR_2024_supplemental.pdf | null |
One-Shot Open Affordance Learning with Foundation Models | Gen Li, Deqing Sun, Laura Sevilla-Lara, Varun Jampani | We introduce One-shot Open Affordance Learning (OOAL) where a model is trained with just one example per base object category but is expected to identify novel objects and affordances. While vision-language models excel at recognizing novel objects and scenes they often struggle to understand finer levels of granularity such as affordances. To handle this issue we conduct a comprehensive analysis of existing foundation models to explore their inherent understanding of affordances and assess the potential for data-limited affordance learning. We then propose a vision-language framework with simple and effective designs that boost the alignment between visual features and affordance text embeddings. Experiments on two affordance segmentation benchmarks show that the proposed method outperforms state-of-the-art models with less than 1% of the full training data and exhibits reasonable generalization capability on unseen objects and affordances. Project page: https://reagan1311.github.io/ooal. | https://openaccess.thecvf.com/content/CVPR2024/papers/Li_One-Shot_Open_Affordance_Learning_with_Foundation_Models_CVPR_2024_paper.pdf | http://arxiv.org/abs/2311.17776 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Li_One-Shot_Open_Affordance_Learning_with_Foundation_Models_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Li_One-Shot_Open_Affordance_Learning_with_Foundation_Models_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Li_One-Shot_Open_Affordance_CVPR_2024_supplemental.pdf | null |
Don't Look into the Dark: Latent Codes for Pluralistic Image Inpainting | Haiwei Chen, Yajie Zhao | We present a method for large-mask pluralistic image inpainting based on the generative framework of discrete latent codes. Our method learns latent priors discretized as tokens by only performing computations at the visible locations of the image. This is realized by a restrictive partial encoder that predicts the token label for each visible block a bidirectional transformer that infers the missing labels by only looking at these tokens and a dedicated synthesis network that couples the tokens with the partial image priors to generate coherent and pluralistic complete image even under extreme mask settings. Experiments on public benchmarks validate our design choices as the proposed method outperforms strong baselines in both visual quality and diversity metrics. | https://openaccess.thecvf.com/content/CVPR2024/papers/Chen_Dont_Look_into_the_Dark_Latent_Codes_for_Pluralistic_Image_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Chen_Dont_Look_into_the_Dark_Latent_Codes_for_Pluralistic_Image_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Chen_Dont_Look_into_the_Dark_Latent_Codes_for_Pluralistic_Image_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Chen_Dont_Look_into_CVPR_2024_supplemental.pdf | null |
Incremental Nuclei Segmentation from Histopathological Images via Future-class Awareness and Compatibility-inspired Distillation | Huyong Wang, Huisi Wu, Jing Qin | We present a novel semantic segmentation approach for incremental nuclei segmentation from histopathological images which is a very challenging task as we have to incrementally optimize existing models to make them perform well in both old and new classes without using training samples of old classes. Yet it is an indispensable component of computer-aided diagnosis systems. The proposed approach has two key techniques. First we propose a new future-class awareness mechanism by separating some potential regions for future classes from background based on their similarities to both old and new classes in the representation space. With this mechanism we can not only reserve more parameter space for future updates but also enhance the representation capability of learned features. We further propose an innovative compatibility-inspired distillation scheme to make our model take full advantage of the knowledge learned by the old model. We conducted extensive experiments on two famous histopathological datasets and the results demonstrate the proposed approach achieves much better performance than state-of-the-art approaches. The code is available at https://github.com/why19991/InSeg. | https://openaccess.thecvf.com/content/CVPR2024/papers/Wang_Incremental_Nuclei_Segmentation_from_Histopathological_Images_via_Future-class_Awareness_and_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Wang_Incremental_Nuclei_Segmentation_from_Histopathological_Images_via_Future-class_Awareness_and_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Wang_Incremental_Nuclei_Segmentation_from_Histopathological_Images_via_Future-class_Awareness_and_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Wang_Incremental_Nuclei_Segmentation_CVPR_2024_supplemental.pdf | null |
