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SelfPose3d: Self-Supervised Multi-Person Multi-View 3d Pose Estimation | Vinkle Srivastav, Keqi Chen, Nicolas Padoy | We present a new self-supervised approach SelfPose3d for estimating 3d poses of multiple persons from multiple camera views. Unlike current state-of-the-art fully-supervised methods our approach does not require any 2d or 3d ground-truth poses and uses only the multi-view input images from a calibrated camera setup and 2d pseudo poses generated from an off-the-shelf 2d human pose estimator. We propose two self-supervised learning objectives: self-supervised person localization in 3d space and self-supervised 3d pose estimation. We achieve self-supervised 3d person localization by training the model on synthetically generated 3d points serving as 3d person root positions and on the projected root-heatmaps in all the views. We then model the 3d poses of all the localized persons with a bottleneck representation map them onto all views obtaining 2d joints and render them using 2d Gaussian heatmaps in an end-to-end differentiable manner. Afterwards we use the corresponding 2d joints and heatmaps from the pseudo 2d poses for learning. To alleviate the intrinsic inaccuracy of the pseudo labels we propose an adaptive supervision attention mechanism to guide the self-supervision. Our experiments and analysis on three public benchmark datasets including Panoptic Shelf and Campus show the effectiveness of our approach which is comparable to fully-supervised methods. Code is available at https://github.com/CAMMA-public/SelfPose3D. | https://openaccess.thecvf.com/content/CVPR2024/papers/Srivastav_SelfPose3d_Self-Supervised_Multi-Person_Multi-View_3d_Pose_Estimation_CVPR_2024_paper.pdf | http://arxiv.org/abs/2404.02041 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Srivastav_SelfPose3d_Self-Supervised_Multi-Person_Multi-View_3d_Pose_Estimation_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Srivastav_SelfPose3d_Self-Supervised_Multi-Person_Multi-View_3d_Pose_Estimation_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Srivastav_SelfPose3d_Self-Supervised_Multi-Person_CVPR_2024_supplemental.pdf | null |
MoDE: CLIP Data Experts via Clustering | Jiawei Ma, Po-Yao Huang, Saining Xie, Shang-Wen Li, Luke Zettlemoyer, Shih-Fu Chang, Wen-Tau Yih, Hu Xu | The success of contrastive language-image pretraining (CLIP) relies on the supervision from the pairing between images and captions which tends to be noisy in web-crawled data. We present Mixture of Data Experts (MoDE) and learn a system of CLIP data experts via clustering. Each data expert is trained on one data cluster being less sensitive to false negative noises in other clusters. At inference time we ensemble their outputs by applying weights determined through the correlation between task metadata and cluster conditions. To estimate the correlation precisely the samples in one cluster should be semantically similar but the number of data experts should still be reasonable for training and inference. As such we consider the ontology in human language and propose to use fine-grained cluster centers to represent each data expert at a coarse-grained level. Experimental studies show that four CLIP data experts on ViT-B/16 outperform the ViT-L/14 by OpenAI CLIP and OpenCLIP on zero-shot image classification but with less (<35%) training cost. Meanwhile MoDE can train all data expert asynchronously and can flexibly include new data experts. The code is available here. | https://openaccess.thecvf.com/content/CVPR2024/papers/Ma_MoDE_CLIP_Data_Experts_via_Clustering_CVPR_2024_paper.pdf | http://arxiv.org/abs/2404.16030 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Ma_MoDE_CLIP_Data_Experts_via_Clustering_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Ma_MoDE_CLIP_Data_Experts_via_Clustering_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Ma_MoDE_CLIP_Data_CVPR_2024_supplemental.pdf | null |
Joint2Human: High-Quality 3D Human Generation via Compact Spherical Embedding of 3D Joints | Muxin Zhang, Qiao Feng, Zhuo Su, Chao Wen, Zhou Xue, Kun Li | 3D human generation is increasingly significant in various applications. However the direct use of 2D generative methods in 3D generation often results in losing local details while methods that reconstruct geometry from generated images struggle with global view consistency. In this work we introduce Joint2Human a novel method that leverages 2D diffusion models to generate detailed 3D human geometry directly ensuring both global structure and local details. To achieve this we employ the Fourier occupancy field (FOF) representation enabling the direct generation of 3D shapes as preliminary results with 2D generative models. With the proposed high-frequency enhancer and the multi-view recarving strategy our method can seamlessly integrate the details from different views into a uniform global shape. To better utilize the 3D human prior and enhance control over the generated geometry we introduce a compact spherical embedding of 3D joints. This allows for an effective guidance of pose during the generation process. Additionally our method can generate 3D humans guided by textual inputs. Our experimental results demonstrate the capability of our method to ensure global structure local details high resolution and low computational cost simultaneously. More results and the code can be found on our project page at http://cic.tju.edu.cn/faculty/likun/projects/Joint2Human. | https://openaccess.thecvf.com/content/CVPR2024/papers/Zhang_Joint2Human_High-Quality_3D_Human_Generation_via_Compact_Spherical_Embedding_of_CVPR_2024_paper.pdf | http://arxiv.org/abs/2312.08591 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Zhang_Joint2Human_High-Quality_3D_Human_Generation_via_Compact_Spherical_Embedding_of_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Zhang_Joint2Human_High-Quality_3D_Human_Generation_via_Compact_Spherical_Embedding_of_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Zhang_Joint2Human_High-Quality_3D_CVPR_2024_supplemental.pdf | null |
Prompt-Free Diffusion: Taking "Text" out of Text-to-Image Diffusion Models | Xingqian Xu, Jiayi Guo, Zhangyang Wang, Gao Huang, Irfan Essa, Humphrey Shi | Text-to-image (T2I) research has grown explosively in the past year owing to the large-scale pre-trained diffusion models and many emerging personalization and editing approaches. Yet one pain point persists: the text prompt engineering and searching high-quality text prompts for customized results is more art than science. Moreover as commonly argued: "an image is worth a thousand words" - the attempt to describe a desired image with texts often ends up being ambiguous and cannot comprehensively cover delicate visual details hence necessitating more additional controls from the visual domain. In this paper we take a bold step forward: taking "Text" out of a pretrained T2I diffusion model to reduce the burdensome prompt engineering efforts for users. Our proposed framework Prompt-Free Diffusion relies on only visual inputs to generate new images: it takes a reference image as "context" an optional image structural conditioning and an initial noise with absolutely no text prompt. The core architecture behind the scene is Semantic Context Encoder (SeeCoder) substituting the commonly used CLIP-based or LLM-based text encoder. The reusability of SeeCoder also makes it a convenient drop-in component: one can also pre-train a SeeCoder in one T2I model and reuse it for another. Through extensive experiments Prompt-Free Diffusion is experimentally found to (i) outperform prior exemplar-based image synthesis approaches; (ii) perform on par with state-of-the-art T2I models using prompts following the best practice; and (iii) be naturally extensible to other downstream applications such as anime figure generation and virtual try-on with promising quality. Our code and models will be open-sourced. | https://openaccess.thecvf.com/content/CVPR2024/papers/Xu_Prompt-Free_Diffusion_Taking_Text_out_of_Text-to-Image_Diffusion_Models_CVPR_2024_paper.pdf | http://arxiv.org/abs/2305.16223 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Xu_Prompt-Free_Diffusion_Taking_Text_out_of_Text-to-Image_Diffusion_Models_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Xu_Prompt-Free_Diffusion_Taking_Text_out_of_Text-to-Image_Diffusion_Models_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Xu_Prompt-Free_Diffusion_Taking_CVPR_2024_supplemental.pdf | null |
MPOD123: One Image to 3D Content Generation Using Mask-enhanced Progressive Outline-to-Detail Optimization | Jimin Xu, Tianbao Wang, Tao Jin, Shengyu Zhang, Dongjie Fu, Zhe Wang, Jiangjing Lyu, Chengfei Lv, Chaoyue Niu, Zhou Yu, Zhou Zhao, Fei Wu | Recent advancements in single image driven 3D content generation have been propelled by leveraging prior knowledge from pretrained 2D diffusion models. However the 3D content generated by existing methods often exhibits distorted outline shapes and inadequate details. To solve this problem we propose a novel framework called Mask-enhanced Progressive Outline-to-Detail optimization (aka. MPOD123) which consists of two stages. Specifically in the first stage MPOD123 utilizes the pretrained view-conditioned diffusion model to guide the outline shape optimization of the 3D content. Given certain viewpoint we estimate outline shape priors in the form of 2D mask from the 3D content by leveraging opacity calculation. In the second stage MPOD123 incorporates Detail Appearance Inpainting (DAI) to guide the refinement on local geometry and texture with the shape priors. The essence of DAI lies in the Mask Rectified Cross-Attention (MRCA) which can be conveniently plugged in the stable diffusion model. The MRCA module utilizes the mask to rectify the attention map from each cross-attention layer. Accompanied with this new module DAI is capable of guiding the detail refinement of the 3D content while better preserves the outline shape. To assess the applicability in practical scenarios we contribute a new dataset modeled on real-world e-commerce environments. Extensive quantitative and qualitative experiments on this dataset and open benchmarks demonstrate the effectiveness of MPOD123 over the state-of-the-arts. | https://openaccess.thecvf.com/content/CVPR2024/papers/Xu_MPOD123_One_Image_to_3D_Content_Generation_Using_Mask-enhanced_Progressive_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Xu_MPOD123_One_Image_to_3D_Content_Generation_Using_Mask-enhanced_Progressive_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Xu_MPOD123_One_Image_to_3D_Content_Generation_Using_Mask-enhanced_Progressive_CVPR_2024_paper.html | CVPR 2024 | null | null |
Multi-agent Long-term 3D Human Pose Forecasting via Interaction-aware Trajectory Conditioning | Jaewoo Jeong, Daehee Park, Kuk-Jin Yoon | Human pose forecasting garners attention for its diverse applications. However challenges in modeling the multi-modal nature of human motion and intricate interactions among agents persist particularly with longer timescales and more agents. In this paper we propose an interaction-aware trajectory-conditioned long-term multi-agent human pose forecasting model utilizing a coarse-to-fine prediction approach: multi-modal global trajectories are initially forecasted followed by respective local pose forecasts conditioned on each mode. In doing so our Trajectory2Pose model introduces a graph-based agent-wise interaction module for a reciprocal forecast of local motion-conditioned global trajectory and trajectory-conditioned local pose. Our model effectively handles the multi-modality of human motion and the complexity of long-term multi-agent interactions improving performance in complex environments. Furthermore we address the lack of long-term (6s+) multi-agent (5+) datasets by constructing a new dataset from real-world images and 2D annotations enabling a comprehensive evaluation of our proposed model. State-of-the-art prediction performance on both complex and simpler datasets confirms the generalized effectiveness of our method. The code is available at https://github.com/Jaewoo97/T2P. | https://openaccess.thecvf.com/content/CVPR2024/papers/Jeong_Multi-agent_Long-term_3D_Human_Pose_Forecasting_via_Interaction-aware_Trajectory_Conditioning_CVPR_2024_paper.pdf | http://arxiv.org/abs/2404.05218 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Jeong_Multi-agent_Long-term_3D_Human_Pose_Forecasting_via_Interaction-aware_Trajectory_Conditioning_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Jeong_Multi-agent_Long-term_3D_Human_Pose_Forecasting_via_Interaction-aware_Trajectory_Conditioning_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Jeong_Multi-agent_Long-term_3D_CVPR_2024_supplemental.zip | null |
UnionFormer: Unified-Learning Transformer with Multi-View Representation for Image Manipulation Detection and Localization | null | null | null | null | null | https://openaccess.thecvf.com/content/CVPR2024/html/Li_UnionFormer_Unified-Learning_Transformer_with_Multi-View_Representation_for_Image_Manipulation_Detection_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Li_UnionFormer_Unified-Learning_Transformer_with_Multi-View_Representation_for_Image_Manipulation_Detection_CVPR_2024_paper.html | CVPR 2024 | null | null |
Situational Awareness Matters in 3D Vision Language Reasoning | Yunze Man, Liang-Yan Gui, Yu-Xiong Wang | Being able to carry out complicated vision language reasoning tasks in 3D space represents a significant milestone in developing household robots and human-centered embodied AI. In this work we demonstrate that a critical and distinct challenge in 3D vision language reasoning is the situational awareness which incorporates two key components: (1) The autonomous agent grounds its self-location based on a language prompt. (2) The agent answers open-ended questions from the perspective of its calculated position. To address this challenge we introduce SIG3D an end-to-end Situation-Grounded model for 3D vision language reasoning. We tokenize the 3D scene into sparse voxel representation and propose a language-grounded situation estimator followed by a situated question answering module. Experiments on the SQA3D and ScanQA datasets show that SIG3D outperforms state-of-the-art models in situational estimation and question answering by a large margin (e.g. an enhancement of over 30% on situation accuracy). Subsequent analysis corroborates our architectural design choices explores the distinct functions of visual and textual tokens and highlights the importance of situational awareness in the domain of 3D question-answering. Project page is available at https://yunzeman.github.io/situation3d. | https://openaccess.thecvf.com/content/CVPR2024/papers/Man_Situational_Awareness_Matters_in_3D_Vision_Language_Reasoning_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Man_Situational_Awareness_Matters_in_3D_Vision_Language_Reasoning_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Man_Situational_Awareness_Matters_in_3D_Vision_Language_Reasoning_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Man_Situational_Awareness_Matters_CVPR_2024_supplemental.pdf | null |
RCBEVDet: Radar-camera Fusion in Bird's Eye View for 3D Object Detection | Zhiwei Lin, Zhe Liu, Zhongyu Xia, Xinhao Wang, Yongtao Wang, Shengxiang Qi, Yang Dong, Nan Dong, Le Zhang, Ce Zhu | Three-dimensional object detection is one of the key tasks in autonomous driving. To reduce costs in practice low-cost multi-view cameras for 3D object detection are proposed to replace the expansive LiDAR sensors. However relying solely on cameras is difficult to achieve highly accurate and robust 3D object detection. An effective solution to this issue is combining multi-view cameras with the economical millimeter-wave radar sensor to achieve more reliable multi-modal 3D object detection. In this paper we introduce RCBEVDet a radar-camera fusion 3D object detection method in the bird's eye view (BEV). Specifically we first design RadarBEVNet for radar BEV feature extraction. RadarBEVNet consists of a dual-stream radar backbone and a Radar Cross-Section (RCS) aware BEV encoder. In the dual-stream radar backbone a point-based encoder and a transformer-based encoder are proposed to extract radar features with an injection and extraction module to facilitate communication between the two encoders. The RCS-aware BEV encoder takes RCS as the object size prior to scattering the point feature in BEV. Besides we present the Cross-Attention Multi-layer Fusion module to automatically align the multi-modal BEV feature from radar and camera with the deformable attention mechanism and then fuse the feature with channel and spatial fusion layers. Experimental results show that RCBEVDet achieves new state-of-the-art radar-camera fusion results on nuScenes and view-of-delft (VoD) 3D object detection benchmarks. Furthermore RCBEVDet achieves better 3D detection results than all real-time camera-only and radar-camera 3D object detectors with a faster inference speed at 21 28 FPS. The source code will be released at https://github.com/VDIGPKU/RCBEVDet. | https://openaccess.thecvf.com/content/CVPR2024/papers/Lin_RCBEVDet_Radar-camera_Fusion_in_Birds_Eye_View_for_3D_Object_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Lin_RCBEVDet_Radar-camera_Fusion_in_Birds_Eye_View_for_3D_Object_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Lin_RCBEVDet_Radar-camera_Fusion_in_Birds_Eye_View_for_3D_Object_CVPR_2024_paper.html | CVPR 2024 | null | null |
CLOAF: CoLlisiOn-Aware Human Flow | Andrey Davydov, Martin Engilberge, Mathieu Salzmann, Pascal Fua | Even the best current algorithms for estimating body 3D shape and pose yield results that include body self-intersections. In this paper we present CLOAF which exploits the diffeomorphic nature of Ordinary Differential Equations to eliminate such self-intersections while still imposing body shape constraints. We show that unlike earlier approaches to addressing this issue ours completely eliminates the self-intersections without compromising the accuracy of the reconstructions. Being differentiable CLOAF can be used to fine-tune pose and shape estimation baselines to improve their overall performance and eliminate self-intersections in their predictions. Furthermore we demonstrate how our CLOAF strategy can be applied to practically any motion field induced by the user. CLOAF also makes it possible to edit motion to interact with the environment without worrying about potential collision or loss of body-shape prior. | https://openaccess.thecvf.com/content/CVPR2024/papers/Davydov_CLOAF_CoLlisiOn-Aware_Human_Flow_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.09050 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Davydov_CLOAF_CoLlisiOn-Aware_Human_Flow_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Davydov_CLOAF_CoLlisiOn-Aware_Human_Flow_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Davydov_CLOAF_CoLlisiOn-Aware_Human_CVPR_2024_supplemental.mp4 | null |
Hybrid Functional Maps for Crease-Aware Non-Isometric Shape Matching | Lennart Bastian, Yizheng Xie, Nassir Navab, Zorah Lähner | Non-isometric shape correspondence remains a fundamental challenge in computer vision. Traditional methods using Laplace-Beltrami operator (LBO) eigenmodes face limitations in characterizing high-frequency extrinsic shape changes like bending and creases. We propose a novel approach of combining the non-orthogonal extrinsic basis of eigenfunctions of the elastic thin-shell hessian with the intrinsic ones of the LBO creating a hybrid spectral space in which we construct functional maps. To this end we present a theoretical framework to effectively integrate non-orthogonal basis functions into descriptor- and learning-based functional map methods. Our approach can be incorporated easily into existing functional map pipelines across varying applications and is able to handle complex deformations beyond isometries. We show extensive evaluations across various supervised and unsupervised settings and demonstrate significant improvements. Notably our approach achieves up to 15% better mean geodesic error for non-isometric correspondence settings and up to 45% improvement in scenarios with topological noise. | https://openaccess.thecvf.com/content/CVPR2024/papers/Bastian_Hybrid_Functional_Maps_for_Crease-Aware_Non-Isometric_Shape_Matching_CVPR_2024_paper.pdf | http://arxiv.org/abs/2312.03678 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Bastian_Hybrid_Functional_Maps_for_Crease-Aware_Non-Isometric_Shape_Matching_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Bastian_Hybrid_Functional_Maps_for_Crease-Aware_Non-Isometric_Shape_Matching_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Bastian_Hybrid_Functional_Maps_CVPR_2024_supplemental.pdf | null |
Density-Guided Semi-Supervised 3D Semantic Segmentation with Dual-Space Hardness Sampling | Jianan Li, Qiulei Dong | Densely annotating the large-scale point clouds is laborious. To alleviate the annotation burden contrastive learning has attracted increasing attention for tackling semi-supervised 3D semantic segmentation. However existing point-to-point contrastive learning techniques in literature are generally sensitive to outliers resulting in insufficient modeling of the point-wise representations. To address this problem we propose a method named DDSemi for semi-supervised 3D semantic segmentation where a density-guided contrastive learning technique is explored. This technique calculates the contrastive loss in a point-to-anchor manner by estimating an anchor for each class from the memory bank based on the finding that the cluster centers tend to be located in dense regions. In this technique an inter-contrast loss is derived from the perturbed unlabeled point cloud pairs while an intra-contrast loss is derived from a single unlabeled point cloud. The derived losses could enhance the discriminability of the features and implicitly constrain the semantic consistency between the perturbed unlabeled point cloud pairs. In addition we propose a dual-space hardness sampling strategy to pay more attention to the hard samples located in sparse regions of both the geometric space and feature space by reweighting the point-wise intra-contrast loss. Experimental results on both indoor-scene and outdoor-scene datasets demonstrate that the proposed method outperforms the comparative state-of-the-art semi-supervised methods. | https://openaccess.thecvf.com/content/CVPR2024/papers/Li_Density-Guided_Semi-Supervised_3D_Semantic_Segmentation_with_Dual-Space_Hardness_Sampling_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Li_Density-Guided_Semi-Supervised_3D_Semantic_Segmentation_with_Dual-Space_Hardness_Sampling_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Li_Density-Guided_Semi-Supervised_3D_Semantic_Segmentation_with_Dual-Space_Hardness_Sampling_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Li_Density-Guided_Semi-Supervised_3D_CVPR_2024_supplemental.pdf | null |
Adaptive Softassign via Hadamard-Equipped Sinkhorn | Binrui Shen, Qiang Niu, Shengxin Zhu | Softassign is a pivotal method in graph matching and other learning tasks. Many softassign-based algorithms exhibit performance sensitivity to a parameter in the softassign. However tuning the parameter is challenging and almost done empirically. This paper proposes an adaptive softassign method for graph matching by analyzing the relationship between the objective score and the parameter. This method can automatically tune the parameter based on a given error bound to guarantee accuracy. The Hadamard-Equipped Sinkhorn formulas introduced in this study significantly enhance the efficiency and stability of the adaptive softassign. Moreover these formulas can also be used in optimal transport problems. The resulting adaptive softassign graph matching algorithm enjoys significantly higher accuracy than previous state-of-the-art large graph matching algorithms while maintaining comparable efficiency. | https://openaccess.thecvf.com/content/CVPR2024/papers/Shen_Adaptive_Softassign_via_Hadamard-Equipped_Sinkhorn_CVPR_2024_paper.pdf | http://arxiv.org/abs/2309.13855 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Shen_Adaptive_Softassign_via_Hadamard-Equipped_Sinkhorn_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Shen_Adaptive_Softassign_via_Hadamard-Equipped_Sinkhorn_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Shen_Adaptive_Softassign_via_CVPR_2024_supplemental.pdf | null |
Re-thinking Data Availability Attacks Against Deep Neural Networks | Bin Fang, Bo Li, Shuang Wu, Shouhong Ding, Ran Yi, Lizhuang Ma | The unauthorized use of personal data for commercial purposes and the covert acquisition of private data for training machine learning models continue to raise concerns. To address these issues researchers have proposed availability attacks that aim to render data unexploitable. However many availability attack methods can be easily disrupted by adversarial training. Although some robust methods can resist adversarial training their protective effects are limited. In this paper we re-examine the existing availability attack methods and propose a novel two-stage min-max-min optimization paradigm to generate robust unlearnable noise. The inner min stage is utilized to generate unlearnable noise while the outer min-max stage simulates the training process of the poisoned model. Additionally we formulate the attack effects and use it to constrain the optimization objective. Comprehensive experiments have revealed that the noise generated by our method can lead to a decline in test accuracy for adversarially trained poisoned models by up to approximately 30% in comparison to SOTA methods. | https://openaccess.thecvf.com/content/CVPR2024/papers/Fang_Re-thinking_Data_Availability_Attacks_Against_Deep_Neural_Networks_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Fang_Re-thinking_Data_Availability_Attacks_Against_Deep_Neural_Networks_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Fang_Re-thinking_Data_Availability_Attacks_Against_Deep_Neural_Networks_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Fang_Re-thinking_Data_Availability_CVPR_2024_supplemental.pdf | null |
ElasticDiffusion: Training-free Arbitrary Size Image Generation through Global-Local Content Separation | Moayed Haji-Ali, Guha Balakrishnan, Vicente Ordonez | Diffusion models have revolutionized image generation in recent years yet they are still limited to a few sizes and aspect ratios. We propose ElasticDiffusion a novel training-free decoding method that enables pretrained text-to-image diffusion models to generate images with various sizes. ElasticDiffusion attempts to decouple the generation trajectory of a pretrained model into local and global signals. The local signal controls low-level pixel information and can be estimated on local patches while the global signal is used to maintain overall structural consistency and is estimated with a reference image. We test our method on CelebA-HQ (faces) and LAION-COCO (objects/indoor/outdoor scenes). Our experiments and qualitative results show superior image coherence quality across aspect ratios compared to MultiDiffusion and the standard decoding strategy of Stable Diffusion. Project Webpage: https://elasticdiffusion.github.io | https://openaccess.thecvf.com/content/CVPR2024/papers/Haji-Ali_ElasticDiffusion_Training-free_Arbitrary_Size_Image_Generation_through_Global-Local_Content_Separation_CVPR_2024_paper.pdf | http://arxiv.org/abs/2311.18822 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Haji-Ali_ElasticDiffusion_Training-free_Arbitrary_Size_Image_Generation_through_Global-Local_Content_Separation_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Haji-Ali_ElasticDiffusion_Training-free_Arbitrary_Size_Image_Generation_through_Global-Local_Content_Separation_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Haji-Ali_ElasticDiffusion_Training-free_Arbitrary_CVPR_2024_supplemental.pdf | null |
Locally Adaptive Neural 3D Morphable Models | Michail Tarasiou, Rolandos Alexandros Potamias, Eimear O'Sullivan, Stylianos Ploumpis, Stefanos Zafeiriou | We present the Locally Adaptive Morphable Model (LAMM) a highly flexible Auto-Encoder (AE) framework for learning to generate and manipulate 3D meshes. We train our architecture following a simple self-supervised training scheme in which input displacements over a set of sparse control vertices are used to overwrite the encoded geometry in order to transform one training sample into another. During inference our model produces a dense output that adheres locally to the specified sparse geometry while maintaining the overall appearance of the encoded object. This approach results in state-of-the-art performance in both disentangling manipulated geometry and 3D mesh reconstruction. To the best of our knowledge LAMM is the first end-to-end framework that enables direct local control of 3D vertex geometry in a single forward pass. A very efficient computational graph allows our network to train with only a fraction of the memory required by previous methods and run faster during inference generating 12k vertex meshes at >60fps on a single CPU thread. We further leverage local geometry control as a primitive for higher level editing operations and present a set of derivative capabilities such as swapping and sampling object parts. Code and pretrained models can be found at https://github.com/michaeltrs/LAMM. | https://openaccess.thecvf.com/content/CVPR2024/papers/Tarasiou_Locally_Adaptive_Neural_3D_Morphable_Models_CVPR_2024_paper.pdf | http://arxiv.org/abs/2401.02937 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Tarasiou_Locally_Adaptive_Neural_3D_Morphable_Models_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Tarasiou_Locally_Adaptive_Neural_3D_Morphable_Models_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Tarasiou_Locally_Adaptive_Neural_CVPR_2024_supplemental.pdf | null |
ICON: Incremental CONfidence for Joint Pose and Radiance Field Optimization | Weiyao Wang, Pierre Gleize, Hao Tang, Xingyu Chen, Kevin J Liang, Matt Feiszli | Neural Radiance Fields (NeRF) exhibit remarkable performance for Novel View Synthesis (NVS) given a set of 2D images. However NeRF training requires accurate camera pose for each input view typically obtained by Structure-from-Motion (SfM) pipelines. Recent works have attempted to relax this constraint but they still often rely on decent initial poses which they can refine. Here we aim at removing the requirement for pose initialization. We present Incremental CONfidence (ICON) an optimization procedure for training NeRFs from 2D video frames. ICON only assumes smooth camera motion to estimate initial guess for poses. Further ICON introduces "confidence": an adaptive measure of model quality used to dynamically reweight gradients. ICON relies on high-confidence poses to learn NeRF and high-confidence 3D structure (as encoded by NeRF) to learn poses. We show that ICON without prior pose initialization achieves superior performance in both CO3D and HO3D versus methods which use SfM pose. | https://openaccess.thecvf.com/content/CVPR2024/papers/Wang_ICON_Incremental_CONfidence_for_Joint_Pose_and_Radiance_Field_Optimization_CVPR_2024_paper.pdf | http://arxiv.org/abs/2401.08937 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Wang_ICON_Incremental_CONfidence_for_Joint_Pose_and_Radiance_Field_Optimization_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Wang_ICON_Incremental_CONfidence_for_Joint_Pose_and_Radiance_Field_Optimization_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Wang_ICON_Incremental_CONfidence_CVPR_2024_supplemental.zip | null |
Learned Scanpaths Aid Blind Panoramic Video Quality Assessment | null | null | null | null | null | https://openaccess.thecvf.com/content/CVPR2024/html/Fan_Learned_Scanpaths_Aid_Blind_Panoramic_Video_Quality_Assessment_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Fan_Learned_Scanpaths_Aid_Blind_Panoramic_Video_Quality_Assessment_CVPR_2024_paper.html | CVPR 2024 | null | null |
FineSports: A Multi-person Hierarchical Sports Video Dataset for Fine-grained Action Understanding | Jinglin Xu, Guohao Zhao, Sibo Yin, Wenhao Zhou, Yuxin Peng | Fine-grained action analysis in multi-person sports is complex due to athletes' quick movements and intense physical confrontations which result in severe visual obstructions in most scenes. In addition accessible multi-person sports video datasets lack fine-grained action annotations in both space and time adding to the difficulty in fine-grained action analysis. To this end we construct a new multi-person basketball sports video dataset named FineSports which contains fine-grained semantic and spatial-temporal annotations on 10000 NBA game videos covering 52 fine-grained action types 16000 action instances and 123000 spatial-temporal bounding boxes. We also propose a new prompt-driven spatial-temporal action location approach called PoSTAL composed of a prompt-driven target action encoder (PTA) and an action tube-specific detector (ATD) to directly generate target action tubes with fine-grained action types without any off-line proposal generation. Extensive experiments on the FineSports dataset demonstrate that PoSTAL outperforms state-of-the-art methods. Data and code are available at https://github.com/PKU-ICST-MIPL/FineSports_CVPR2024. | https://openaccess.thecvf.com/content/CVPR2024/papers/Xu_FineSports_A_Multi-person_Hierarchical_Sports_Video_Dataset_for_Fine-grained_Action_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Xu_FineSports_A_Multi-person_Hierarchical_Sports_Video_Dataset_for_Fine-grained_Action_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Xu_FineSports_A_Multi-person_Hierarchical_Sports_Video_Dataset_for_Fine-grained_Action_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Xu_FineSports_A_Multi-person_CVPR_2024_supplemental.pdf | null |
SHiNe: Semantic Hierarchy Nexus for Open-vocabulary Object Detection | Mingxuan Liu, Tyler L. Hayes, Elisa Ricci, Gabriela Csurka, Riccardo Volpi | Open-vocabulary object detection (OvOD) has transformed detection into a language-guided task empowering users to freely define their class vocabularies of interest during inference. However our initial investigation indicates that existing OvOD detectors exhibit significant variability when dealing with vocabularies across various semantic granularities posing a concern for real-world deployment. To this end we introduce Semantic Hierarchy Nexus (SHiNe) a novel classifier that uses semantic knowledge from class hierarchies. It runs offline in three steps: i) it retrieves relevant super-/sub-categories from a hierarchy for each target class; ii) it integrates these categories into hierarchy-aware sentences; iii) it fuses these sentence embeddings to generate the nexus classifier vector. Our evaluation on various detection benchmarks demonstrates that SHiNe enhances robustness across diverse vocabulary granularities achieving up to +31.9% mAP50 with ground truth hierarchies while retaining improvements using hierarchies generated by large language models. Moreover when applied to open-vocabulary classification on ImageNet-1k SHiNe improves the CLIP zero-shot baseline by +2.8% accuracy. SHiNe is training-free and can be seamlessly integrated with any off-the-shelf OvOD detector without incurring additional computational overhead during inference. The code is open source. | https://openaccess.thecvf.com/content/CVPR2024/papers/Liu_SHiNe_Semantic_Hierarchy_Nexus_for_Open-vocabulary_Object_Detection_CVPR_2024_paper.pdf | http://arxiv.org/abs/2405.10053 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Liu_SHiNe_Semantic_Hierarchy_Nexus_for_Open-vocabulary_Object_Detection_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Liu_SHiNe_Semantic_Hierarchy_Nexus_for_Open-vocabulary_Object_Detection_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Liu_SHiNe_Semantic_Hierarchy_CVPR_2024_supplemental.pdf | null |
TI2V-Zero: Zero-Shot Image Conditioning for Text-to-Video Diffusion Models | Haomiao Ni, Bernhard Egger, Suhas Lohit, Anoop Cherian, Ye Wang, Toshiaki Koike-Akino, Sharon X. Huang, Tim K. Marks | Text-conditioned image-to-video generation (TI2V) aims to synthesize a realistic video starting from a given image (e.g. a woman's photo) and a text description (e.g. "a woman is drinking water."). Existing TI2V frameworks often require costly training on video-text datasets and specific model designs for text and image conditioning. In this paper we propose TI2V-Zero a zero-shot tuning-free method that empowers a pretrained text-to-video (T2V) diffusion model to be conditioned on a provided image enabling TI2V generation without any optimization fine-tuning or introducing external modules. Our approach leverages a pretrained T2V diffusion foundation model as the generative prior. To guide video generation with the additional image input we propose a "repeat-and-slide" strategy that modulates the reverse denoising process allowing the frozen diffusion model to synthesize a video frame-by-frame starting from the provided image. To ensure temporal continuity we employ a DDPM inversion strategy to initialize Gaussian noise for each newly synthesized frame and a resampling technique to help preserve visual details. We conduct comprehensive experiments on both domain-specific and open-domain datasets where TI2V-Zero consistently outperforms a recent open-domain TI2V model. Furthermore we show that TI2V-Zero can seamlessly extend to other tasks such as video infilling and prediction when provided with more images. Its autoregressive design also supports long video generation. | https://openaccess.thecvf.com/content/CVPR2024/papers/Ni_TI2V-Zero_Zero-Shot_Image_Conditioning_for_Text-to-Video_Diffusion_Models_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Ni_TI2V-Zero_Zero-Shot_Image_Conditioning_for_Text-to-Video_Diffusion_Models_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Ni_TI2V-Zero_Zero-Shot_Image_Conditioning_for_Text-to-Video_Diffusion_Models_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Ni_TI2V-Zero_Zero-Shot_Image_CVPR_2024_supplemental.zip | null |
Ranking Distillation for Open-Ended Video Question Answering with Insufficient Labels | Tianming Liang, Chaolei Tan, Beihao Xia, Wei-Shi Zheng, Jian-Fang Hu | This paper focuses on open-ended video question answering which aims to find the correct answers from a large answer set in response to a video-related question. This is essentially a multi-label classification task since a question may have multiple answers. However due to annotation costs the labels in existing benchmarks are always extremely insufficient typically one answer per question. As a result existing works tend to directly treat all the unlabeled answers as negative labels leading to limited ability for generalization. In this work we introduce a simple yet effective ranking distillation framework (RADI) to mitigate this problem without additional manual annotation. RADI employs a teacher model trained with incomplete labels to generate rankings for potential answers which contain rich knowledge about label priority as well as label-associated visual cues thereby enriching the insufficient labeling information. To avoid overconfidence in the imperfect teacher model we further present two robust and parameter-free ranking distillation approaches: a pairwise approach which introduces adaptive soft margins to dynamically refine the optimization constraints on various pairwise rankings and a listwise approach which adopts sampling-based partial listwise learning to resist the bias in teacher ranking. Extensive experiments on five popular benchmarks consistently show that both our pairwise and listwise RADIs outperform state-of-the-art methods. Further analysis demonstrates the effectiveness of our methods on the insufficient labeling problem. | https://openaccess.thecvf.com/content/CVPR2024/papers/Liang_Ranking_Distillation_for_Open-Ended_Video_Question_Answering_with_Insufficient_Labels_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.14430 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Liang_Ranking_Distillation_for_Open-Ended_Video_Question_Answering_with_Insufficient_Labels_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Liang_Ranking_Distillation_for_Open-Ended_Video_Question_Answering_with_Insufficient_Labels_CVPR_2024_paper.html | CVPR 2024 | null | null |
GARField: Group Anything with Radiance Fields | Chung Min Kim, Mingxuan Wu, Justin Kerr, Ken Goldberg, Matthew Tancik, Angjoo Kanazawa | Grouping is inherently ambiguous due to the multiple levels of granularity in which one can decompose a scene --- should the wheels of an excavator be considered separate or part of the whole? We propose Group Anything with Radiance Fields (GARField) an approach for decomposing 3D scenes into a hierarchy of semantically meaningful groups from posed image inputs. To do this we embrace group ambiguity through physical scale: by optimizing a scale-conditioned 3D affinity feature field a point in the world can belong to different groups of different sizes. We optimize this field from a set of 2D masks provided by Segment Anything (SAM) in a way that respects coarse-to-fine hierarchy using scale to consistently fuse conflicting masks from different viewpoints. From this field we can derive a hierarchy of possible groupings via automatic tree construction or user interaction. We evaluate GARField on a variety of in-the-wild scenes and find it effectively extracts groups at many levels: clusters of objects objects and various subparts. GARField inherently represents multi-view consistent groupings and produces higher fidelity groups than the input SAM masks. GARField's hierarchical grouping could have exciting downstream applications such as 3D asset extraction or dynamic scene understanding. Project site: https://www.garfield.studio/ | https://openaccess.thecvf.com/content/CVPR2024/papers/Kim_GARField_Group_Anything_with_Radiance_Fields_CVPR_2024_paper.pdf | http://arxiv.org/abs/2401.09419 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Kim_GARField_Group_Anything_with_Radiance_Fields_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Kim_GARField_Group_Anything_with_Radiance_Fields_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Kim_GARField_Group_Anything_CVPR_2024_supplemental.pdf | null |
Depth-Aware Concealed Crop Detection in Dense Agricultural Scenes | Liqiong Wang, Jinyu Yang, Yanfu Zhang, Fangyi Wang, Feng Zheng | Concealed Object Detection (COD) aims to identify objects visually embedded in their background. Existing COD datasets and methods predominantly focus on animals or humans ignoring the agricultural domain which often contains numerous small and concealed crops with severe occlusions. In this paper we introduce Concealed Crop Detection (CCD) which extends classic COD to agricultural domains. Experimental study shows that unimodal data provides insufficient information for CCD. To address this gap we first collect a large-scale RGB-D dataset ACOD-12K containing high-resolution crop images and depth maps. Then we propose a foundational framework named Recurrent Iterative Segmentation Network (RISNet). To tackle the challenge of dense objects we employ multi-scale receptive fields to capture objects of varying sizes thus enhancing the detection performance for dense objects. By fusing depth features our method can acquire spatial information about concealed objects to mitigate disturbances caused by intricate backgrounds and occlusions. Furthermore our model adopts a multi-stage iterative approach using predictions from each stage as gate attention to reinforce position information thereby improving the detection accuracy for small objects. Extensive experimental results demonstrate that our RISNet achieves new state-of-the-art performance on both newly proposed CCD and classic COD tasks. All resources will be available at https://github.com/Kki2Eve/RISNet. | https://openaccess.thecvf.com/content/CVPR2024/papers/Wang_Depth-Aware_Concealed_Crop_Detection_in_Dense_Agricultural_Scenes_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Wang_Depth-Aware_Concealed_Crop_Detection_in_Dense_Agricultural_Scenes_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Wang_Depth-Aware_Concealed_Crop_Detection_in_Dense_Agricultural_Scenes_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Wang_Depth-Aware_Concealed_Crop_CVPR_2024_supplemental.pdf | null |
Learning Equi-angular Representations for Online Continual Learning | Minhyuk Seo, Hyunseo Koh, Wonje Jeung, Minjae Lee, San Kim, Hankook Lee, Sungjun Cho, Sungik Choi, Hyunwoo Kim, Jonghyun Choi | Online continual learning suffers from an underfitted solution due to insufficient training for prompt model updates (e.g. single-epoch training). To address the challenge we propose an efficient online continual learning method using the neural collapse phenomenon. In particular we induce neural collapse to form a simplex equiangular tight frame (ETF) structure in the representation space so that the continuously learned model with a single epoch can better fit to the streamed data by proposing preparatory data training and residual correction in the representation space. With an extensive set of empirical validations using CIFAR-10/100 TinyImageNet ImageNet-200 and ImageNet-1K we show that our proposed method outperforms state-of-the-art methods by a noticeable margin in various online continual learning scenarios such as disjoint and Gaussian scheduled continuous (i.e. boundary-free) data setups. | https://openaccess.thecvf.com/content/CVPR2024/papers/Seo_Learning_Equi-angular_Representations_for_Online_Continual_Learning_CVPR_2024_paper.pdf | http://arxiv.org/abs/2404.01628 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Seo_Learning_Equi-angular_Representations_for_Online_Continual_Learning_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Seo_Learning_Equi-angular_Representations_for_Online_Continual_Learning_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Seo_Learning_Equi-angular_Representations_CVPR_2024_supplemental.pdf | null |
iToF-flow-based High Frame Rate Depth Imaging | Yu Meng, Zhou Xue, Xu Chang, Xuemei Hu, Tao Yue | iToF is a prevalent cost-effective technology for 3D perception. While its reliance on multi-measurement commonly leads to reduced performance in dynamic environments. Based on the analysis of the physical iToF imaging process we propose the iToF flow composed of crossmode transformation and uni-mode photometric correction to model the variation of measurements caused by different measurement modes and 3D motion respectively. We propose a local linear transform (LLT) based cross-mode transfer module (LCTM) for mode-varying and pixel shift compensation of cross-mode flow and uni-mode photometric correct module (UPCM) for estimating the depth-wise motion caused photometric residual of uni-mode flow. The iToF flow-based depth extraction network is proposed which could facilitate the estimation of the 4-phase measurements at each individual time for high framerate and accurate depth estimation. Extensive experiments including both simulation and real-world experiments are conducted to demonstrate the effectiveness of the proposed methods. Compared with the SOTA method our approach reduces the computation time by 75% while improving the performance by 38%. The code and database are available at https://github.com/ComputationalPerceptionLab/iToF_flow. | https://openaccess.thecvf.com/content/CVPR2024/papers/Meng_iToF-flow-based_High_Frame_Rate_Depth_Imaging_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Meng_iToF-flow-based_High_Frame_Rate_Depth_Imaging_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Meng_iToF-flow-based_High_Frame_Rate_Depth_Imaging_CVPR_2024_paper.html | CVPR 2024 | null | null |