DiffEditor: Boosting Accuracy and Flexibility on Diffusion-based Image Editing | Chong Mou, Xintao Wang, Jiechong Song, Ying Shan, Jian Zhang | Large-scale Text-to-Image (T2I) diffusion models have revolutionized image generation over the last few years. Although owning diverse and high-quality generation capabilities translating these abilities to fine-grained image editing remains challenging. In this paper we propose DiffEditor to rectify two weaknesses in existing diffusion-based image editing: (1) in complex scenarios editing results often lack editing accuracy and exhibit unexpected artifacts; (2) lack of flexibility to harmonize editing operations e.g. imagine new content. In our solution we introduce image prompts in fine-grained image editing cooperating with the text prompt to better describe the editing content. To increase the flexibility while maintaining content consistency we locally combine stochastic differential equation (SDE) into the ordinary differential equation (ODE) sampling. In addition we incorporate regional score-based gradient guidance and a time travel strategy into the diffusion sampling further improving the editing quality. Extensive experiments demonstrate that our method can efficiently achieve state-of-the-art performance on various fine-grained image editing tasks including editing within a single image (e.g. object moving resizing and content dragging) and across images (e.g. appearance replacing and object pasting). Our source code is released at https://github.com/MC-E/DragonDiffusion. | https://openaccess.thecvf.com/content/CVPR2024/papers/Mou_DiffEditor_Boosting_Accuracy_and_Flexibility_on_Diffusion-based_Image_Editing_CVPR_2024_paper.pdf | http://arxiv.org/abs/2402.02583 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Mou_DiffEditor_Boosting_Accuracy_and_Flexibility_on_Diffusion-based_Image_Editing_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Mou_DiffEditor_Boosting_Accuracy_and_Flexibility_on_Diffusion-based_Image_Editing_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Mou_DiffEditor_Boosting_Accuracy_CVPR_2024_supplemental.pdf | null |
Solving Masked Jigsaw Puzzles with Diffusion Vision Transformers | Jinyang Liu, Wondmgezahu Teshome, Sandesh Ghimire, Mario Sznaier, Octavia Camps | Solving image and video jigsaw puzzles poses the challenging task of rearranging image fragments or video frames from unordered sequences to restore meaningful images and video sequences. Existing approaches often hinge on discriminative models tasked with predicting either the absolute positions of puzzle elements or the permutation actions applied to the original data. Unfortunately these methods face limitations in effectively solving puzzles with a large number of elements. In this paper we propose JPDVT an innovative approach that harnesses diffusion transformers to address this challenge. Specifically we generate positional information for image patches or video frames conditioned on their underlying visual content. This information is then employed to accurately assemble the puzzle pieces in their correct positions even in scenarios involving missing pieces. Our method achieves state-of-the-art performance on several datasets. | https://openaccess.thecvf.com/content/CVPR2024/papers/Liu_Solving_Masked_Jigsaw_Puzzles_with_Diffusion_Vision_Transformers_CVPR_2024_paper.pdf | http://arxiv.org/abs/2404.07292 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Liu_Solving_Masked_Jigsaw_Puzzles_with_Diffusion_Vision_Transformers_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Liu_Solving_Masked_Jigsaw_Puzzles_with_Diffusion_Vision_Transformers_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Liu_Solving_Masked_Jigsaw_CVPR_2024_supplemental.pdf | null |
InstructVideo: Instructing Video Diffusion Models with Human Feedback | Hangjie Yuan, Shiwei Zhang, Xiang Wang, Yujie Wei, Tao Feng, Yining Pan, Yingya Zhang, Ziwei Liu, Samuel Albanie, Dong Ni | Diffusion models have emerged as the de facto paradigm for video generation. However their reliance on web-scale data of varied quality often yields results that are visually unappealing and misaligned with the textual prompts. To tackle this problem we propose InstructVideo to instruct text-to-video diffusion models with human feedback by reward fine-tuning. InstructVideo has two key ingredients: 1) To ameliorate the cost of reward fine-tuning induced by generating through the full DDIM sampling chain we recast reward fine-tuning as editing. By leveraging the diffusion process to corrupt a sampled video InstructVideo requires only partial inference of the DDIM sampling chain reducing fine-tuning cost while improving fine-tuning efficiency. 