Solving the Catastrophic Forgetting Problem in Generalized Category Discovery | Xinzi Cao, Xiawu Zheng, Guanhong Wang, Weijiang Yu, Yunhang Shen, Ke Li, Yutong Lu, Yonghong Tian | Generalized Category Discovery (GCD) aims to identify a mix of known and novel categories within unlabeled data sets providing a more realistic setting for image recognition. Essentially GCD needs to remember existing patterns thoroughly to recognize novel categories. Recent state-of-the-art method SimGCD transfers the knowledge from known-class data to the learning of novel classes through debiased learning. However some patterns are catastrophically forgot during adaptation and thus lead to poor performance in novel categories classification. To address this issue we propose a novel learning approach LegoGCD which is seamlessly integrated into previous methods to enhance the discrimination of novel classes while maintaining performance on previously encountered known classes. Specifically we design two types of techniques termed as \underline L ocal \underline E ntropy Re\underline g ularization (LER) and Dual-views Kullback-Leibler divergence c\underline o nstraint (DKL). The LER optimizes the distribution of potential known class samples in unlabeled data thus ensuring the preservation of knowledge related to known categories while learning novel classes. Meanwhile DKL introduces Kullback-Leibler divergence to encourage the model to produce a similar prediction distribution of two view samples from the same image. In this way it successfully avoids mismatched prediction and generates more reliable potential known class samples simultaneously. Extensive experiments validate that the proposed LegoGCD effectively addresses the known category forgetting issue across all datasets e.g. delivering a 7.74% and 2.51% accuracy boost on known and novel classes in CUB respectively. Our code is available at: https://github.com/Cliffia123/LegoGCD. | https://openaccess.thecvf.com/content/CVPR2024/papers/Cao_Solving_the_Catastrophic_Forgetting_Problem_in_Generalized_Category_Discovery_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Cao_Solving_the_Catastrophic_Forgetting_Problem_in_Generalized_Category_Discovery_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Cao_Solving_the_Catastrophic_Forgetting_Problem_in_Generalized_Category_Discovery_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Cao_Solving_the_Catastrophic_CVPR_2024_supplemental.pdf | null |
Data-Efficient Unsupervised Interpolation Without Any Intermediate Frame for 4D Medical Images | JungEun Kim, Hangyul Yoon, Geondo Park, Kyungsu Kim, Eunho Yang | 4D medical images which represent 3D images with temporal information are crucial in clinical practice for capturing dynamic changes and monitoring long-term disease progression. However acquiring 4D medical images poses challenges due to factors such as radiation exposure and imaging duration necessitating a balance between achieving high temporal resolution and minimizing adverse effects. Given these circumstances not only is data acquisition challenging but increasing the frame rate for each dataset also proves difficult. To address this challenge this paper proposes a simple yet effective Unsupervised Volumetric Interpolation framework UVI-Net. This framework facilitates temporal interpolation without the need for any intermediate frames distinguishing it from the majority of other existing unsupervised methods. Experiments on benchmark datasets demonstrate significant improvements across diverse evaluation metrics compared to unsupervised and supervised baselines. Remarkably our approach achieves this superior performance even when trained with a dataset as small as one highlighting its exceptional robustness and efficiency in scenarios with sparse supervision. This positions UVI-Net as a compelling alternative for 4D medical imaging particularly in settings where data availability is limited. The source code is available at https://github.com/jungeun122333/UVI-Net. | https://openaccess.thecvf.com/content/CVPR2024/papers/Kim_Data-Efficient_Unsupervised_Interpolation_Without_Any_Intermediate_Frame_for_4D_Medical_CVPR_2024_paper.pdf | http://arxiv.org/abs/2404.01464 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Kim_Data-Efficient_Unsupervised_Interpolation_Without_Any_Intermediate_Frame_for_4D_Medical_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Kim_Data-Efficient_Unsupervised_Interpolation_Without_Any_Intermediate_Frame_for_4D_Medical_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Kim_Data-Efficient_Unsupervised_Interpolation_CVPR_2024_supplemental.pdf | null |
POCE: Primal Policy Optimization with Conservative Estimation for Multi-constraint Offline Reinforcement Learning | Jiayi Guan, Li Shen, Ao Zhou, Lusong Li, Han Hu, Xiaodong He, Guang Chen, Changjun Jiang | Multi-constraint offline reinforcement learning (RL) promises to learn policies that satisfy both cumulative and state-wise costs from offline datasets. This arrangement provides an effective approach for the widespread application of RL in high-risk scenarios where both cumulative and state-wise costs need to be considered simultaneously. However previously constrained offline RL algorithms are primarily designed to handle single-constraint problems related to cumulative cost which faces challenges when addressing multi-constraint tasks that involve both cumulative and state-wise costs. In this work we propose a novel Primal policy Optimization with Conservative Estimation algorithm (POCE) to address the problem of multi-constraint offline RL. Concretely we reframe the objective of multi-constraint offline RL by introducing the concept of Maximum Markov Decision Processes (MMDP). Subsequently we present a primal policy optimization algorithm to confront the multi-constraint problems which improves the stability and convergence speed of model training. Furthermore we propose a conditional Bellman operator to estimate cumulative and state-wise Q-values reducing the extrapolation error caused by out-of-distribution (OOD) actions. Finally extensive experiments demonstrate that the POCE algorithm achieves competitive performance across multiple experimental tasks particularly outperforming baseline algorithms in terms of safety. Our code is available at \href https://github.com/guanjiayi/poce github.POCE . | https://openaccess.thecvf.com/content/CVPR2024/papers/Guan_POCE_Primal_Policy_Optimization_with_Conservative_Estimation_for_Multi-constraint_Offline_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Guan_POCE_Primal_Policy_Optimization_with_Conservative_Estimation_for_Multi-constraint_Offline_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Guan_POCE_Primal_Policy_Optimization_with_Conservative_Estimation_for_Multi-constraint_Offline_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Guan_POCE_Primal_Policy_CVPR_2024_supplemental.pdf | null |
Learning the 3D Fauna of the Web | Zizhang Li, Dor Litvak, Ruining Li, Yunzhi Zhang, Tomas Jakab, Christian Rupprecht, Shangzhe Wu, Andrea Vedaldi, Jiajun Wu | Learning 3D models of all animals in nature requires massively scaling up existing solutions. With this ultimate goal in mind we develop 3D-Fauna an approach that learns a pan-category deformable 3D animal model for more than 100 animal species jointly. One crucial bottleneck of modeling animals is the limited availability of training data which we overcome by learning our model from 2D Internet images. We show that prior approaches which are category-specific fail to generalize to rare species with limited training images. We address this challenge by introducing the Semantic Bank of Skinned Models (SBSM) which automatically discovers a small set of base animal shapes by combining geometric inductive priors with semantic knowledge implicitly captured by an off-the-shelf self-supervised feature extractor. To train such a model we also contribute a new large-scale dataset of diverse animal species. At inference time given a single image of any quadruped animal our model reconstructs an articulated 3D mesh in a feed-forward manner in seconds. | https://openaccess.thecvf.com/content/CVPR2024/papers/Li_Learning_the_3D_Fauna_of_the_Web_CVPR_2024_paper.pdf | http://arxiv.org/abs/2401.02400 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Li_Learning_the_3D_Fauna_of_the_Web_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Li_Learning_the_3D_Fauna_of_the_Web_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Li_Learning_the_3D_CVPR_2024_supplemental.pdf | null |
Masked Spatial Propagation Network for Sparsity-Adaptive Depth Refinement | Jinyoung Jun, Jae-Han Lee, Chang-Su Kim | The main function of depth completion is to compensate for an insufficient and unpredictable number of sparse depth measurements of hardware sensors. However existing research on depth completion assumes that the sparsity --- the number of points or LiDAR lines --- is fixed for training and testing. Hence the completion performance drops severely when the number of sparse depths changes significantly. To address this issue we propose the sparsity-adaptive depth refinement (SDR) framework which refines monocular depth estimates using sparse depth points. For SDR we propose the masked spatial propagation network (MSPN) to perform SDR with a varying number of sparse depths effectively by gradually propagating sparse depth information throughout the entire depth map. Experimental results demonstrate that MPSN achieves state-of-the-art performance on both SDR and conventional depth completion scenarios. | https://openaccess.thecvf.com/content/CVPR2024/papers/Jun_Masked_Spatial_Propagation_Network_for_Sparsity-Adaptive_Depth_Refinement_CVPR_2024_paper.pdf | http://arxiv.org/abs/2404.19294 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Jun_Masked_Spatial_Propagation_Network_for_Sparsity-Adaptive_Depth_Refinement_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Jun_Masked_Spatial_Propagation_Network_for_Sparsity-Adaptive_Depth_Refinement_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Jun_Masked_Spatial_Propagation_CVPR_2024_supplemental.pdf | null |
LISA: Reasoning Segmentation via Large Language Model | Xin Lai, Zhuotao Tian, Yukang Chen, Yanwei Li, Yuhui Yuan, Shu Liu, Jiaya Jia | Although perception systems have made remarkable advancements in recent years they still rely on explicit human instruction or pre-defined categories to identify the target objects before executing visual recognition tasks. Such systems cannot actively reason and comprehend implicit user intention. In this work we propose a new segmentation task --- reasoning segmentation. The task is designed to output a segmentation mask given a complex and implicit query text. Furthermore we establish a benchmark comprising over one thousand image-instruction-mask data samples incorporating intricate reasoning and world knowledge for evaluation purposes. Finally we present LISA: large Language Instructed Segmentation Assistant which inherits the language generation capabilities of multimodal Large Language Models (LLMs) while also possessing the ability to produce segmentation masks. We expand the original vocabulary with a <SEG> token and propose the embedding-as-mask paradigm to unlock the segmentation capability. Remarkably LISA can handle cases involving complex reasoning and world knowledge. Also it demonstrates robust zero-shot capability when trained exclusively on reasoning-free datasets. In addition fine-tuning the model with merely 239 reasoning segmentation data samples results in further performance enhancement. Both quantitative and qualitative experiments show our method effectively unlocks new reasoning segmentation capabilities for multimodal LLMs. Code models and data are available at github.com/dvlab-research/LISA. | https://openaccess.thecvf.com/content/CVPR2024/papers/Lai_LISA_Reasoning_Segmentation_via_Large_Language_Model_CVPR_2024_paper.pdf | http://arxiv.org/abs/2308.00692 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Lai_LISA_Reasoning_Segmentation_via_Large_Language_Model_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Lai_LISA_Reasoning_Segmentation_via_Large_Language_Model_CVPR_2024_paper.html | CVPR 2024 | null | null |
Relightful Harmonization: Lighting-aware Portrait Background Replacement | Mengwei Ren, Wei Xiong, Jae Shin Yoon, Zhixin Shu, Jianming Zhang, HyunJoon Jung, Guido Gerig, He Zhang | Portrait harmonization aims to composite a subject into a new background adjusting its lighting and color to ensure harmony with the background scene. Existing harmonization techniques often only focus on adjusting the global color and brightness of the foreground and ignore crucial illumination cues from the background such as apparent lighting direction leading to unrealistic compositions. We introduce Relightful Harmonization a lighting-aware diffusion model designed to seamlessly harmonize sophisticated lighting effect for the foreground portrait using any background image. Our approach unfolds in three stages. First we introduce a lighting representation module that allows our diffusion model to encode lighting information from target image background. Second we introduce an alignment network that aligns lighting features learned from image background with lighting features learned from panorama environment maps which is a complete representation for scene illumination. Last to further boost the photorealism of the proposed method we introduce a novel data simulation pipeline that generates synthetic training pairs from a diverse range of natural images which are used to refine the model. Our method outperforms existing benchmarks in visual fidelity and lighting coherence showing superior generalization in real-world testing scenarios highlighting its versatility and practicality. | https://openaccess.thecvf.com/content/CVPR2024/papers/Ren_Relightful_Harmonization_Lighting-aware_Portrait_Background_Replacement_CVPR_2024_paper.pdf | http://arxiv.org/abs/2312.06886 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Ren_Relightful_Harmonization_Lighting-aware_Portrait_Background_Replacement_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Ren_Relightful_Harmonization_Lighting-aware_Portrait_Background_Replacement_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Ren_Relightful_Harmonization_Lighting-aware_CVPR_2024_supplemental.pdf | null |
Bridging the Gap: A Unified Video Comprehension Framework for Moment Retrieval and Highlight Detection | Yicheng Xiao, Zhuoyan Luo, Yong Liu, Yue Ma, Hengwei Bian, Yatai Ji, Yujiu Yang, Xiu Li | Video Moment Retrieval (MR) and Highlight Detection (HD) have attracted significant attention due to the growing demand for video analysis. Recent approaches treat MR and HD as similar video grounding problems and address them together with transformer-based architecture. However we observe that the emphasis of MR and HD differs with one necessitating the perception of local relationships and the other prioritizing the understanding of global contexts. Consequently the lack of task-specific design will inevitably lead to limitations in associating the intrinsic specialty of two tasks. To tackle the issue we propose a Unified Video COMprehension framework (UVCOM) to bridge the gap and jointly solve MR and HD effectively. By performing progressive integration on intra and inter-modality across multi-granularity UVCOM achieves the comprehensive understanding in processing a video. Moreover we present multi-aspect contrastive learning to consolidate the local relation modeling and global knowledge accumulation via well aligned multi-modal space. Extensive experiments on QVHighlights Charades-STA TACoS YouTube Highlights and TVSum datasets demonstrate the effectiveness and rationality of UVCOM which outperforms the state-of-the-art methods by a remarkable margin. | https://openaccess.thecvf.com/content/CVPR2024/papers/Xiao_Bridging_the_Gap_A_Unified_Video_Comprehension_Framework_for_Moment_CVPR_2024_paper.pdf | http://arxiv.org/abs/2311.16464 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Xiao_Bridging_the_Gap_A_Unified_Video_Comprehension_Framework_for_Moment_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Xiao_Bridging_the_Gap_A_Unified_Video_Comprehension_Framework_for_Moment_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Xiao_Bridging_the_Gap_CVPR_2024_supplemental.pdf | null |
MuseChat: A Conversational Music Recommendation System for Videos | Zhikang Dong, Xiulong Liu, Bin Chen, Pawel Polak, Peng Zhang | Music recommendation for videos attracts growing interest in multi-modal research. However existing systems focus primarily on content compatibility often ignoring the users' preferences. Their inability to interact with users for further refinements or to provide explanations leads to a less satisfying experience. We address these issues with MuseChat a first-of-its-kind dialogue-based recommendation system that personalizes music suggestions for videos. Our system consists of two key functionalities with associated modules: recommendation and reasoning. The recommendation module takes a video along with optional information including previous suggested music and user's preference as inputs and retrieves an appropriate music matching the context. The reasoning module equipped with the power of Large Language Model (Vicuna-7B) and extended to multi-modal inputs is able to provide reasonable explanation for the recommended music. To evaluate the effectiveness of MuseChat we build a large-scale dataset conversational music recommendation for videos that simulates a two-turn interaction between a user and a recommender based on accurate music track information. Experiment results show that MuseChat achieves significant improvements over existing video-based music retrieval methods as well as offers strong interpretability and interactability. The dataset of this work is available at https://dongzhikang.github.io/musechat. | https://openaccess.thecvf.com/content/CVPR2024/papers/Dong_MuseChat_A_Conversational_Music_Recommendation_System_for_Videos_CVPR_2024_paper.pdf | http://arxiv.org/abs/2310.06282 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Dong_MuseChat_A_Conversational_Music_Recommendation_System_for_Videos_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Dong_MuseChat_A_Conversational_Music_Recommendation_System_for_Videos_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Dong_MuseChat_A_Conversational_CVPR_2024_supplemental.zip | null |
Mitigating Motion Blur in Neural Radiance Fields with Events and Frames | Marco Cannici, Davide Scaramuzza | Neural Radiance Fields (NeRFs) have shown great potential in novel view synthesis. However they struggle to render sharp images when the data used for training is affected by motion blur. On the other hand event cameras excel in dynamic scenes as they measure brightness changes with microsecond resolution and are thus only marginally affected by blur. Recent methods attempt to enhance NeRF reconstructions under camera motion by fusing frames and events. However they face challenges in recovering accurate color content or constrain the NeRF to a set of predefined camera poses harming reconstruction quality in challenging conditions. This paper proposes a novel formulation addressing these issues by leveraging both model- and learning-based modules. We explicitly model the blur formation process exploiting the event double integral as an additional model-based prior. Additionally we model the event-pixel response using an end-to-end learnable response function allowing our method to adapt to non-idealities in the real event-camera sensor. We show on synthetic and real data that the proposed approach outperforms existing deblur NeRFs that use only frames as well as those that combine frames and events by +6.13dB and +2.48dB respectively. | https://openaccess.thecvf.com/content/CVPR2024/papers/Cannici_Mitigating_Motion_Blur_in_Neural_Radiance_Fields_with_Events_and_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.19780 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Cannici_Mitigating_Motion_Blur_in_Neural_Radiance_Fields_with_Events_and_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Cannici_Mitigating_Motion_Blur_in_Neural_Radiance_Fields_with_Events_and_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Cannici_Mitigating_Motion_Blur_CVPR_2024_supplemental.zip | null |
C3Net: Compound Conditioned ControlNet for Multimodal Content Generation | Juntao Zhang, Yuehuai Liu, Yu-Wing Tai, Chi-Keung Tang | We present Compound Conditioned ControlNet C3Net a novel generative neural architecture taking conditions from multiple modalities and synthesizing multimodal contents simultaneously (e.g. image text audio). C3Net adapts the ControlNet architecture to jointly train and make inferences on a production-ready diffusion model and its trainable copies. Specifically C3Net first aligns the conditions from multi-modalities to the same semantic latent space using modality-specific encoders based on contrastive training. Then it generates multimodal outputs based on the aligned latent space whose semantic information is combined using a ControlNet-like architecture called Control C3-UNet. Correspondingly with this system design our model offers an improved solution for joint-modality generation through learning and explaining multimodal conditions involving more than just linear interpolation within the latent space. Meanwhile as we align conditions to a unified latent space C3Net only requires one trainable Control C3-UNet to work on multimodal semantic information. Furthermore our model employs unimodal pretraining on the condition alignment stage outperforming the non-pretrained alignment even on relatively scarce training data and thus demonstrating high-quality compound condition generation. We contribute the first high-quality tri-modal validation set to validate quantitatively that C3Net outperforms or is on par with the first and contemporary state-of-the-art multimodal generation. Our codes and tri-modal dataset will be released. | https://openaccess.thecvf.com/content/CVPR2024/papers/Zhang_C3Net_Compound_Conditioned_ControlNet_for_Multimodal_Content_Generation_CVPR_2024_paper.pdf | http://arxiv.org/abs/2311.17951 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Zhang_C3Net_Compound_Conditioned_ControlNet_for_Multimodal_Content_Generation_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Zhang_C3Net_Compound_Conditioned_ControlNet_for_Multimodal_Content_Generation_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Zhang_C3Net_Compound_Conditioned_CVPR_2024_supplemental.zip | null |
Device-Wise Federated Network Pruning | Shangqian Gao, Junyi Li, Zeyu Zhang, Yanfu Zhang, Weidong Cai, Heng Huang | Neural network pruning particularly channel pruning is a widely used technique for compressing deep learning models to enable their deployment on edge devices with limited resources. Typically redundant weights or structures are removed to achieve the target resource budget. Although data-driven pruning approaches have proven to be more effective they cannot be directly applied to federated learning (FL) which has emerged as a popular technique in edge computing applications because of distributed and confidential datasets. In response to this challenge we design a new network pruning method for FL. We propose device-wise sub-networks for each device assuming that the data distribution is similar within each device. These sub-networks are generated through sub-network embeddings and a hypernetwork. To further minimize memory usage and communication costs we permanently prune the full model to remove weights that are not useful for all devices. During the FL process we simultaneously train the device-wise sub-networks and the base sub-network to facilitate the pruning process. We then finetune the pruned model with device-wise sub-networks to regain performance. Moreover we provided the theoretical guarantee of convergence for our method. Our method achieves better performance and resource trade-off than other well-established network pruning baselines as demonstrated through extensive experiments on CIFAR-10 CIFAR-100 and TinyImageNet. | https://openaccess.thecvf.com/content/CVPR2024/papers/Gao_Device-Wise_Federated_Network_Pruning_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Gao_Device-Wise_Federated_Network_Pruning_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Gao_Device-Wise_Federated_Network_Pruning_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Gao_Device-Wise_Federated_Network_CVPR_2024_supplemental.pdf | null |
Adapt Before Comparison: A New Perspective on Cross-Domain Few-Shot Segmentation | Jonas Herzog | Few-shot segmentation performance declines substantially when facing images from a domain different than the training domain effectively limiting real-world use cases. To alleviate this recently cross-domain few-shot segmentation (CD-FSS) has emerged. Works that address this task mainly attempted to learn segmentation on a source domain in a manner that generalizes across domains. Surprisingly we can outperform these approaches while eliminating the training stage and removing their main segmentation network. We show test-time task-adaption is the key for successful CD-FSS instead. Task-adaption is achieved by appending small networks to the feature pyramid of a conventionally classification-pretrained backbone. To avoid overfitting to the few labeled samples in supervised fine-tuning consistency across augmented views of input images serves as guidance while learning the parameters of the attached layers. Despite our self-restriction not to use any images other than the few labeled samples at test time we achieve new state-of-the-art performance in CD-FSS evidencing the need to rethink approaches for the task. Code is available at https://github.com/Vision-Kek/ABCDFSS. | https://openaccess.thecvf.com/content/CVPR2024/papers/Herzog_Adapt_Before_Comparison_A_New_Perspective_on_Cross-Domain_Few-Shot_Segmentation_CVPR_2024_paper.pdf | http://arxiv.org/abs/2402.17614 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Herzog_Adapt_Before_Comparison_A_New_Perspective_on_Cross-Domain_Few-Shot_Segmentation_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Herzog_Adapt_Before_Comparison_A_New_Perspective_on_Cross-Domain_Few-Shot_Segmentation_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Herzog_Adapt_Before_Comparison_CVPR_2024_supplemental.pdf | null |
TokenHMR: Advancing Human Mesh Recovery with a Tokenized Pose Representation | Sai Kumar Dwivedi, Yu Sun, Priyanka Patel, Yao Feng, Michael J. Black | We address the problem of regressing 3D human pose and shape from a single image with a focus on 3D accuracy. The current best methods leverage large datasets of 3D pseudo-ground-truth (p-GT) and 2D keypoints leading to robust performance. With such methods however we observe a paradoxical decline in 3D pose accuracy with increasing 2D accuracy. This is caused by biases in the p-GT and the use of an approximate camera projection model. We quantify the error induced by current camera models and show that fitting 2D keypoints and p-GT accurately causes incorrect 3D poses. Our analysis defines the invalid distances within which minimizing 2D and p-GT losses is detrimental. We use this to formulate a new loss "Threshold-Adaptive Loss Scaling" (TALS) that penalizes gross 2D and p-GT errors but not smaller ones. With such a loss there are many 3D poses that could equally explain the 2D evidence. To reduce this ambiguity we need a prior over valid human poses but such priors can introduce unwanted bias. To address this we exploit a tokenized representation of human pose and reformulate the problem as token prediction. This restricts the estimated poses to the space of valid poses effectively improving robustness to occlusion. Extensive experiments on the EMDB and 3DPW datasets show that our reformulated loss and tokenization allows us to train on in-the-wild data while improving 3D accuracy over the state-of-the-art. Our models and code are available for research at https://tokenhmr.is.tue.mpg.de. | https://openaccess.thecvf.com/content/CVPR2024/papers/Dwivedi_TokenHMR_Advancing_Human_Mesh_Recovery_with_a_Tokenized_Pose_Representation_CVPR_2024_paper.pdf | http://arxiv.org/abs/2404.16752 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Dwivedi_TokenHMR_Advancing_Human_Mesh_Recovery_with_a_Tokenized_Pose_Representation_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Dwivedi_TokenHMR_Advancing_Human_Mesh_Recovery_with_a_Tokenized_Pose_Representation_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Dwivedi_TokenHMR_Advancing_Human_CVPR_2024_supplemental.pdf | null |
MoReVQA: Exploring Modular Reasoning Models for Video Question Answering | Juhong Min, Shyamal Buch, Arsha Nagrani, Minsu Cho, Cordelia Schmid | This paper addresses the task of video question answering (videoQA) via a decomposed multi-stage modular reasoning framework. Previous modular methods have shown promise with a single planning stage ungrounded in visual content. However through a simple and effective baseline we find that such systems can lead to brittle behavior in practice for challenging videoQA settings. Thus unlike traditional single-stage planning methods we propose a multi-stage system consisting of an event parser a grounding stage and a final reasoning stage in conjunction with an external memory. All stages are training-free and performed using few-shot prompting of large models creating interpretable intermediate outputs at each stage. By decomposing the underlying planning and task complexity our method MoReVQA improves over prior work on standard videoQA benchmarks (NExT-QA iVQA EgoSchema and ActivityNet-QA) with state-of-the-art results and extensions to related tasks (grounded videoQA paragraph captioning). | https://openaccess.thecvf.com/content/CVPR2024/papers/Min_MoReVQA_Exploring_Modular_Reasoning_Models_for_Video_Question_Answering_CVPR_2024_paper.pdf | http://arxiv.org/abs/2404.06511 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Min_MoReVQA_Exploring_Modular_Reasoning_Models_for_Video_Question_Answering_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Min_MoReVQA_Exploring_Modular_Reasoning_Models_for_Video_Question_Answering_CVPR_2024_paper.html | CVPR 2024 | null | null |
Low-Rank Rescaled Vision Transformer Fine-Tuning: A Residual Design Approach | Wei Dong, Xing Zhang, Bihui Chen, Dawei Yan, Zhijun Lin, Qingsen Yan, Peng Wang, Yang Yang | Parameter-efficient fine-tuning for pre-trained Vision Transformers aims to adeptly tailor a model to downstream tasks by learning a minimal set of new adaptation parameters while preserving the frozen majority of pre-trained parameters. Striking a balance between retaining the generalizable representation capacity of the pre-trained model and acquiring task-specific features poses a key challenge. Currently there is a lack of focus on guiding this delicate trade-off. In this study we approach the problem from the perspective of Singular Value Decomposition (SVD) of pre-trained parameter matrices providing insights into the tuning dynamics of existing methods. Building upon this understanding we propose a Residual-based Low-Rank Rescaling (RLRR) fine-tuning strategy. This strategy not only enhances flexibility in parameter tuning but also ensures that new parameters do not deviate excessively from the pre-trained model through a residual design. Extensive experiments demonstrate that our method achieves competitive performance across various downstream image classification tasks all while maintaining comparable new parameters. We believe this work takes a step forward in offering a unified perspective for interpreting existing methods and serves as motivation for the development of new approaches that move closer to effectively considering the crucial trade-off mentioned above. Our code is available at https://github.com/zstarN70/RLRR.git. | https://openaccess.thecvf.com/content/CVPR2024/papers/Dong_Low-Rank_Rescaled_Vision_Transformer_Fine-Tuning_A_Residual_Design_Approach_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Dong_Low-Rank_Rescaled_Vision_Transformer_Fine-Tuning_A_Residual_Design_Approach_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Dong_Low-Rank_Rescaled_Vision_Transformer_Fine-Tuning_A_Residual_Design_Approach_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Dong_Low-Rank_Rescaled_Vision_CVPR_2024_supplemental.pdf | null |
FaceCom: Towards High-fidelity 3D Facial Shape Completion via Optimization and Inpainting Guidance | Yinglong Li, Hongyu Wu, Xiaogang Wang, Qingzhao Qin, Yijiao Zhao, Yong Wang, Aimin Hao | We propose FaceCom a method for 3D facial shape completion which delivers high-fidelity results for incomplete facial inputs of arbitrary forms. Unlike end-to-end shape completion methods based on point clouds or voxels our approach relies on a mesh-based generative network that is easy to optimize enabling it to handle shape completion for irregular facial scans. We first train a shape generator on a mixed 3D facial dataset containing 2405 identities. Based on the incomplete facial input we fit complete faces using an optimization approach under image inpainting guidance. The completion results are refined through a post-processing step. FaceCom demonstrates the ability to effectively and naturally complete facial scan data with varying missing regions and degrees of missing areas. Our method can be used in medical prosthetic fabrication and the registration of deficient scanning data. Our experimental results demonstrate that FaceCom achieves exceptional performance in fitting and shape completion tasks. | https://openaccess.thecvf.com/content/CVPR2024/papers/Li_FaceCom_Towards_High-fidelity_3D_Facial_Shape_Completion_via_Optimization_and_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Li_FaceCom_Towards_High-fidelity_3D_Facial_Shape_Completion_via_Optimization_and_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Li_FaceCom_Towards_High-fidelity_3D_Facial_Shape_Completion_via_Optimization_and_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Li_FaceCom_Towards_High-fidelity_CVPR_2024_supplemental.zip | null |
Distribution-aware Knowledge Prototyping for Non-exemplar Lifelong Person Re-identification | Kunlun Xu, Xu Zou, Yuxin Peng, Jiahuan Zhou | Lifelong person re-identification (LReID) suffers from the catastrophic forgetting problem when learning from non-stationary data. Existing exemplar-based and knowledge distillation-based LReID methods encounter data privacy and limited acquisition capacity respectively. In this paper we instead introduce the prototype which is under-investigated in LReID to better balance knowledge forgetting and acquisition. Existing prototype-based works primarily focus on the classification task where the prototypes are set as discrete points or statistical distributions. However they either discard the distribution information or omit instance-level diversity which are crucial fine-grained clues for LReID. To address the above problems we propose Distribution-aware Knowledge Prototyping (DKP) where the instance-level diversity of each sample is modeled to transfer comprehensive fine-grained knowledge for prototyping and facilitating LReID learning. Specifically an Instance-level Distribution Modeling network is proposed to capture the local diversity of each instance. Then the Distribution-oriented Prototype Generation algorithm transforms the instance-level diversity into identity-level distributions as prototypes which is further explored by the designed Prototype-based Knowledge Transfer module to enhance the knowledge anti-forgetting and acquisition capacity of the LReID model. Extensive experiments verify that our method achieves superior plasticity and stability balancing and outperforms existing LReID methods by 8.1%/9.1% average mAP/R@1 improvement. The code is available at https://github.com/zhoujiahuan1991/CVPR2024-DKP | https://openaccess.thecvf.com/content/CVPR2024/papers/Xu_Distribution-aware_Knowledge_Prototyping_for_Non-exemplar_Lifelong_Person_Re-identification_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Xu_Distribution-aware_Knowledge_Prototyping_for_Non-exemplar_Lifelong_Person_Re-identification_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Xu_Distribution-aware_Knowledge_Prototyping_for_Non-exemplar_Lifelong_Person_Re-identification_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Xu_Distribution-aware_Knowledge_Prototyping_CVPR_2024_supplemental.pdf | null |
LightOctree: Lightweight 3D Spatially-Coherent Indoor Lighting Estimation | Xuecan Wang, Shibang Xiao, Xiaohui Liang | We present a lightweight solution for estimating spatially-coherent indoor lighting from a single RGB image. Previous methods for estimating illumination using volumetric representations have overlooked the sparse distribution of light sources in space necessitating substantial memory and computational resources for achieving high-quality results. We introduce a unified voxel octree-based illumination estimation framework to produce 3D spatially-coherent lighting. Additionally a differentiable voxel octree cone tracing rendering layer is proposed to eliminate regular volumetric representation throughout the entire process and ensure the retention of features across different frequency domains. This reduction significantly decreases spatial usage and required floating-point operations without substantially compromising precision. Experimental results demonstrate that our approach achieves high-quality coherent estimation with minimal cost compared to previous methods. | https://openaccess.thecvf.com/content/CVPR2024/papers/Wang_LightOctree_Lightweight_3D_Spatially-Coherent_Indoor_Lighting_Estimation_CVPR_2024_paper.pdf | http://arxiv.org/abs/2404.03925 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Wang_LightOctree_Lightweight_3D_Spatially-Coherent_Indoor_Lighting_Estimation_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Wang_LightOctree_Lightweight_3D_Spatially-Coherent_Indoor_Lighting_Estimation_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Wang_LightOctree_Lightweight_3D_CVPR_2024_supplemental.pdf | null |
Generating Enhanced Negatives for Training Language-Based Object Detectors | Shiyu Zhao, Long Zhao, Vijay Kumar B G, Yumin Suh, Dimitris N. Metaxas, Manmohan Chandraker, Samuel Schulter | The recent progress in language-based open-vocabulary object detection can be largely attributed to finding better ways of leveraging large-scale data with free-form text annotations. Training such models with a discriminative objective function has proven successful but requires good positive and negative samples. However the free-form nature and the open vocabulary of object descriptions make the space of negatives extremely large. Prior works randomly sample negatives or use rule-based techniques to build them. In contrast we propose to leverage the vast knowledge built into modern generative models to automatically build negatives that are more relevant to the original data. Specifically we use large-language-models to generate negative text descriptions and text-to-image diffusion models to also generate corresponding negative images. Our experimental analysis confirms the relevance of the generated negative data and its use in language-based detectors improves performance on two complex benchmarks. Code is available at https://github.com/xiaofeng94/Gen-Enhanced-Negs. | https://openaccess.thecvf.com/content/CVPR2024/papers/Zhao_Generating_Enhanced_Negatives_for_Training_Language-Based_Object_Detectors_CVPR_2024_paper.pdf | http://arxiv.org/abs/2401.00094 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Zhao_Generating_Enhanced_Negatives_for_Training_Language-Based_Object_Detectors_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Zhao_Generating_Enhanced_Negatives_for_Training_Language-Based_Object_Detectors_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Zhao_Generating_Enhanced_Negatives_CVPR_2024_supplemental.pdf | null |
Insect-Foundation: A Foundation Model and Large-scale 1M Dataset for Visual Insect Understanding | Hoang-Quan Nguyen, Thanh-Dat Truong, Xuan Bac Nguyen, Ashley Dowling, Xin Li, Khoa Luu | In precision agriculture the detection and recognition of insects play an essential role in the ability of crops to grow healthy and produce a high-quality yield. The current machine vision model requires a large volume of data to achieve high performance. However there are approximately 5.5 million different insect species in the world. None of the existing insect datasets can cover even a fraction of them due to varying geographic locations and acquisition costs. In this paper we introduce a novel "Insect-1M" dataset a game-changing resource poised to revolutionize insect-related foundation model training. Covering a vast spectrum of insect species our dataset including 1 million images with dense identification labels of taxonomy hierarchy and insect descriptions offers a panoramic view of entomology enabling foundation models to comprehend visual and semantic information about insects like never before. Then to efficiently establish an Insect Foundation Model we develop a micro-feature self-supervised learning method with a Patch-wise Relevant Attention mechanism capable of discerning the subtle differences among insect images. In addition we introduce Description Consistency loss to improve micro-feature modeling via insect descriptions. Through our experiments we illustrate the effectiveness of our proposed approach in insect modeling and achieve State-of-the-Art performance on standard benchmarks of insect-related tasks. Our Insect Foundation Model and Dataset promise to empower the next generation of insect-related vision models bringing them closer to the ultimate goal of precision agriculture. | https://openaccess.thecvf.com/content/CVPR2024/papers/Nguyen_Insect-Foundation_A_Foundation_Model_and_Large-scale_1M_Dataset_for_Visual_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Nguyen_Insect-Foundation_A_Foundation_Model_and_Large-scale_1M_Dataset_for_Visual_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Nguyen_Insect-Foundation_A_Foundation_Model_and_Large-scale_1M_Dataset_for_Visual_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Nguyen_Insect-Foundation_A_Foundation_CVPR_2024_supplemental.pdf | null |
Data-Efficient Multimodal Fusion on a Single GPU | Noël Vouitsis, Zhaoyan Liu, Satya Krishna Gorti, Valentin Villecroze, Jesse C. Cresswell, Guangwei Yu, Gabriel Loaiza-Ganem, Maksims Volkovs | The goal of multimodal alignment is to learn a single latent space that is shared between multimodal inputs. The most powerful models in this space have been trained using massive datasets of paired inputs and large-scale computational resources making them prohibitively expensive to train in many practical scenarios. We surmise that existing unimodal encoders pre-trained on large amounts of unimodal data should provide an effective bootstrap to create multimodal models from unimodal ones at much lower costs. We therefore propose FuseMix a multimodal augmentation scheme that operates on the latent spaces of arbitrary pre-trained unimodal encoders. Using FuseMix for multimodal alignment we achieve competitive performance - and in certain cases outperform state-of-the art methods - in both image-text and audio-text retrieval with orders of magnitude less compute and data: for example we outperform CLIP on the Flickr30K text-to-image retrieval task with ?600x fewer GPU days and ?80x fewer image-text pairs. Additionally we show how our method can be applied to convert pre-trained text-to-image generative models into audio-to-image ones. Code is available at: https://github.com/layer6ai-labs/fusemix. | https://openaccess.thecvf.com/content/CVPR2024/papers/Vouitsis_Data-Efficient_Multimodal_Fusion_on_a_Single_GPU_CVPR_2024_paper.pdf | http://arxiv.org/abs/2312.10144 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Vouitsis_Data-Efficient_Multimodal_Fusion_on_a_Single_GPU_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Vouitsis_Data-Efficient_Multimodal_Fusion_on_a_Single_GPU_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Vouitsis_Data-Efficient_Multimodal_Fusion_CVPR_2024_supplemental.pdf | null |
FedSelect: Personalized Federated Learning with Customized Selection of Parameters for Fine-Tuning | Rishub Tamirisa, Chulin Xie, Wenxuan Bao, Andy Zhou, Ron Arel, Aviv Shamsian | Standard federated learning approaches suffer when client data distributions have sufficient heterogeneity. Recent methods addressed the client data heterogeneity issue via personalized federated learning (PFL) - a class of FL algorithms aiming to personalize learned global knowledge to better suit the clients' local data distributions. Existing PFL methods usually decouple global updates in deep neural networks by performing personalization on particular layers (i.e. classifier heads) and global aggregation for the rest of the network. However preselecting network layers for personalization may result in suboptimal storage of global knowledge. In this work we propose FedSelect a novel PFL algorithm inspired by the iterative subnetwork discovery procedure used for the Lottery Ticket Hypothesis. FedSelect incrementally expands subnetworks to personalize client parameters concurrently conducting global aggregations on the remaining parameters. This approach enables the personalization of both client parameters and subnetwork structure during the training process. Finally we show that FedSelect outperforms recent state-of-the-art PFL algorithms under challenging client data heterogeneity settings and demonstrates robustness to various real-world distributional shifts. | https://openaccess.thecvf.com/content/CVPR2024/papers/Tamirisa_FedSelect_Personalized_Federated_Learning_with_Customized_Selection_of_Parameters_for_CVPR_2024_paper.pdf | http://arxiv.org/abs/2404.02478 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Tamirisa_FedSelect_Personalized_Federated_Learning_with_Customized_Selection_of_Parameters_for_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Tamirisa_FedSelect_Personalized_Federated_Learning_with_Customized_Selection_of_Parameters_for_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Tamirisa_FedSelect_Personalized_Federated_CVPR_2024_supplemental.pdf | null |