2) To mitigate the absence of a dedicated video reward model for human preferences we repurpose established image reward models e.g. HPSv2. To this end we propose Segmental Video Reward a mechanism to provide reward signals based on segmental sparse sampling and Temporally Attenuated Reward a method that mitigates temporal modeling degradation during fine-tuning. Extensive experiments both qualitative and quantitative validate the practicality and efficacy of using image reward models in InstructVideo significantly enhancing the visual quality of generated videos without compromising generalization capabilities. Code and models can be accessed through our project page https://instructvideo.github.io/. | https://openaccess.thecvf.com/content/CVPR2024/papers/Yuan_InstructVideo_Instructing_Video_Diffusion_Models_with_Human_Feedback_CVPR_2024_paper.pdf | http://arxiv.org/abs/2312.12490 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Yuan_InstructVideo_Instructing_Video_Diffusion_Models_with_Human_Feedback_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Yuan_InstructVideo_Instructing_Video_Diffusion_Models_with_Human_Feedback_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Yuan_InstructVideo_Instructing_Video_CVPR_2024_supplemental.pdf | null |
Fully Exploiting Every Real Sample: SuperPixel Sample Gradient Model Stealing | Yunlong Zhao, Xiaoheng Deng, Yijing Liu, Xinjun Pei, Jiazhi Xia, Wei Chen | Model stealing (MS) involves querying and observing the output of a machine learning model to steal its capabilities. The quality of queried data is crucial yet obtaining a large amount of real data for MS is often challenging. Recent works have reduced reliance on real data by using generative models. However when high-dimensional query data is required these methods are impractical due to the high costs of querying and the risk of model collapse. In this work we propose using sample gradients (SG) to enhance the utility of each real sample as SG provides crucial guidance on the decision boundaries of the victim model. However utilizing SG in the model stealing scenario faces two challenges: 1. Pixel-level gradient estimation requires extensive query volume and is susceptible to defenses. 2. The estimation of sample gradients has a significant variance. This paper proposes Superpixel Sample Gradient stealing (SPSG) for model stealing under the constraint of limited real samples. With the basic idea of imitating the victim model's low-variance patch-level gradients instead of pixel-level gradients SPSG achieves efficient sample gradient estimation through two steps. First we perform patch-wise perturbations on query images to estimate the average gradient in different regions of the image. Then we filter the gradients through a threshold strategy to reduce variance. Exhaustive experiments demonstrate that with the same number of real samples SPSG achieves accuracy agreements and adversarial success rate significantly surpassing the current state-of-the-art MS methods. Codes are available at https://github.com/zyl123456aB/SPSG_attack. | https://openaccess.thecvf.com/content/CVPR2024/papers/Zhao_Fully_Exploiting_Every_Real_Sample_SuperPixel_Sample_Gradient_Model_Stealing_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Zhao_Fully_Exploiting_Every_Real_Sample_SuperPixel_Sample_Gradient_Model_Stealing_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Zhao_Fully_Exploiting_Every_Real_Sample_SuperPixel_Sample_Gradient_Model_Stealing_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Zhao_Fully_Exploiting_Every_CVPR_2024_supplemental.pdf | null |
Progressive Divide-and-Conquer via Subsampling Decomposition for Accelerated MRI | Chong Wang, Lanqing Guo, Yufei Wang, Hao Cheng, Yi Yu, Bihan Wen | Deep unfolding networks (DUN) have emerged as a popular iterative framework for accelerated magnetic resonance imaging (MRI) reconstruction. However conventional DUN aims to reconstruct all the missing information within the entire space in each iteration. Thus it could be challenging when dealing with highly ill-posed degradation often resulting in subpar reconstruction. In this work we propose a Progressive Divide-And-Conquer (PDAC) strategy aiming to break down the subsampling process in the actual severe degradation and thus perform reconstruction sequentially. Starting from decomposing the original maximum-a-posteriori problem of accelerated MRI we present a rigorous derivation of the proposed PDAC framework which could be further unfolded into an end-to-end trainable network. Each PDAC iteration specifically