FaceLift: Semi-supervised 3D Facial Landmark Localization | David Ferman, Pablo Garrido, Gaurav Bharaj | 3D facial landmark localization has proven to be of particular use for applications such as face tracking 3D face modeling and image-based 3D face reconstruction. In the supervised learning case such methods usually rely on 3D landmark datasets derived from 3DMM-based registration that often lack spatial definition alignment as compared with that chosen by hand-labeled human consensus e.g. how are eyebrow landmarks defined? This creates a gap between landmark datasets generated via high-quality 2D human labels and 3DMMs and it ultimately limits their effectiveness. To address this issue we introduce a novel semi-supervised learning approach that learns 3D landmarks by directly lifting (visible) hand-labeled 2D landmarks and ensures better definition alignment without the need for 3D landmark datasets. To lift 2D landmarks to 3D we leverage 3D-aware GANs for better multi-view consistency learning and in-the-wild multi-frame videos for robust cross-generalization. Empirical experiments demonstrate that our method not only achieves better definition alignment between 2D-3D landmarks but also outperforms other supervised learning 3D landmark localization methods on both 3DMM labeled and photogrammetric ground truth evaluation datasets. Project Page: https://davidcferman.github.io/FaceLift | https://openaccess.thecvf.com/content/CVPR2024/papers/Ferman_FaceLift_Semi-supervised_3D_Facial_Landmark_Localization_CVPR_2024_paper.pdf | http://arxiv.org/abs/2405.19646 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Ferman_FaceLift_Semi-supervised_3D_Facial_Landmark_Localization_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Ferman_FaceLift_Semi-supervised_3D_Facial_Landmark_Localization_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Ferman_FaceLift_Semi-supervised_3D_CVPR_2024_supplemental.zip | null |
PSDPM: Prototype-based Secondary Discriminative Pixels Mining for Weakly Supervised Semantic Segmentation | Xinqiao Zhao, Ziqian Yang, Tianhong Dai, Bingfeng Zhang, Jimin Xiao | Image-level Weakly Supervised Semantic Segmentation (WSSS) has received increasing attention due to its low annotation cost. Class Activation Mapping (CAM) generated through classifier weights in WSSS inevitably ignores certain useful cues while the CAM generated through class prototypes can alleviate that. However because of the different goals of image classification and semantic segmentation the class prototypes still focus on activating primary discriminative pixels learned from classification loss leading to incomplete CAM. In this paper we propose a plugand-play Prototype-based Secondary Discriminative Pixels Mining (PSDPM) framework for enabling class prototypes to activate more secondary discriminative pixels thus generating a more complete CAM. Specifically we introduce a Foreground Pixel Estimation Module (FPEM) for estimating potential foreground pixels based on the correlations between primary and secondary discriminative pixels and the semantic segmentation results of baseline methods. Then we enable WSSS model to learn discriminative features from secondary discriminative pixels through a consistency loss calculated between FPEM result and class-prototype CAM. Experimental results show that our PSDPM improves various baseline methods significantly and achieves new state-of-the-art performances on WSSS benchmarks. Codes are available at https://github.com/xinqiaozhao/PSDPM. | https://openaccess.thecvf.com/content/CVPR2024/papers/Zhao_PSDPM_Prototype-based_Secondary_Discriminative_Pixels_Mining_for_Weakly_Supervised_Semantic_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Zhao_PSDPM_Prototype-based_Secondary_Discriminative_Pixels_Mining_for_Weakly_Supervised_Semantic_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Zhao_PSDPM_Prototype-based_Secondary_Discriminative_Pixels_Mining_for_Weakly_Supervised_Semantic_CVPR_2024_paper.html | CVPR 2024 | null | null |
Bidirectional Multi-Scale Implicit Neural Representations for Image Deraining | Xiang Chen, Jinshan Pan, Jiangxin Dong | How to effectively explore multi-scale representations of rain streaks is important for image deraining. In contrast to existing Transformer-based methods that depend mostly on single-scale rain appearance we develop an end-to-end multi-scale Transformer that leverages the potentially useful features in various scales to facilitate high-quality image reconstruction. To better explore the common degradation representations from spatially-varying rain streaks we incorporate intra-scale implicit neural representations based on pixel coordinates with the degraded inputs in a closed-loop design enabling the learned features to facilitate rain removal and improve the robustness of the model in complex scenarios. To ensure richer collaborative representation from different scales we embed a simple yet effective inter-scale bidirectional feedback operation into our multi-scale Transformer by performing coarse-to-fine and fine-to-coarse information communication. Extensive experiments demonstrate that our approach named as NeRD-Rain performs favorably against the state-of-the-art ones on both synthetic and real-world benchmark datasets. The source code and trained models are available at https://github.com/cschenxiang/NeRD-Rain. | https://openaccess.thecvf.com/content/CVPR2024/papers/Chen_Bidirectional_Multi-Scale_Implicit_Neural_Representations_for_Image_Deraining_CVPR_2024_paper.pdf | http://arxiv.org/abs/2404.01547 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Chen_Bidirectional_Multi-Scale_Implicit_Neural_Representations_for_Image_Deraining_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Chen_Bidirectional_Multi-Scale_Implicit_Neural_Representations_for_Image_Deraining_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Chen_Bidirectional_Multi-Scale_Implicit_CVPR_2024_supplemental.pdf | null |
Frozen CLIP: A Strong Backbone for Weakly Supervised Semantic Segmentation | Bingfeng Zhang, Siyue Yu, Yunchao Wei, Yao Zhao, Jimin Xiao | Weakly supervised semantic segmentation has witnessed great achievements with image-level labels. Several recent approaches use the CLIP model to generate pseudo labels for training an individual segmentation model while there is no attempt to apply the CLIP model as the backbone to directly segment objects with image-level labels. In this paper we propose WeCLIP a CLIP-based single-stage pipeline for weakly supervised semantic segmentation. Specifically the frozen CLIP model is applied as the backbone for semantic feature extraction and a new decoder is designed to interpret extracted semantic features for final prediction. Meanwhile we utilize the above frozen backbone to generate pseudo labels for training the decoder. Such labels cannot be optimized during training. We then propose a refinement module (RFM) to rectify them dynamically. Our architecture enforces the proposed decoder and RFM to benefit from each other to boost the final performance. Extensive experiments show that our approach significantly outperforms other approaches with less training cost. Additionally our WeCLIP also obtains promising results for fully supervised settings. The code is available at https://github.com/zbf1991/WeCLIP. | https://openaccess.thecvf.com/content/CVPR2024/papers/Zhang_Frozen_CLIP_A_Strong_Backbone_for_Weakly_Supervised_Semantic_Segmentation_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Zhang_Frozen_CLIP_A_Strong_Backbone_for_Weakly_Supervised_Semantic_Segmentation_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Zhang_Frozen_CLIP_A_Strong_Backbone_for_Weakly_Supervised_Semantic_Segmentation_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Zhang_Frozen_CLIP_A_CVPR_2024_supplemental.pdf | null |
FedAS: Bridging Inconsistency in Personalized Federated Learning | Xiyuan Yang, Wenke Huang, Mang Ye | Personalized Federated Learning (PFL) is primarily designed to provide customized models for each client to better fit the non-iid distributed client data which is a inherent challenge in Federated Learning. However current PFL methods suffer from inconsistencies in both intra-client and inter-client levels: 1) The intra-client inconsistency stems from the asynchronous update strategy for personalized and shared parameters. In PFL clients update their shared parameters to communicate and learn from others while keeping personalized parts unchanged leading to poor coordination between these two components. 2) The Inter-client inconsistency arises from "stragglers" - inactive clients that communicate and train with the server less frequently. This results in their under-trained personalized models and impedes the collaborative training stage for other clients. In this paper we present a novel PFL framework named FedAS which uses Federated Parameter-Alignment and Client-Synchronization to overcome above challenges. Initially we enhance the localization of global parameters by infusing them with local insights. We make the shared parts learn from previous model thereby increasing their local relevance and reducing the impact of parameter inconsistency. Furthermore we design a robust aggregation method to mitigate the impact of stragglers by preventing the incorporation of their under-trained knowledge into aggregated model. Experimental results on Cifar10 and Cifar100 validate the effectiveness of our FedAS in achieving better performance and robustness against data heterogeneity. | https://openaccess.thecvf.com/content/CVPR2024/papers/Yang_FedAS_Bridging_Inconsistency_in_Personalized_Federated_Learning_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Yang_FedAS_Bridging_Inconsistency_in_Personalized_Federated_Learning_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Yang_FedAS_Bridging_Inconsistency_in_Personalized_Federated_Learning_CVPR_2024_paper.html | CVPR 2024 | null | null |
LAFS: Landmark-based Facial Self-supervised Learning for Face Recognition | Zhonglin Sun, Chen Feng, Ioannis Patras, Georgios Tzimiropoulos | In this work we focus on learning facial representations that can be adapted to train effective face recognition models particularly in the absence of labels. Firstly compared with existing labelled face datasets a vastly larger magnitude of unlabeled faces exists in the real world. We explore the learning strategy of these unlabeled facial images through self-supervised pretraining to transfer generalized face recognition performance. Moreover motivated by one recent finding that is the face saliency area is critical for face recognition in contrast to utilizing random cropped blocks of images for constructing augmentations in pretraining we utilize patches localized by extracted facial landmarks. This enables our method - namely Landmark-based Facial Self-supervised learning (LAFS) to learn key representation that is more critical for face recognition. We also incorporate two landmark-specific augmentations which introduce more diversity of landmark information to further regularize the learning. With learned landmark-based facial representations we further adapt the representation for face recognition with regularization mitigating variations in landmark positions. Our method achieves significant improvement over the state-of-the-art on multiple face recognition benchmarks especially on more challenging few-shot scenarios. The code is available at https://github.com/szlbiubiubiu/LAFS_CVPR2024 | https://openaccess.thecvf.com/content/CVPR2024/papers/Sun_LAFS_Landmark-based_Facial_Self-supervised_Learning_for_Face_Recognition_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.08161 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Sun_LAFS_Landmark-based_Facial_Self-supervised_Learning_for_Face_Recognition_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Sun_LAFS_Landmark-based_Facial_Self-supervised_Learning_for_Face_Recognition_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Sun_LAFS_Landmark-based_Facial_CVPR_2024_supplemental.pdf | null |
SED: A Simple Encoder-Decoder for Open-Vocabulary Semantic Segmentation | Bin Xie, Jiale Cao, Jin Xie, Fahad Shahbaz Khan, Yanwei Pang | Open-vocabulary semantic segmentation strives to distinguish pixels into different semantic groups from an open set of categories. Most existing methods explore utilizing pre-trained vision-language models in which the key is to adopt the image-level model for pixel-level segmentation task. In this paper we propose a simple encoder-decoder named SED for open-vocabulary semantic segmentation which comprises a hierarchical encoder-based cost map generation and a gradual fusion decoder with category early rejection. The hierarchical encoder-based cost map generation employs hierarchical backbone instead of plain transformer to predict pixel-level image-text cost map. Compared to plain transformer hierarchical backbone better captures local spatial information and has linear computational complexity with respect to input size. Our gradual fusion decoder employs a top-down structure to combine cost map and the feature maps of different backbone levels for segmentation. To accelerate inference speed we introduce a category early rejection scheme in the decoder that rejects many no-existing categories at the early layer of decoder resulting in at most 4.7 times acceleration without accuracy degradation. Experiments are performed on multiple open-vocabulary semantic segmentation datasets which demonstrates the efficacy of our SED method. When using ConvNeXt-B our SED method achieves mIoU score of 31.6% on ADE20K with 150 categories at 82 millisecond (ms) per image on a single A6000. Our source code is available at https://github.com/xb534/SED. | https://openaccess.thecvf.com/content/CVPR2024/papers/Xie_SED_A_Simple_Encoder-Decoder_for_Open-Vocabulary_Semantic_Segmentation_CVPR_2024_paper.pdf | http://arxiv.org/abs/2311.15537 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Xie_SED_A_Simple_Encoder-Decoder_for_Open-Vocabulary_Semantic_Segmentation_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Xie_SED_A_Simple_Encoder-Decoder_for_Open-Vocabulary_Semantic_Segmentation_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Xie_SED_A_Simple_CVPR_2024_supplemental.pdf | null |
GPLD3D: Latent Diffusion of 3D Shape Generative Models by Enforcing Geometric and Physical Priors | Yuan Dong, Qi Zuo, Xiaodong Gu, Weihao Yuan, Zhengyi Zhao, Zilong Dong, Liefeng Bo, Qixing Huang | State-of-the-art man-made shape generative models usually adopt established generative models under a suitable implicit shape representation. A common theme is to perform distribution alignment which does not explicitly model important shape priors. As a result many synthetic shapes are not connected. Other synthetic shapes present problems of physical stability and geometric feasibility. This paper introduces a novel latent diffusion shape-generative model regularized by a quality checker that outputs a score of a latent code. The scoring function employs a learned function that provides a geometric feasibility score and a deterministic procedure to quantify a physical stability score. The key to our approach is a new diffusion procedure that combines the discrete empirical data distribution and a continuous distribution induced by the quality checker. We introduce a principled approach to determine the tradeoff parameters for learning the denoising network at different noise levels. Experimental results show that our approach outperforms state-of-the-art shape generations quantitatively and qualitatively on ShapeNet-v2. | https://openaccess.thecvf.com/content/CVPR2024/papers/Dong_GPLD3D_Latent_Diffusion_of_3D_Shape_Generative_Models_by_Enforcing_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Dong_GPLD3D_Latent_Diffusion_of_3D_Shape_Generative_Models_by_Enforcing_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Dong_GPLD3D_Latent_Diffusion_of_3D_Shape_Generative_Models_by_Enforcing_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Dong_GPLD3D_Latent_Diffusion_CVPR_2024_supplemental.pdf | null |
Enhancing Quality of Compressed Images by Mitigating Enhancement Bias Towards Compression Domain | Qunliang Xing, Mai Xu, Shengxi Li, Xin Deng, Meisong Zheng, Huaida Liu, Ying Chen | Existing quality enhancement methods for compressed images focus on aligning the enhancement domain with the raw domain to yield realistic images. However these methods exhibit a pervasive enhancement bias towards the compression domain inadvertently regarding it as more realistic than the raw domain. This bias makes enhanced images closely resemble their compressed counterparts thus degrading their perceptual quality. In this paper we propose a simple yet effective method to mitigate this bias and enhance the quality of compressed images. Our method employs a conditional discriminator with the compressed image as a key condition and then incorporates a domain-divergence regularization to actively distance the enhancement domain from the compression domain. Through this dual strategy our method enables the discrimination against the compression domain and brings the enhancement domain closer to the raw domain. Comprehensive quality evaluations confirm the superiority of our method over other state-of-the-art methods without incurring inference overheads. | https://openaccess.thecvf.com/content/CVPR2024/papers/Xing_Enhancing_Quality_of_Compressed_Images_by_Mitigating_Enhancement_Bias_Towards_CVPR_2024_paper.pdf | http://arxiv.org/abs/2402.17200 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Xing_Enhancing_Quality_of_Compressed_Images_by_Mitigating_Enhancement_Bias_Towards_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Xing_Enhancing_Quality_of_Compressed_Images_by_Mitigating_Enhancement_Bias_Towards_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Xing_Enhancing_Quality_of_CVPR_2024_supplemental.pdf | null |
LangSplat: 3D Language Gaussian Splatting | Minghan Qin, Wanhua Li, Jiawei Zhou, Haoqian Wang, Hanspeter Pfister | Humans live in a 3D world and commonly use natural language to interact with a 3D scene. Modeling a 3D language field to support open-ended language queries in 3D has gained increasing attention recently. This paper introduces LangSplat which constructs a 3D language field that enables precise and efficient open-vocabulary querying within 3D spaces. Unlike existing methods that ground CLIP language embeddings in a NeRF model LangSplat advances the field by utilizing a collection of 3D Gaussians each encoding language features distilled from CLIP to represent the language field. By employing a tile-based splatting technique for rendering language features we circumvent the costly rendering process inherent in NeRF. Instead of directly learning CLIP embeddings LangSplat first trains a scene-wise language autoencoder and then learns language features on the scene-specific latent space thereby alleviating substantial memory demands imposed by explicit modeling. Existing methods struggle with imprecise and vague 3D language fields which fail to discern clear boundaries between objects. We delve into this issue and propose to learn hierarchical semantics using SAM thereby eliminating the need for extensively querying the language field across various scales and the regularization of DINO features. Extensive experimental results show that LangSplat significantly outperforms the previous state-of-the-art method LERF by a large margin. Notably LangSplat is extremely efficient achieving a 199 x speedup compared to LERF at the resolution of 1440 x 1080. We strongly recommend readers to check out our video results at https://langsplat.github.io/. | https://openaccess.thecvf.com/content/CVPR2024/papers/Qin_LangSplat_3D_Language_Gaussian_Splatting_CVPR_2024_paper.pdf | http://arxiv.org/abs/2312.16084 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Qin_LangSplat_3D_Language_Gaussian_Splatting_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Qin_LangSplat_3D_Language_Gaussian_Splatting_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Qin_LangSplat_3D_Language_CVPR_2024_supplemental.pdf | null |
MoST: Multi-Modality Scene Tokenization for Motion Prediction | Norman Mu, Jingwei Ji, Zhenpei Yang, Nate Harada, Haotian Tang, Kan Chen, Charles R. Qi, Runzhou Ge, Kratarth Goel, Zoey Yang, Scott Ettinger, Rami Al-Rfou, Dragomir Anguelov, Yin Zhou | Many existing motion prediction approaches rely on symbolic perception outputs to generate agent trajectories such as bounding boxes road graph information and traffic lights. This symbolic representation is a high-level abstraction of the real world which may render the motion prediction model vulnerable to perception errors (e.g. failures in detecting open-vocabulary obstacles) while missing salient information from the scene context (e.g. poor road conditions). An alternative paradigm is end-to-end learning from raw sensors. However this approach suffers from the lack of interpretability and requires significantly more training resources. In this work we propose tokenizing the visual world into a compact set of scene elements and then leveraging pre-trained image foundation models and LiDAR neural networks to encode all the scene elements in an open-vocabulary manner. The image foundation model enables our scene tokens to encode the general knowledge of the open world while the LiDAR neural network encodes geometry information. Our proposed representation can efficiently encode the multi-frame multi-modality observations with a few hundred tokens and is compatible with most transformer-based architectures. To evaluate our method we have augmented Waymo Open Motion Dataset with camera embeddings. Experiments over Waymo Open Motion Dataset show that our approach leads to significant performance improvements over the state-of-the-art. | https://openaccess.thecvf.com/content/CVPR2024/papers/Mu_MoST_Multi-Modality_Scene_Tokenization_for_Motion_Prediction_CVPR_2024_paper.pdf | http://arxiv.org/abs/2404.19531 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Mu_MoST_Multi-Modality_Scene_Tokenization_for_Motion_Prediction_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Mu_MoST_Multi-Modality_Scene_Tokenization_for_Motion_Prediction_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Mu_MoST_Multi-Modality_Scene_CVPR_2024_supplemental.pdf | null |
PIGEON: Predicting Image Geolocations | Lukas Haas, Michal Skreta, Silas Alberti, Chelsea Finn | Planet-scale image geolocalization remains a challenging problem due to the diversity of images originating from anywhere in the world. Although approaches based on vision transformers have made significant progress in geolocalization accuracy success in prior literature is constrained to narrow distributions of images of landmarks and performance has not generalized to unseen places. We present a new geolocalization system that combines semantic geocell creation multi-task contrastive pretraining and a novel loss function. Additionally our work is the first to perform retrieval over location clusters for guess refinements. We train two models for evaluations on street-level data and general-purpose image geolocalization; the first model PIGEON is trained on data from the game of GeoGuessr and is capable of placing over 40% of its guesses within 25 kilometers of the target location globally. We also develop a bot and deploy PIGEON in a blind experiment against humans ranking in the top 0.01% of players. We further challenge one of the world's foremost professional GeoGuessr players to a series of six matches with millions of viewers winning all six games. Our second model PIGEOTTO differs in that it is trained on a dataset of images from Flickr and Wikipedia achieving state-of-the-art results on a wide range of image geolocalization benchmarks outperforming the previous SOTA by up to 7.7 percentage points on the city accuracy level and up to 38.8 percentage points on the country level. Our findings suggest that PIGEOTTO is the first image geolocalization model that effectively generalizes to unseen places and that our approach can pave the way for highly accurate planet-scale image geolocalization systems. Our code is available on GitHub. | https://openaccess.thecvf.com/content/CVPR2024/papers/Haas_PIGEON_Predicting_Image_Geolocations_CVPR_2024_paper.pdf | http://arxiv.org/abs/2307.05845 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Haas_PIGEON_Predicting_Image_Geolocations_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Haas_PIGEON_Predicting_Image_Geolocations_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Haas_PIGEON_Predicting_Image_CVPR_2024_supplemental.pdf | null |
Improving Spectral Snapshot Reconstruction with Spectral-Spatial Rectification | Jiancheng Zhang, Haijin Zeng, Yongyong Chen, Dengxiu Yu, Yin-Ping Zhao | How to effectively utilize the spectral and spatial characteristics of Hyperspectral Image (HSI) is always a key problem in spectral snapshot reconstruction. Recently the spectra-wise transformer has shown great potential in capturing inter-spectra similarities of HSI but the classic design of the transformer i.e. multi-head division in the spectral (channel) dimension hinders the modeling of global spectral information and results in mean effect. In addition previous methods adopt the normal spatial priors without taking imaging processes into account and fail to address the unique spatial degradation in snapshot spectral reconstruction. In this paper we analyze the influence of multi-head division and propose a novel Spectral-Spatial Rectification (SSR) method to enhance the utilization of spectral information and improve spatial degradation. Specifically SSR includes two core parts: Window-based Spectra-wise Self-Attention (WSSA) and spAtial Rectification Block (ARB). WSSA is proposed to capture global spectral information and account for local differences whereas ARB aims to mitigate the spatial degradation using a spatial alignment strategy. The experimental results on simulation and real scenes demonstrate the effectiveness of the proposed modules and we also provide models at multiple scales to demonstrate the superiority of our approach. | https://openaccess.thecvf.com/content/CVPR2024/papers/Zhang_Improving_Spectral_Snapshot_Reconstruction_with_Spectral-Spatial_Rectification_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Zhang_Improving_Spectral_Snapshot_Reconstruction_with_Spectral-Spatial_Rectification_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Zhang_Improving_Spectral_Snapshot_Reconstruction_with_Spectral-Spatial_Rectification_CVPR_2024_paper.html | CVPR 2024 | null | null |
Self-correcting LLM-controlled Diffusion Models | null | null | null | null | null | https://openaccess.thecvf.com/content/CVPR2024/html/Wu_Self-correcting_LLM-controlled_Diffusion_Models_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Wu_Self-correcting_LLM-controlled_Diffusion_Models_CVPR_2024_paper.html | CVPR 2024 | null | null |
PACER+: On-Demand Pedestrian Animation Controller in Driving Scenarios | Jingbo Wang, Zhengyi Luo, Ye Yuan, Yixuan Li, Bo Dai | We address the challenge of content diversity and controllability in pedestrian simulation for driving scenarios. Recent pedestrian animation frameworks have a significant limitation wherein they primarily focus on either following trajectory or the content of the reference video consequently overlooking the potential diversity of human motion within such scenarios. This limitation restricts the ability to generate pedestrian behaviors that exhibit a wider range of variations and realistic motions and therefore restricts its usage to provide rich motion content for other components in the driving simulation system e.g. suddenly changed motion to which the autonomous vehicle should respond. In our approach we strive to surpass the limitation by showcasing diverse human motions obtained from various sources such as generated human motions in addition to following the given trajectory. The fundamental contribution of our framework lies in combining the motion tracking task with trajectory following which enables the tracking of specific motion parts (e.g. upper body) while simultaneously following the given trajectory by a single policy. This way we significantly enhance both the diversity of simulated human motion within the given scenario and the controllability of the content including language-based control. Our framework facilitates the generation of a wide range of human motions contributing to greater realism and adaptability in pedestrian simulations for driving scenarios. | https://openaccess.thecvf.com/content/CVPR2024/papers/Wang_PACER_On-Demand_Pedestrian_Animation_Controller_in_Driving_Scenarios_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Wang_PACER_On-Demand_Pedestrian_Animation_Controller_in_Driving_Scenarios_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Wang_PACER_On-Demand_Pedestrian_Animation_Controller_in_Driving_Scenarios_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Wang_PACER_On-Demand_Pedestrian_CVPR_2024_supplemental.pdf | null |
LTM: Lightweight Textured Mesh Extraction and Refinement of Large Unbounded Scenes for Efficient Storage and Real-time Rendering | Jaehoon Choi, Rajvi Shah, Qinbo Li, Yipeng Wang, Ayush Saraf, Changil Kim, Jia-Bin Huang, Dinesh Manocha, Suhib Alsisan, Johannes Kopf | Advancements in neural signed distance fields (SDFs) have enabled modeling 3D surface geometry from a set of 2D images of real-world scenes. Baking neural SDFs can extract explicit mesh with appearance baked into texture maps as neural features. The baked meshes still have a large memory footprint and require a powerful GPU for real-time rendering. Neural optimization of such large meshes with differentiable rendering pose significant challenges. We propose a method to produce optimized meshes for large unbounded scenes with low triangle budget and high fidelity of geometry and appearance. We achieve this by combining advancements in baking neural SDFs with classical mesh simplification techniques and proposing a joint appearance-geometry refinement step. The visual quality is comparable to or better than state-of-the-art neural meshing and baking methods with high geometric accuracy despite significant reduction in triangle count making the produced meshes efficient for storage transmission and rendering on mobile hardware. We validate the effectiveness of the proposed method on large unbounded scenes from mip-NeRF 360 Tanks & Temples and Deep Blending datasets achieving at-par rendering quality with 73x reduced triangles and 11x reduction in memory footprint. | https://openaccess.thecvf.com/content/CVPR2024/papers/Choi_LTM_Lightweight_Textured_Mesh_Extraction_and_Refinement_of_Large_Unbounded_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Choi_LTM_Lightweight_Textured_Mesh_Extraction_and_Refinement_of_Large_Unbounded_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Choi_LTM_Lightweight_Textured_Mesh_Extraction_and_Refinement_of_Large_Unbounded_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Choi_LTM_Lightweight_Textured_CVPR_2024_supplemental.pdf | null |
Don't Drop Your Samples! Coherence-Aware Training Benefits Conditional Diffusion | Nicolas Dufour, Victor Besnier, Vicky Kalogeiton, David Picard | Conditional diffusion models are powerful generative models that can leverage various types of conditional information such as class labels segmentation masks or text captions. However in many real-world scenarios conditional information may be noisy or unreliable due to human annotation errors or weak alignment. In this paper we propose the Coherence-Aware Diffusion (CAD) a novel method to integrate confidence in conditional information into diffusion models allowing them to learn from noisy annotations without discarding data. We assume that each data point has an associated confidence score that reflects the quality of the conditional information. We then condition the diffusion model on both the conditional information and the confidence score. In this way the model learns to ignore or discount the conditioning when the confidence is low. We show that our method is theoretically sound and empirically effective on various conditional generation tasks. Moreover we show that leveraging confidence generates realistic and diverse samples that respect conditional information better than models trained on cleaned datasets where samples with low confidence have been discarded. | https://openaccess.thecvf.com/content/CVPR2024/papers/Dufour_Dont_Drop_Your_Samples_Coherence-Aware_Training_Benefits_Conditional_Diffusion_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Dufour_Dont_Drop_Your_Samples_Coherence-Aware_Training_Benefits_Conditional_Diffusion_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Dufour_Dont_Drop_Your_Samples_Coherence-Aware_Training_Benefits_Conditional_Diffusion_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Dufour_Dont_Drop_Your_CVPR_2024_supplemental.pdf | null |
Flow-Guided Online Stereo Rectification for Wide Baseline Stereo | Anush Kumar, Fahim Mannan, Omid Hosseini Jafari, Shile Li, Felix Heide | Stereo rectification is widely considered "solved" due to the abundance of traditional approaches to perform rectification. However autonomous vehicles and robots in-the-wild require constant re-calibration due to exposure to various environmental factors including vibration and structural stress when cameras are arranged in a wide-baseline configuration. Conventional rectification methods fail in these challenging scenarios: especially for larger vehicles such as autonomous freight trucks and semi-trucks the resulting incorrect rectification severely affects the quality of downstream tasks that use stereo/multi-view data. To tackle these challenges we propose an online rectification approach that operates at real-time rates while achieving high accuracy. We propose a novel learning-based online calibration approach that utilizes stereo correlation volumes built from a feature representation obtained from cross-image attention. Our model is trained to minimize vertical optical flow as proxy rectification constraint and predicts the relative rotation between the stereo pair. The method is real-time and even outperforms conventional methods used for offline calibration and substantially improves downstream stereo depth post-rectification. We release two public datasets (https://light.princeton.edu/online-stereo-recification/) a synthetic and experimental wide baseline dataset to foster further research. | https://openaccess.thecvf.com/content/CVPR2024/papers/Kumar_Flow-Guided_Online_Stereo_Rectification_for_Wide_Baseline_Stereo_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Kumar_Flow-Guided_Online_Stereo_Rectification_for_Wide_Baseline_Stereo_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Kumar_Flow-Guided_Online_Stereo_Rectification_for_Wide_Baseline_Stereo_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Kumar_Flow-Guided_Online_Stereo_CVPR_2024_supplemental.pdf | null |
DNGaussian: Optimizing Sparse-View 3D Gaussian Radiance Fields with Global-Local Depth Normalization | Jiahe Li, Jiawei Zhang, Xiao Bai, Jin Zheng, Xin Ning, Jun Zhou, Lin Gu | Radiance fields have demonstrated impressive performance in synthesizing novel views from sparse input views yet prevailing methods suffer from high training costs and slow inference speed. This paper introduces DNGaussian a depth-regularized framework based on 3D Gaussian radiance fields offering real-time and high-quality few-shot novel view synthesis at low costs. Our motivation stems from the highly efficient representation and surprising quality of the recent 3D Gaussian Splatting despite it will encounter a geometry degradation when input views decrease. In the Gaussian radiance fields we find this degradation in scene geometry primarily lined to the positioning of Gaussian primitives and can be mitigated by depth constraint. Consequently we propose a Hard and Soft Depth Regularization to restore accurate scene geometry under coarse monocular depth supervision while maintaining a fine-grained color appearance. To further refine detailed geometry reshaping we introduce Global-Local Depth Normalization enhancing the focus on small local depth changes. Extensive experiments on LLFF DTU and Blender datasets demonstrate that DNGaussian outperforms state-of-the-art methods achieving comparable or better results with significantly reduced memory cost a 25x reduction in training time and over 3000x faster rendering speed. Code is available at: https://github.com/Fictionarry/DNGaussian | https://openaccess.thecvf.com/content/CVPR2024/papers/Li_DNGaussian_Optimizing_Sparse-View_3D_Gaussian_Radiance_Fields_with_Global-Local_Depth_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.06912 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Li_DNGaussian_Optimizing_Sparse-View_3D_Gaussian_Radiance_Fields_with_Global-Local_Depth_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Li_DNGaussian_Optimizing_Sparse-View_3D_Gaussian_Radiance_Fields_with_Global-Local_Depth_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Li_DNGaussian_Optimizing_Sparse-View_CVPR_2024_supplemental.pdf | null |
ColorPCR: Color Point Cloud Registration with Multi-Stage Geometric-Color Fusion | Juncheng Mu, Lin Bie, Shaoyi Du, Yue Gao | Point cloud registration is still a challenging and open problem. For example when the overlap between two point clouds is extremely low geo-only features may be not sufficient. Therefore it is important to further explore how to utilize color data in this task. Under such circumstances we propose ColorPCR for color point cloud registration with multi-stage geometric-color fusion. We design a Hierarchical Color Enhanced Feature Extraction module to extract multi-level geometric-color features and a GeoColor Superpoint Matching Module to encode transformation-invariant geo-color global context for robust patch correspondences. In this way both geometric and color data can be used thus lead to robust performance even under extremely challenging scenarios such as low overlap between two point clouds. To evaluate the performance of our method we colorize 3DMatch/3DLoMatch datasets as Color3DMatch/Color3DLoMatch and evaluations on these datasets demonstrate the effectiveness of our proposed method. Our method achieves state-of-the-art registration recall of 97.5%/88.9% on them. | https://openaccess.thecvf.com/content/CVPR2024/papers/Mu_ColorPCR_Color_Point_Cloud_Registration_with_Multi-Stage_Geometric-Color_Fusion_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Mu_ColorPCR_Color_Point_Cloud_Registration_with_Multi-Stage_Geometric-Color_Fusion_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Mu_ColorPCR_Color_Point_Cloud_Registration_with_Multi-Stage_Geometric-Color_Fusion_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Mu_ColorPCR_Color_Point_CVPR_2024_supplemental.pdf | null |
HomoFormer: Homogenized Transformer for Image Shadow Removal | Jie Xiao, Xueyang Fu, Yurui Zhu, Dong Li, Jie Huang, Kai Zhu, Zheng-Jun Zha | The spatial non-uniformity and diverse patterns of shadow degradation conflict with the weight sharing manner of dominant models which may lead to an unsatisfactory compromise. To tackle with this issue we present a novel strategy from the view of shadow transformation in this paper: directly homogenizing the spatial distribution of shadow degradation. Our key design is the random shuffle operation and its corresponding inverse operation. Specifically random shuffle operation stochastically rearranges the pixels across spatial space and the inverse operation recovers the original order. After randomly shuffling the shadow diffuses in the whole image and the degradation appears in a homogenized way which can be effectively processed by the local self-attention layer. Moreover we further devise a new feed forward network with position modeling to exploit image structural information. Based on these elements we construct the final local window based transformer named HomoFormer for image shadow removal. Our HomoFormer can enjoy the linear complexity of local transformers while bypassing challenges of non-uniformity and diversity of shadow. Extensive experiments are conducted to verify the superiority of our HomoFormer across public datasets. | https://openaccess.thecvf.com/content/CVPR2024/papers/Xiao_HomoFormer_Homogenized_Transformer_for_Image_Shadow_Removal_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Xiao_HomoFormer_Homogenized_Transformer_for_Image_Shadow_Removal_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Xiao_HomoFormer_Homogenized_Transformer_for_Image_Shadow_Removal_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Xiao_HomoFormer_Homogenized_Transformer_CVPR_2024_supplemental.pdf | null |
What If the TV Was Off? Examining Counterfactual Reasoning Abilities of Multi-modal Language Models | Letian Zhang, Xiaotong Zhai, Zhongkai Zhao, Yongshuo Zong, Xin Wen, Bingchen Zhao | Counterfactual reasoning a fundamental aspect of human cognition involves contemplating alternatives to established facts or past events significantly enhancing our abilities in planning and decision-making. In light of the advancements in current multi-modal large language models we explore their effectiveness in counterfactual reasoning. To facilitate this investigation we introduce a novel dataset C-VQA specifically designed to examine the counterfactual reasoning capabilities of modern multi-modal large language models. This dataset is constructed by infusing original questions with counterfactual presuppositions spanning various types such as numerical and boolean queries. It encompasses a mix of real and synthetic data representing a wide range of difficulty levels. Our thorough evaluations of contemporary vision-language models using this dataset have revealed substantial performance drops with some models showing up to a 40% decrease highlighting a significant gap between current models and human-like vision reasoning capabilities. We hope our dataset will serve as a vital benchmark for evaluating the counterfactual reasoning capabilities of models. Code and dataset are publicly available at https://bzhao.me/C-VQA/. | https://openaccess.thecvf.com/content/CVPR2024/papers/Zhang_What_If_the_TV_Was_Off_Examining_Counterfactual_Reasoning_Abilities_CVPR_2024_paper.pdf | http://arxiv.org/abs/2310.06627 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Zhang_What_If_the_TV_Was_Off_Examining_Counterfactual_Reasoning_Abilities_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Zhang_What_If_the_TV_Was_Off_Examining_Counterfactual_Reasoning_Abilities_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Zhang_What_If_the_CVPR_2024_supplemental.pdf | null |
What Do You See in Vehicle? Comprehensive Vision Solution for In-Vehicle Gaze Estimation | Yihua Cheng, Yaning Zhu, Zongji Wang, Hongquan Hao, Yongwei Liu, Shiqing Cheng, Xi Wang, Hyung Jin Chang | Driver's eye gaze holds a wealth of cognitive and intentional cues crucial for intelligent vehicles. Despite its significance research on in-vehicle gaze estimation remains limited due to the scarcity of comprehensive and well-annotated datasets in real driving scenarios. In this paper we present three novel elements to advance in-vehicle gaze research. Firstly we introduce IVGaze a pioneering dataset capturing in-vehicle gaze collected from 125 individuals and covering a large range of gaze and head within vehicles. Conventional gaze collection systems are inadequate for in-vehicle use. In this dataset we propose a new vision-based solution for in-vehicle gaze collection introducing a refined gaze target calibration method to tackle annotation challenges. Second our research focuses on in-vehicle gaze estimation leveraging the IVGaze. Images of in-vehicle faces often suffer from low resolution prompting our introduction of a gaze pyramid transformer that harnesses transformer-based multilevel features integration. Expanding upon this we introduce the dual-stream gaze pyramid transformer (GazeDPTR). Employing perspective transformation we rotate virtual cameras to normalize images utilizing camera pose to merge normalized and original images for accurate gaze estimation. GazeDPTR showcases state-of-the-art performance on the IVGaze dataset. Thirdly we explore a novel strategy for gaze zone classification by extending the GazeDPTR. A foundational tri-plane and project gaze onto these planes are newly defined. Leveraging both positional features from the projection points and visual attributes from images we achieve superior performance compared to relying solely on visual features thereby substantiating the advantage of gaze estimation. The project is available at https://yihua.zone/work/ivgaze | https://openaccess.thecvf.com/content/CVPR2024/papers/Cheng_What_Do_You_See_in_Vehicle_Comprehensive_Vision_Solution_for_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.15664 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Cheng_What_Do_You_See_in_Vehicle_Comprehensive_Vision_Solution_for_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Cheng_What_Do_You_See_in_Vehicle_Comprehensive_Vision_Solution_for_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Cheng_What_Do_You_CVPR_2024_supplemental.pdf | null |
Driving Everywhere with Large Language Model Policy Adaptation | Boyi Li, Yue Wang, Jiageng Mao, Boris Ivanovic, Sushant Veer, Karen Leung, Marco Pavone | Adapting driving behavior to new environments customs and laws is a long-standing problem in autonomous driving precluding the widespread deployment of autonomous vehicles (AVs). In this paper we present LLaDA a simple yet powerful tool that enables human drivers and autonomous vehicles alike to drive everywhere by adapting their tasks and motion plans to traffic rules in new locations. LLaDA achieves this by leveraging the impressive zero-shot generalizability of large language models (LLMs) in interpreting the traffic rules in the local driver handbook. Through an extensive user study we show that LLaDA's instructions are useful in disambiguating in-the-wild unexpected situations. We also demonstrate LLaDA's ability to adapt AV motion planning policies in real-world datasets; LLaDA outperforms baseline planning approaches on all our metrics. Please check our website for more details: https://boyiliee.github.io/llada. | https://openaccess.thecvf.com/content/CVPR2024/papers/Li_Driving_Everywhere_with_Large_Language_Model_Policy_Adaptation_CVPR_2024_paper.pdf | http://arxiv.org/abs/2402.05932 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Li_Driving_Everywhere_with_Large_Language_Model_Policy_Adaptation_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Li_Driving_Everywhere_with_Large_Language_Model_Policy_Adaptation_CVPR_2024_paper.html | CVPR 2024 | null | null |