targets a distinct segment of moderate degradation based on the decomposition. Furthermore as part of the PDAC iteration such decomposition is adaptively learned as an auxiliary task through a degradation predictor which provides an estimation of the decomposed sampling mask. Following this prediction the sampling mask is further integrated via a severity conditioning module to ensure awareness of the degradation severity at each stage. Extensive experiments demonstrate that our proposed method achieves superior performance on the publicly available fastMRI and Stanford2D FSE datasets in both multi-coil and single-coil settings. | https://openaccess.thecvf.com/content/CVPR2024/papers/Wang_Progressive_Divide-and-Conquer_via_Subsampling_Decomposition_for_Accelerated_MRI_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.10064 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Wang_Progressive_Divide-and-Conquer_via_Subsampling_Decomposition_for_Accelerated_MRI_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Wang_Progressive_Divide-and-Conquer_via_Subsampling_Decomposition_for_Accelerated_MRI_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Wang_Progressive_Divide-and-Conquer_via_CVPR_2024_supplemental.pdf | null |
DiffMOT: A Real-time Diffusion-based Multiple Object Tracker with Non-linear Prediction | Weiyi Lv, Yuhang Huang, Ning Zhang, Ruei-Sung Lin, Mei Han, Dan Zeng | In Multiple Object Tracking objects often exhibit non-linear motion of acceleration and deceleration with irregular direction changes. Tacking-by-detection (TBD) trackers with Kalman Filter motion prediction work well in pedestrian-dominant scenarios but fall short in complex situations when multiple objects perform non-linear and diverse motion simultaneously. To tackle the complex non-linear motion we propose a real-time diffusion-based MOT approach named DiffMOT. Specifically for the motion predictor component we propose a novel Decoupled Diffusion-based Motion Predictor (D^2MP). It models the entire distribution of various motion presented by the data as a whole. It also predicts an individual object's motion conditioning on an individual's historical motion information. Furthermore it optimizes the diffusion process with much fewer sampling steps. As a MOT tracker the DiffMOT is real-time at 22.7FPS and also outperforms the state-of-the-art on DanceTrack and SportsMOT datasets with 62.3% and 76.2% in HOTA metrics respectively. To the best of our knowledge DiffMOT is the first to introduce a diffusion probabilistic model into the MOT to tackle non-linear motion prediction. | https://openaccess.thecvf.com/content/CVPR2024/papers/Lv_DiffMOT_A_Real-time_Diffusion-based_Multiple_Object_Tracker_with_Non-linear_Prediction_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.02075 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Lv_DiffMOT_A_Real-time_Diffusion-based_Multiple_Object_Tracker_with_Non-linear_Prediction_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Lv_DiffMOT_A_Real-time_Diffusion-based_Multiple_Object_Tracker_with_Non-linear_Prediction_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Lv_DiffMOT_A_Real-time_CVPR_2024_supplemental.pdf | null |
MV-Adapter: Multimodal Video Transfer Learning for Video Text Retrieval | Xiaojie Jin, Bowen Zhang, Weibo Gong, Kai Xu, Xueqing Deng, Peng Wang, Zhao Zhang, Xiaohui Shen, Jiashi Feng | State-of-the-art video-text retrieval (VTR) methods typically involve fully fine-tuning a pre-trained model (e.g. CLIP) on specific datasets. However this can result in significant storage costs in practical applications as a separate model per task must be stored. To address this issue we present our pioneering work that enables parameter-efficient VTR using a pre-trained model with only a small number of tunable parameters during training. Towards this goal we propose a new method dubbed Multimodal Video Adapter (MV-Adapter) for efficiently transferring the knowledge in the pre-trained CLIP from image-text to video-text. Specifically MV-Adapter utilizes bottleneck structures in both video and text branches along with two novel components. The first is a Temporal Adaptation Module that is incorporated in the video branch to introduce global and local temporal contexts. We also train weights calibrations to adjust to dynamic variations across frames. The second is Cross Modality Tying that generates weights for video/text branches through sharing cross modality factors for better aligning between modalities. Thanks to above innovations MV-Adapter can achieve comparable or better performance than standard fine-tuning with negligible parameters overhead. Notably MV-Adapter consistently outperforms various competing methods in V2T/T2V tasks with large margins on five widely used VTR benchmarks (MSR-VTT MSVD LSMDC DiDemo and ActivityNet). Codes will be released. | https://openaccess.thecvf.com/content/CVPR2024/papers/Jin_MV-Adapter_Multimodal_Video_Transfer_Learning_for_Video_Text_Retrieval_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Jin_MV-Adapter_Multimodal_Video_Transfer_Learning_for_Video_Text_Retrieval_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Jin_MV-Adapter_Multimodal_Video_Transfer_Learning_for_Video_Text_Retrieval_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Jin_MV-Adapter_Multimodal_Video_CVPR_2024_supplemental.zip | null |