UFORecon: Generalizable Sparse-View Surface Reconstruction from Arbitrary and Unfavorable Sets | Youngju Na, Woo Jae Kim, Kyu Beom Han, Suhyeon Ha, Sung-Eui Yoon | Generalizable neural implicit surface reconstruction aims to obtain an accurate underlying geometry given a limited number of multi-view images from unseen scenes. However existing methods select only informative and relevant views using predefined scores for training and testing phases. This constraint renders the model impractical in real-world scenarios where the availability of favorable combinations cannot always be ensured. We introduce and validate a view-combination score to indicate the effectiveness of the input view combination. We observe that previous methods output degenerate solutions under arbitrary and unfavorable sets. Building upon this finding we propose UFORecon a robust view-combination generalizable surface reconstruction framework. To achieve this we apply cross-view matching transformers to model interactions between source images and build correlation frustums to capture global correlations. Additionally we explicitly encode pairwise feature similarities as view-consistent priors. Our proposed framework significantly outperforms previous methods in terms of view-combination generalizability and also in the conventional generalizable protocol trained with favorable view-combinations. The code is available at https://github.com/Youngju-Na/UFORecon. | https://openaccess.thecvf.com/content/CVPR2024/papers/Na_UFORecon_Generalizable_Sparse-View_Surface_Reconstruction_from_Arbitrary_and_Unfavorable_Sets_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.05086 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Na_UFORecon_Generalizable_Sparse-View_Surface_Reconstruction_from_Arbitrary_and_Unfavorable_Sets_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Na_UFORecon_Generalizable_Sparse-View_Surface_Reconstruction_from_Arbitrary_and_Unfavorable_Sets_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Na_UFORecon_Generalizable_Sparse-View_CVPR_2024_supplemental.pdf | null |
FAR: Flexible Accurate and Robust 6DoF Relative Camera Pose Estimation | Chris Rockwell, Nilesh Kulkarni, Linyi Jin, Jeong Joon Park, Justin Johnson, David F. Fouhey | Estimating relative camera poses between images has been a central problem in computer vision. Methods that find correspondences and solve for the fundamental matrix offer high precision in most cases. Conversely methods predicting pose directly using neural networks are more robust to limited overlap and can infer absolute translation scale but at the expense of reduced precision. We show how to combine the best of both methods; our approach yields results that are both precise and robust while also accurately inferring translation scales. At the heart of our model lies a Transformer that (1) learns to balance between solved and learned pose estimations and (2) provides a prior to guide a solver. A comprehensive analysis supports our design choices and demonstrates that our method adapts flexibly to various feature extractors and correspondence estimators showing state-of-the-art performance in 6DoF pose estimation on Matterport3D InteriorNet StreetLearn and Map-free Relocalization. | https://openaccess.thecvf.com/content/CVPR2024/papers/Rockwell_FAR_Flexible_Accurate_and_Robust_6DoF_Relative_Camera_Pose_Estimation_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.03221 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Rockwell_FAR_Flexible_Accurate_and_Robust_6DoF_Relative_Camera_Pose_Estimation_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Rockwell_FAR_Flexible_Accurate_and_Robust_6DoF_Relative_Camera_Pose_Estimation_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Rockwell_FAR_Flexible_Accurate_CVPR_2024_supplemental.pdf | null |
eTraM: Event-based Traffic Monitoring Dataset | Aayush Atul Verma, Bharatesh Chakravarthi, Arpitsinh Vaghela, Hua Wei, Yezhou Yang | Event cameras with their high temporal and dynamic range and minimal memory usage have found applications in various fields. However their potential in static traffic monitoring remains largely unexplored. To facilitate this exploration we present eTraM - a first-of-its-kind fully event-based traffic monitoring dataset. eTraM offers 10 hr of data from different traffic scenarios in various lighting and weather conditions providing a comprehensive overview of real-world situations. Providing 2M bounding box annotations it covers eight distinct classes of traffic participants ranging from vehicles to pedestrians and micro-mobility. eTraM's utility has been assessed using state-of-the-art methods for traffic participant detection including RVT RED and YOLOv8. We quantitatively evaluate the ability of event-based models to generalize on nighttime and unseen scenes. Our findings substantiate the compelling potential of leveraging event cameras for traffic monitoring opening new avenues for research and application. eTraM is available at https://eventbasedvision.github.io/eTraM. | https://openaccess.thecvf.com/content/CVPR2024/papers/Verma_eTraM_Event-based_Traffic_Monitoring_Dataset_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.19976 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Verma_eTraM_Event-based_Traffic_Monitoring_Dataset_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Verma_eTraM_Event-based_Traffic_Monitoring_Dataset_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Verma_eTraM_Event-based_Traffic_CVPR_2024_supplemental.pdf | null |
MoCha-Stereo: Motif Channel Attention Network for Stereo Matching | Ziyang Chen, Wei Long, He Yao, Yongjun Zhang, Bingshu Wang, Yongbin Qin, Jia Wu | Learning-based stereo matching techniques have made significant progress. However existing methods inevitably lose geometrical structure information during the feature channel generation process resulting in edge detail mismatches. In this paper the Motif Channel Attention Stereo Matching Network (MoCha-Stereo) is designed to address this problem. We provide the Motif Channel Correlation Volume (MCCV) to determine more accurate edge matching costs. MCCV is achieved by projecting motif channels which capture common geometric structures in feature channels onto feature maps and cost volumes. In addition edge variations in the reconstruction error map also affect details matching we propose the Reconstruction Error Motif Penalty (REMP) module to further refine the full-resolution disparity estimation. REMP integrates the frequency information of typical channel features from the reconstruction error. MoCha-Stereo ranks 1st on the KITTI-2015 and KITTI-2012 Reflective leaderboards. Our structure also shows excellent performance in Multi-View Stereo. Code is avaliable at https://github.com/ZYangChen/MoCha-Stereo. | https://openaccess.thecvf.com/content/CVPR2024/papers/Chen_MoCha-Stereo_Motif_Channel_Attention_Network_for_Stereo_Matching_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Chen_MoCha-Stereo_Motif_Channel_Attention_Network_for_Stereo_Matching_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Chen_MoCha-Stereo_Motif_Channel_Attention_Network_for_Stereo_Matching_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Chen_MoCha-Stereo_Motif_Channel_CVPR_2024_supplemental.pdf | null |
Koala: Key Frame-Conditioned Long Video-LLM | Reuben Tan, Ximeng Sun, Ping Hu, Jui-hsien Wang, Hanieh Deilamsalehy, Bryan A. Plummer, Bryan Russell, Kate Saenko | Long video question answering is a challenging task that involves recognizing short-term activities and reasoning about their fine-grained relationships. State-of-the-art video Large Language Models (vLLMs) hold promise as a viable solution due to their demonstrated emergent capabilities on new tasks. However despite being trained on millions of short seconds-long videos vLLMs are unable to understand minutes-long videos and accurately answer questions about them. To address this limitation we propose a lightweight and self-supervised approach Key frame-conditioned long video-LLM (Koala) that introduces learnable spatiotemporal queries to adapt pretrained vLLMs for generalizing to longer videos. Our approach introduces two new tokenizers that condition on visual tokens computed from sparse video key frames for understanding short and long video moments. We train our proposed approach on HowTo100M and demonstrate its effectiveness on zero-shot long video understanding benchmarks where it outperforms state-of-the-art large models by 3 - 6% in absolute accuracy across all tasks. Surprisingly we also empirically show that our approach not only helps a pretrained vLLM to understand long videos but also improves its accuracy on short-term action recognition. | https://openaccess.thecvf.com/content/CVPR2024/papers/Tan_Koala_Key_Frame-Conditioned_Long_Video-LLM_CVPR_2024_paper.pdf | http://arxiv.org/abs/2404.04346 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Tan_Koala_Key_Frame-Conditioned_Long_Video-LLM_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Tan_Koala_Key_Frame-Conditioned_Long_Video-LLM_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Tan_Koala_Key_Frame-Conditioned_CVPR_2024_supplemental.pdf | null |
Extend Your Own Correspondences: Unsupervised Distant Point Cloud Registration by Progressive Distance Extension | Quan Liu, Hongzi Zhu, Zhenxi Wang, Yunsong Zhou, Shan Chang, Minyi Guo | Registration of point clouds collected from a pair of distant vehicles provides a comprehensive and accurate 3D view of the driving scenario which is vital for driving safety related applications yet existing literature suffers from the expensive pose label acquisition and the deficiency to generalize to new data distributions. In this paper we propose EYOC an unsupervised distant point cloud registration method that adapts to new point cloud distributions on the fly requiring no global pose labels. The core idea of EYOC is to train a feature extractor in a progressive fashion where in each round the feature extractor trained with near point cloud pairs can label slightly farther point cloud pairs enabling self-supervision on such far point cloud pairs. This process continues until the derived extractor can be used to register distant point clouds. Particularly to enable high-fidelity correspondence label generation we devise an effective spatial filtering scheme to select the most representative correspondences to register a point cloud pair and then utilize the aligned point clouds to discover more correct correspondences. Experiments show that EYOC can achieve comparable performance with state-of-the-art supervised methods at a lower training cost. Moreover it outwits supervised methods regarding generalization performance on new data distributions. | https://openaccess.thecvf.com/content/CVPR2024/papers/Liu_Extend_Your_Own_Correspondences_Unsupervised_Distant_Point_Cloud_Registration_by_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.03532 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Liu_Extend_Your_Own_Correspondences_Unsupervised_Distant_Point_Cloud_Registration_by_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Liu_Extend_Your_Own_Correspondences_Unsupervised_Distant_Point_Cloud_Registration_by_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Liu_Extend_Your_Own_CVPR_2024_supplemental.pdf | null |
HallusionBench: An Advanced Diagnostic Suite for Entangled Language Hallucination and Visual Illusion in Large Vision-Language Models | Tianrui Guan, Fuxiao Liu, Xiyang Wu, Ruiqi Xian, Zongxia Li, Xiaoyu Liu, Xijun Wang, Lichang Chen, Furong Huang, Yaser Yacoob, Dinesh Manocha, Tianyi Zhou | We introduce "HallusionBench" a comprehensive benchmark designed for the evaluation of image-context reasoning. This benchmark presents significant challenges to advanced large visual-language models (LVLMs) such as GPT-4V(ision) Gemini Pro Vision Claude 3 and LLaVA-1.5 by emphasizing nuanced understanding and interpretation of visual data. The benchmark comprises 346 images paired with 1129 questions all meticulously crafted by human experts. We introduce a novel structure for these visual questions designed to establish control groups. This structure enables us to conduct a quantitative analysis of the models' response tendencies logical consistency and various failure modes. In our evaluation on HallusionBench we benchmarked 15 different models highlighting a 31.42% question-pair accuracy achieved by the state-of-the-art GPT-4V. Notably all other evaluated models achieve accuracy below 16%. Moreover our analysis not only highlights the observed failure modes including language hallucination and visual illusion but also deepens an under standing of these pitfalls. Our comprehensive case studies within HallusionBench shed light on the challenges of hallucination and illusion in LVLMs. Based on these insights we suggest potential pathways for their future improvement. The benchmark and codebase can be accessed at https://github.com/tianyilab/HallusionBench. | https://openaccess.thecvf.com/content/CVPR2024/papers/Guan_HallusionBench_An_Advanced_Diagnostic_Suite_for_Entangled_Language_Hallucination_and_CVPR_2024_paper.pdf | http://arxiv.org/abs/2310.14566 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Guan_HallusionBench_An_Advanced_Diagnostic_Suite_for_Entangled_Language_Hallucination_and_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Guan_HallusionBench_An_Advanced_Diagnostic_Suite_for_Entangled_Language_Hallucination_and_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Guan_HallusionBench_An_Advanced_CVPR_2024_supplemental.pdf | null |
ID-like Prompt Learning for Few-Shot Out-of-Distribution Detection | Yichen Bai, Zongbo Han, Bing Cao, Xiaoheng Jiang, Qinghua Hu, Changqing Zhang | Out-of-distribution (OOD) detection methods often exploit auxiliary outliers to train model identifying OOD samples especially discovering challenging outliers from auxiliary outliers dataset to improve OOD detection. However they may still face limitations in effectively distinguishing between the most challenging OOD samples that are much like in-distribution (ID) data i.e. ID-like samples. To this end we propose a novel OOD detection framework that discovers ID-like outliers using CLIP from the vicinity space of the ID samples thus helping to identify these most challenging OOD samples. Then a prompt learning framework is proposed that utilizes the identified ID-like outliers to further leverage the capabilities of CLIP for OOD detection. Benefiting from the powerful CLIP we only need a small number of ID samples to learn the prompts of the model without exposing other auxiliary outlier datasets. By focusing on the most challenging ID-like OOD samples and elegantly exploiting the capabilities of CLIP our method achieves superior few-shot learning performance on various real-world image datasets (e.g. in 4-shot OOD detection on the ImageNet-1k dataset our method reduces the average FPR95 by 12.16% and improves the average AUROC by 2.76% compared to state-of-the-art methods). | https://openaccess.thecvf.com/content/CVPR2024/papers/Bai_ID-like_Prompt_Learning_for_Few-Shot_Out-of-Distribution_Detection_CVPR_2024_paper.pdf | http://arxiv.org/abs/2311.15243 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Bai_ID-like_Prompt_Learning_for_Few-Shot_Out-of-Distribution_Detection_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Bai_ID-like_Prompt_Learning_for_Few-Shot_Out-of-Distribution_Detection_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Bai_ID-like_Prompt_Learning_CVPR_2024_supplemental.pdf | null |
Breathing Life Into Sketches Using Text-to-Video Priors | Rinon Gal, Yael Vinker, Yuval Alaluf, Amit Bermano, Daniel Cohen-Or, Ariel Shamir, Gal Chechik | A sketch is one of the most intuitive and versatile tools humans use to convey their ideas visually. An animated sketch opens another dimension to the expression of ideas and is widely used by designers for a variety of purposes. Animating sketches is a laborious process requiring extensive experience and professional design skills. In this work we present a method that automatically adds motion to a single-subject sketch (hence "breathing life into it") merely by providing a text prompt indicating the desired motion. The output is a short animation provided in vector representation which can be easily edited. Our method does not require extensive training but instead leverages the motion prior of a large pretrained text-to-video diffusion model using a score-distillation loss to guide the placement of strokes. To promote natural and smooth motion and to better preserve the sketch's appearance we model the learned motion through two components. The first governs small local deformations and the second controls global affine transformations. Surprisingly we find that even models that struggle to generate sketch videos on their own can still serve as a useful backbone for animating abstract representations. | https://openaccess.thecvf.com/content/CVPR2024/papers/Gal_Breathing_Life_Into_Sketches_Using_Text-to-Video_Priors_CVPR_2024_paper.pdf | http://arxiv.org/abs/2311.13608 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Gal_Breathing_Life_Into_Sketches_Using_Text-to-Video_Priors_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Gal_Breathing_Life_Into_Sketches_Using_Text-to-Video_Priors_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Gal_Breathing_Life_Into_CVPR_2024_supplemental.pdf | null |
Multi-modal Learning for Geospatial Vegetation Forecasting | Vitus Benson, Claire Robin, Christian Requena-Mesa, Lazaro Alonso, Nuno Carvalhais, José Cortés, Zhihan Gao, Nora Linscheid, Mélanie Weynants, Markus Reichstein | Precise geospatial vegetation forecasting holds potential across diverse sectors including agriculture forestry humanitarian aid and carbon accounting. To leverage the vast availability of satellite imagery for this task various works have applied deep neural networks for predicting multispectral images in photorealistic quality. However the important area of vegetation dynamics has not been thoroughly explored. Our study introduces GreenEarthNet the first dataset specifically designed for high-resolution vegetation forecasting and Contextformer a novel deep learning approach for predicting vegetation greenness from Sentinel 2 satellite images with fine resolution across Europe. Our multi-modal transformer model Contextformer leverages spatial context through a vision backbone and predicts the temporal dynamics on local context patches incorporating meteorological time series in a parameter-efficient manner. The GreenEarthNet dataset features a learned cloud mask and an appropriate evaluation scheme for vegetation modeling. It also maintains compatibility with the existing satellite imagery forecasting dataset EarthNet2021 enabling cross-dataset model comparisons. Our extensive qualitative and quantitative analyses reveal that our methods outperform a broad range of baseline techniques. This includes surpassing previous state-of-the-art models on EarthNet2021 as well as adapted models from time series forecasting and video prediction. To the best of our knowledge this work presents the first models for continental-scale vegetation modeling at fine resolution able to capture anomalies beyond the seasonal cycle thereby paving the way for predicting vegetation health and behaviour in response to climate variability and extremes. We provide open source code and pre-trained weights to reproduce our experimental results under https://github.com/vitusbenson/greenearthnet. | https://openaccess.thecvf.com/content/CVPR2024/papers/Benson_Multi-modal_Learning_for_Geospatial_Vegetation_Forecasting_CVPR_2024_paper.pdf | http://arxiv.org/abs/2303.16198 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Benson_Multi-modal_Learning_for_Geospatial_Vegetation_Forecasting_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Benson_Multi-modal_Learning_for_Geospatial_Vegetation_Forecasting_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Benson_Multi-modal_Learning_for_CVPR_2024_supplemental.pdf | null |
Learning Diffusion Texture Priors for Image Restoration | Tian Ye, Sixiang Chen, Wenhao Chai, Zhaohu Xing, Jing Qin, Ge Lin, Lei Zhu | Diffusion Models have shown remarkable performance in image generation tasks which are capable of generating diverse and realistic image content. When adopting diffusion models for image restoration the crucial challenge lies in how to preserve high-level image fidelity in the randomness diffusion process and generate accurate background structures and realistic texture details. In this paper we propose a general framework and develop a Diffusion Texture Prior Model (DTPM) for image restoration tasks. DTPM explicitly models high-quality texture details through the diffusion process rather than global contextual content. In phase one of the training stage we pre-train DTPM on approximately 55K high-quality image samples after which we freeze most of its parameters. In phase two we insert conditional guidance adapters into DTPM and equip it with an initial predictor thereby facilitating its rapid adaptation to downstream image restoration tasks. Our DTPM could mitigate the randomness of traditional diffusion models by utilizing encapsulated rich and diverse texture knowledge and background structural information provided by the initial predictor during the sampling process. Our comprehensive evaluations of five image restoration tasks demonstrate DTPM's superiority over existing regression and diffusion-based image restoration methods in perceptual quality and its exceptional generalization capabilities. | https://openaccess.thecvf.com/content/CVPR2024/papers/Ye_Learning_Diffusion_Texture_Priors_for_Image_Restoration_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Ye_Learning_Diffusion_Texture_Priors_for_Image_Restoration_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Ye_Learning_Diffusion_Texture_Priors_for_Image_Restoration_CVPR_2024_paper.html | CVPR 2024 | null | null |
Bring Event into RGB and LiDAR: Hierarchical Visual-Motion Fusion for Scene Flow | Hanyu Zhou, Yi Chang, Zhiwei Shi | Single RGB or LiDAR is the mainstream sensor for the challenging scene flow which relies heavily on visual features to match motion features. Compared with single modality existing methods adopt a fusion strategy to directly fuse the cross-modal complementary knowledge in motion space. However these direct fusion methods may suffer the modality gap due to the visual intrinsic heterogeneous nature between RGB and LiDAR thus deteriorating motion features. We discover that event has the homogeneous nature with RGB and LiDAR in both visual and motion spaces. In this work we bring the event as a bridge between RGB and LiDAR and propose a novel hierarchical visual-motion fusion framework for scene flow which explores a homogeneous space to fuse the cross-modal complementary knowledge for physical interpretation. In visual fusion we discover that event has a complementarity (relative v.s. absolute) in luminance space with RGB for high dynamic imaging and has a complementarity (local boundary v.s. global shape) in scene structure space with LiDAR for structure integrity. In motion fusion we figure out that RGB event and LiDAR are complementary (spatial-dense temporal-dense v.s. spatiotemporal-sparse) to each other in correlation space which motivates us to fuse their motion correlations for motion continuity. The proposed hierarchical fusion can explicitly fuse the multimodal knowledge to progressively improve scene flow from visual space to motion space. Extensive experiments have been performed to verify the superiority of the proposed method. | https://openaccess.thecvf.com/content/CVPR2024/papers/Zhou_Bring_Event_into_RGB_and_LiDAR_Hierarchical_Visual-Motion_Fusion_for_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.07432 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Zhou_Bring_Event_into_RGB_and_LiDAR_Hierarchical_Visual-Motion_Fusion_for_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Zhou_Bring_Event_into_RGB_and_LiDAR_Hierarchical_Visual-Motion_Fusion_for_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Zhou_Bring_Event_into_CVPR_2024_supplemental.zip | null |