Rethinking Multi-view Representation Learning via Distilled Disentangling | Guanzhou Ke, Bo Wang, Xiaoli Wang, Shengfeng He | Multi-view representation learning aims to derive robust representations that are both view-consistent and view-specific from diverse data sources. This paper presents an in-depth analysis of existing approaches in this domain highlighting a commonly overlooked aspect: the redundancy between view-consistent and view-specific representations. To this end we propose an innovative framework for multi-view representation learning which incorporates a technique we term 'distilled disentangling'. Our method introduces the concept of masked cross-view prediction enabling the extraction of compact high-quality view-consistent representations from various sources without incurring extra computational overhead. Additionally we develop a distilled disentangling module that efficiently filters out consistency-related information from multi-view representations resulting in purer view-specific representations. This approach significantly reduces redundancy between view-consistent and view-specific representations enhancing the overall efficiency of the learning process. Our empirical evaluations reveal that higher mask ratios substantially improve the quality of view-consistent representations. Moreover we find that reducing the dimensionality of view-consistent representations relative to that of view-specific representations further refines the quality of the combined representations. | https://openaccess.thecvf.com/content/CVPR2024/papers/Ke_Rethinking_Multi-view_Representation_Learning_via_Distilled_Disentangling_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.10897 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Ke_Rethinking_Multi-view_Representation_Learning_via_Distilled_Disentangling_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Ke_Rethinking_Multi-view_Representation_Learning_via_Distilled_Disentangling_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Ke_Rethinking_Multi-view_Representation_CVPR_2024_supplemental.pdf | null |
Just Add ?! Pose Induced Video Transformers for Understanding Activities of Daily Living | Dominick Reilly, Srijan Das | Video transformers have become the de facto standard for human action recognition yet their exclusive reliance on the RGB modality still limits their adoption in certain domains. One such domain is Activities of Daily Living (ADL) where RGB alone is not sufficient to distinguish between visually similar actions or actions observed from multiple viewpoints. To facilitate the adoption of video transformers for ADL we hypothesize that the augmentation of RGB with human pose information known for its sensitivity to fine-grained motion and multiple viewpoints is essential. Consequently we introduce the first Pose Induced Video Transformer: PI-ViT (or π-ViT) a novel approach that augments the RGB representations learned by video transformers with 2D and 3D pose information. The key elements of π-ViT are two plug-in modules 2D Skeleton Induction Module and 3D Skeleton Induction Module that are responsible for inducing 2D and 3D pose information into the RGB representations. These modules operate by performing pose-aware auxiliary tasks a design choice that allows π-ViT to discard the modules during inference. Notably π-ViT achieves the state-of-the-art performance on three prominent ADL datasets encompassing both real-world and large-scale RGB-D datasets without requiring poses or additional computational overhead at inference. | https://openaccess.thecvf.com/content/CVPR2024/papers/Reilly_Just_Add__Pose_Induced_Video_Transformers_for_Understanding_Activities_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Reilly_Just_Add__Pose_Induced_Video_Transformers_for_Understanding_Activities_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Reilly_Just_Add__Pose_Induced_Video_Transformers_for_Understanding_Activities_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Reilly_Just_Add__CVPR_2024_supplemental.pdf | null |
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