Entangled View-Epipolar Information Aggregation for Generalizable Neural Radiance Fields | Zhiyuan Min, Yawei Luo, Wei Yang, Yuesong Wang, Yi Yang | Generalizable NeRF can directly synthesize novel views across new scenes eliminating the need for scene-specific retraining in vanilla NeRF. A critical enabling factor in these approaches is the extraction of a generalizable 3D representation by aggregating source-view features. In this paper we propose an Entangled View-Epipolar Information Aggregation method dubbed EVE-NeRF. Different from existing methods that consider cross-view and along-epipolar information independently EVE-NeRF conducts the view-epipolar feature aggregation in an entangled manner by injecting the scene-invariant appearance continuity and geometry consistency priors to the aggregation process. Our approach effectively mitigates the potential lack of inherent geometric and appearance constraint resulting from one-dimensional interactions thus further boosting the 3D representation generalizablity. EVE-NeRF attains state-of-the-art performance across various evaluation scenarios. Extensive experiments demonstate that compared to prevailing single-dimensional aggregation the entangled network excels in the accuracy of 3D scene geometry and appearance reconstruction. Our code is publicly available at https://github.com/tatakai1/EVENeRF. | https://openaccess.thecvf.com/content/CVPR2024/papers/Min_Entangled_View-Epipolar_Information_Aggregation_for_Generalizable_Neural_Radiance_Fields_CVPR_2024_paper.pdf | http://arxiv.org/abs/2311.11845 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Min_Entangled_View-Epipolar_Information_Aggregation_for_Generalizable_Neural_Radiance_Fields_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Min_Entangled_View-Epipolar_Information_Aggregation_for_Generalizable_Neural_Radiance_Fields_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Min_Entangled_View-Epipolar_Information_CVPR_2024_supplemental.pdf | null |
Jack of All Tasks Master of Many: Designing General-Purpose Coarse-to-Fine Vision-Language Model | Shraman Pramanick, Guangxing Han, Rui Hou, Sayan Nag, Ser-Nam Lim, Nicolas Ballas, Qifan Wang, Rama Chellappa, Amjad Almahairi | The ability of large language models (LLMs) to process visual inputs has given rise to general-purpose vision systems unifying various vision-language (VL) tasks by instruction tuning. However due to the enormous diversity in input-output formats in the vision domain existing general-purpose models fail to successfully integrate segmentation and multi-image inputs with coarse-level tasks into a single framework. In this work we introduce VistaLLM a powerful visual system that addresses coarse- and fine grained VL tasks over single and multiple input images using a unified framework. VistaLLM utilizes an instruction-guided image tokenizer that filters global embeddings using task descriptions to extract compressed and refined features from numerous images. Moreover VistaLLM employs a gradient-aware adaptive sampling technique to represent binary segmentation masks as sequences significantly improving over previously used uniform sampling. To bolster the desired capability of VistaLLM we curate CoinIt a comprehensive coarse-to-fine instruction tuning dataset with 6.8M samples. We also address the lack of multi-image grounding datasets by introducing a novel task AttCoSeg (Attribute-level Co Segmentation) which boosts the model's reasoning and grounding capability over multiple input images. Extensive experiments on a wide range of V- and VL tasks demonstrate the effectiveness of VistaLLM by achieving consistent state-of-the-art performance over strong baselines across many downstream tasks. Our project page can be found at https://shramanpramanick.github.io/VistaLLM/ | https://openaccess.thecvf.com/content/CVPR2024/papers/Pramanick_Jack_of_All_Tasks_Master_of_Many_Designing_General-Purpose_Coarse-to-Fine_CVPR_2024_paper.pdf | http://arxiv.org/abs/2312.12423 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Pramanick_Jack_of_All_Tasks_Master_of_Many_Designing_General-Purpose_Coarse-to-Fine_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Pramanick_Jack_of_All_Tasks_Master_of_Many_Designing_General-Purpose_Coarse-to-Fine_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Pramanick_Jack_of_All_CVPR_2024_supplemental.pdf | null |
MMVP: A Multimodal MoCap Dataset with Vision and Pressure Sensors | He Zhang, Shenghao Ren, Haolei Yuan, Jianhui Zhao, Fan Li, Shuangpeng Sun, Zhenghao Liang, Tao Yu, Qiu Shen, Xun Cao | Foot contact is an important cue for human motion capture understanding and generation. Existing datasets tend to annotate dense foot contact using visual matching with thresholding or incorporating pressure signals. However these approaches either suffer from low accuracy or are only designed for small-range and slow motion. There is still a lack of a vision-pressure multimodal dataset with large-range and fast human motion as well as accurate and dense foot-contact annotation. To fill this gap we propose a Multimodal MoCap Dataset with Vision and Pressure sensors named MMVP. MMVP provides accurate and dense plantar pressure signals synchronized with RGBD observations which is especially useful for both plausible shape estimation robust pose fitting without foot drifting and accurate global translation tracking. To validate the dataset we propose an RGBD-P SMPL fitting method and also a monocular-video-based baseline framework VP-MoCap for human motion capture. Experiments demonstrate that our RGBD-P SMPL Fitting results significantly outperform pure visual motion capture. Moreover VP-MoCap outperforms SOTA methods in foot-contact and global translation estimation accuracy. We believe the configuration of the dataset and the baseline frameworks will stimulate the research in this direction and also provide a good reference for MoCap applications in various domains. Project page: https://metaverse-ai-lab-thu.github.io/MMVP-Dataset/. | https://openaccess.thecvf.com/content/CVPR2024/papers/Zhang_MMVP_A_Multimodal_MoCap_Dataset_with_Vision_and_Pressure_Sensors_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.17610 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Zhang_MMVP_A_Multimodal_MoCap_Dataset_with_Vision_and_Pressure_Sensors_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Zhang_MMVP_A_Multimodal_MoCap_Dataset_with_Vision_and_Pressure_Sensors_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Zhang_MMVP_A_Multimodal_CVPR_2024_supplemental.zip | null |
YolOOD: Utilizing Object Detection Concepts for Multi-Label Out-of-Distribution Detection | Alon Zolfi, Guy Amit, Amit Baras, Satoru Koda, Ikuya Morikawa, Yuval Elovici, Asaf Shabtai | Out-of-distribution (OOD) detection has attracted a large amount of attention from the machine learning research community in recent years due to its importance in deployed systems. Most of the previous studies focused on the detection of OOD samples in the multi-class classification task. However OOD detection in the multi-label classification task a more common real-world use case remains an underexplored domain. In this research we propose YolOOD - a method that utilizes concepts from the object detection domain to perform OOD detection in the multi-label classification task. Object detection models have an inherent ability to distinguish between objects of interest (in-distribution data) and irrelevant objects (OOD data) in images that contain multiple objects belonging to different class categories. These abilities allow us to convert a regular object detection model into an image classifier with inherent OOD detection capabilities with just minor changes. We compare our approach to state-of-the-art OOD detection methods and demonstrate YolOOD's ability to outperform these methods on a comprehensive suite of in-distribution and OOD benchmark datasets. | https://openaccess.thecvf.com/content/CVPR2024/papers/Zolfi_YolOOD_Utilizing_Object_Detection_Concepts_for_Multi-Label_Out-of-Distribution_Detection_CVPR_2024_paper.pdf | http://arxiv.org/abs/2212.02081 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Zolfi_YolOOD_Utilizing_Object_Detection_Concepts_for_Multi-Label_Out-of-Distribution_Detection_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Zolfi_YolOOD_Utilizing_Object_Detection_Concepts_for_Multi-Label_Out-of-Distribution_Detection_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Zolfi_YolOOD_Utilizing_Object_CVPR_2024_supplemental.pdf | null |
SchurVINS: Schur Complement-Based Lightweight Visual Inertial Navigation System | Yunfei Fan, Tianyu Zhao, Guidong Wang | Accuracy and computational efficiency are the most important metrics to Visual Inertial Navigation System (VINS). The existing VINS algorithms with either high accuracy or low computational complexity are difficult to provide the high precision localization in resource-constrained devices. To this end we propose a novel filter-based VINS framework named SchurVINS (SV) which could guarantee both high accuracy by building a complete residual model and low computational complexity with Schur complement. Technically we first formulate the full residual model where Gradient Hessian and observation covariance are explicitly modeled. Then Schur complement is employed to decompose the full model into ego-motion residual model and landmark residual model. Finally Extended Kalman Filter (EKF) update is implemented in these two models with high efficiency. Experiments on EuRoC and TUM-VI datasets show that our method notably outperforms state-of-the-art (SOTA) methods in both accuracy and computational complexity. The experimental code of SchurVINS is available at https://github.com/bytedance/SchurVINS. | https://openaccess.thecvf.com/content/CVPR2024/papers/Fan_SchurVINS_Schur_Complement-Based_Lightweight_Visual_Inertial_Navigation_System_CVPR_2024_paper.pdf | http://arxiv.org/abs/2312.01616 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Fan_SchurVINS_Schur_Complement-Based_Lightweight_Visual_Inertial_Navigation_System_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Fan_SchurVINS_Schur_Complement-Based_Lightweight_Visual_Inertial_Navigation_System_CVPR_2024_paper.html | CVPR 2024 | null | null |
Collaborating Foundation Models for Domain Generalized Semantic Segmentation | Yasser Benigmim, Subhankar Roy, Slim Essid, Vicky Kalogeiton, Stéphane Lathuilière | Domain Generalized Semantic Segmentation (DGSS) deals with training a model on a labeled source domain with the aim of generalizing to unseen domains during inference. Existing DGSS methods typically effectuate robust features by means of Domain Randomization (DR). Such an approach is often limited as it can only account for style diversification and not content. In this work we take an orthogonal approach to DGSS and propose to use an assembly of CoLlaborative FOUndation models for Domain Generalized Semantic Segmentation (CLOUDS). In detail CLOUDS is a framework that integrates Foundation Models of various kinds: (i) CLIP backbone for its robust feature representation (ii) Diffusion Model to diversify the content thereby covering various modes of the possible target distribution and (iii) Segment Anything Model (SAM) for iteratively refining the predictions of the segmentation model. Extensive experiments show that our CLOUDS excels in adapting from synthetic to real DGSS benchmarks and under varying weather conditions notably outperforming prior methods by 5.6% and 6.7% on averaged mIoU respectively. Our code is available at https://github.com/yasserben/CLOUDS | https://openaccess.thecvf.com/content/CVPR2024/papers/Benigmim_Collaborating_Foundation_Models_for_Domain_Generalized_Semantic_Segmentation_CVPR_2024_paper.pdf | http://arxiv.org/abs/2312.09788 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Benigmim_Collaborating_Foundation_Models_for_Domain_Generalized_Semantic_Segmentation_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Benigmim_Collaborating_Foundation_Models_for_Domain_Generalized_Semantic_Segmentation_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Benigmim_Collaborating_Foundation_Models_CVPR_2024_supplemental.pdf | null |
Towards Variable and Coordinated Holistic Co-Speech Motion Generation | Yifei Liu, Qiong Cao, Yandong Wen, Huaiguang Jiang, Changxing Ding | This paper addresses the problem of generating lifelike holistic co-speech motions for 3D avatars focusing on two key aspects: variability and coordination. Variability allows the avatar to exhibit a wide range of motions even with similar speech content while coordination ensures a harmonious alignment among facial expressions hand gestures and body poses. We aim to achieve both with ProbTalk a unified probabilistic framework designed to jointly model facial hand and body movements in speech. ProbTalk builds on the variational autoencoder (VAE) architecture and incorporates three core designs. First we introduce product quantization (PQ) to the VAE which enriches the representation of complex holistic motion. Second we devise a novel non-autoregressive model that embeds 2D positional encoding into the product-quantized representation thereby preserving essential structure information of the PQ codes. Last we employ a secondary stage to refine the preliminary prediction further sharpening the high-frequency details. Coupling these three designs enables ProbTalk to generate natural and diverse holistic co-speech motions outperforming several state-of-the-art methods in qualitative and quantitative evaluations particularly in terms of realism. Our code and model will be released for research purposes at https://feifeifeiliu.github.io/probtalk/. | https://openaccess.thecvf.com/content/CVPR2024/papers/Liu_Towards_Variable_and_Coordinated_Holistic_Co-Speech_Motion_Generation_CVPR_2024_paper.pdf | http://arxiv.org/abs/2404.00368 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Liu_Towards_Variable_and_Coordinated_Holistic_Co-Speech_Motion_Generation_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Liu_Towards_Variable_and_Coordinated_Holistic_Co-Speech_Motion_Generation_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Liu_Towards_Variable_and_CVPR_2024_supplemental.zip | null |
JoAPR: Cleaning the Lens of Prompt Learning for Vision-Language Models | Yuncheng Guo, Xiaodong Gu | Leveraging few-shot datasets in prompt learning for Vision-Language Models eliminates the need for manual prompt engineering while highlighting the necessity of accurate annotations for the labels. However high-level or complex label noise challenges prompt learning for Vision-Language Models. Aiming at this issue we propose a new framework for improving its robustness. Specifically we introduce the Joint Adaptive Partitioning for Label Refurbishment (JoAPR) a structured framework encompassing two key steps. 1) Data Partitioning where we differentiate between clean and noisy data using joint adaptive thresholds. 2) Label Refurbishment where we correct the labels based on the partition outcomes before retraining the network. Our comprehensive experiments confirm that JoAPR substantially enhances the robustness of prompt learning for Vision-Language Models against label noise offering a promising direction for future research. | https://openaccess.thecvf.com/content/CVPR2024/papers/Guo_JoAPR_Cleaning_the_Lens_of_Prompt_Learning_for_Vision-Language_Models_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Guo_JoAPR_Cleaning_the_Lens_of_Prompt_Learning_for_Vision-Language_Models_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Guo_JoAPR_Cleaning_the_Lens_of_Prompt_Learning_for_Vision-Language_Models_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Guo_JoAPR_Cleaning_the_CVPR_2024_supplemental.pdf | null |
AllSpark: Reborn Labeled Features from Unlabeled in Transformer for Semi-Supervised Semantic Segmentation | Haonan Wang, Qixiang Zhang, Yi Li, Xiaomeng Li | Semi-supervised semantic segmentation (SSSS) has been proposed to alleviate the burden of time-consuming pixel-level manual labeling which leverages limited labeled data along with larger amounts of unlabeled data. Current state-of-the-art methods train the labeled data with ground truths and unlabeled data with pseudo labels. However the two training flows are separate which allows labeled data to dominate the training process resulting in low-quality pseudo labels and consequently sub-optimal results. To alleviate this issue we present AllSpark which reborns the labeled features from unlabeled ones with the channel-wise cross-attention mechanism. We further introduce a Semantic Memory along with a Channel Semantic Grouping strategy to ensure that unlabeled features adequately represent labeled features. The AllSpark shed new light on the architecture level designs of SSSS rather than framework level which avoids increasingly complicated training pipeline designs. It can also be regarded as a flexible bottleneck module that can be seamlessly integrated into a general transformer-based segmentation model. The proposed AllSpark outperforms existing methods across all evaluation protocols on Pascal Cityscapes and COCO benchmarks without bells-and-whistles. Code and model weights are available at: https://github.com/xmed-lab/AllSpark. | https://openaccess.thecvf.com/content/CVPR2024/papers/Wang_AllSpark_Reborn_Labeled_Features_from_Unlabeled_in_Transformer_for_Semi-Supervised_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.01818 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Wang_AllSpark_Reborn_Labeled_Features_from_Unlabeled_in_Transformer_for_Semi-Supervised_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Wang_AllSpark_Reborn_Labeled_Features_from_Unlabeled_in_Transformer_for_Semi-Supervised_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Wang_AllSpark_Reborn_Labeled_CVPR_2024_supplemental.pdf | null |
Open-Vocabulary 3D Semantic Segmentation with Foundation Models | Li Jiang, Shaoshuai Shi, Bernt Schiele | In dynamic 3D environments the ability to recognize a diverse range of objects without the constraints of predefined categories is indispensable for real-world applications. In response to this need we introduce OV3D an innovative framework designed for open-vocabulary 3D semantic segmentation. OV3D leverages the broad open-world knowledge embedded in vision and language foundation models to establish a fine-grained correspondence between 3D points and textual entity descriptions. These entity descriptions are enriched with contextual information enabling a more open and comprehensive understanding. By seamlessly aligning 3D point features with entity text features OV3D empowers open-vocabulary recognition in the 3D domain achieving state-of-the-art open-vocabulary semantic segmentation performance across multiple datasets including ScanNet Matterport3D and nuScenes. | https://openaccess.thecvf.com/content/CVPR2024/papers/Jiang_Open-Vocabulary_3D_Semantic_Segmentation_with_Foundation_Models_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Jiang_Open-Vocabulary_3D_Semantic_Segmentation_with_Foundation_Models_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Jiang_Open-Vocabulary_3D_Semantic_Segmentation_with_Foundation_Models_CVPR_2024_paper.html | CVPR 2024 | null | null |
SIGNeRF: Scene Integrated Generation for Neural Radiance Fields | Jan-Niklas Dihlmann, Andreas Engelhardt, Hendrik Lensch | Advances in image diffusion models have recently led to notable improvements in the generation of high-quality images. In combination with Neural Radiance Fields (NeRFs) they enabled new opportunities in 3D generation. However most generative 3D approaches are object-centric and applying them to editing existing photorealistic scenes is not trivial. We propose SIGNeRF a novel approach for fast and controllable NeRF scene editing and scene-integrated object generation. A new generative update strategy ensures 3D consistency across the edited images without requiring iterative optimization. We find that depth-conditioned diffusion models inherently possess the capability to generate 3D consistent views by requesting a grid of images instead of single views. Based on these insights we introduce a multi-view reference sheet of modified images. Our method updates an image collection consistently based on the reference sheet and refines the original NeRF with the newly generated image set in one go. By exploiting the depth conditioning mechanism of the image diffusion model we gain fine control over the spatial location of the edit and enforce shape guidance by a selected region or an external mesh. | https://openaccess.thecvf.com/content/CVPR2024/papers/Dihlmann_SIGNeRF_Scene_Integrated_Generation_for_Neural_Radiance_Fields_CVPR_2024_paper.pdf | http://arxiv.org/abs/2401.01647 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Dihlmann_SIGNeRF_Scene_Integrated_Generation_for_Neural_Radiance_Fields_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Dihlmann_SIGNeRF_Scene_Integrated_Generation_for_Neural_Radiance_Fields_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Dihlmann_SIGNeRF_Scene_Integrated_CVPR_2024_supplemental.pdf | null |
ViP-LLaVA: Making Large Multimodal Models Understand Arbitrary Visual Prompts | Mu Cai, Haotian Liu, Siva Karthik Mustikovela, Gregory P. Meyer, Yuning Chai, Dennis Park, Yong Jae Lee | While existing large vision-language multimodal models focus on whole image understanding there is a prominent gap in achieving region-specific comprehension. Current approaches that use textual coordinates or spatial encodings often fail to provide a user-friendly interface for visual prompting. To address this challenge we introduce a novel multimodal model capable of decoding arbitrary (free-form) visual prompts. This allows users to intuitively mark images and interact with the model using natural cues like a "red bounding box" or "pointed arrow'". Our simple design directly overlays visual markers onto the RGB image eliminating the need for complex region encodings yet achieves state-of-the-art performance on region-understanding tasks like Visual7W PointQA and Visual Commonsense Reasoning benchmark. Furthermore we present ViP-Bench a comprehensive benchmark to assess the capability of models in understanding visual prompts across multiple dimensions enabling future research in this domain. Code data and model are publicly available. | https://openaccess.thecvf.com/content/CVPR2024/papers/Cai_ViP-LLaVA_Making_Large_Multimodal_Models_Understand_Arbitrary_Visual_Prompts_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Cai_ViP-LLaVA_Making_Large_Multimodal_Models_Understand_Arbitrary_Visual_Prompts_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Cai_ViP-LLaVA_Making_Large_Multimodal_Models_Understand_Arbitrary_Visual_Prompts_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Cai_ViP-LLaVA_Making_Large_CVPR_2024_supplemental.pdf | null |
OVER-NAV: Elevating Iterative Vision-and-Language Navigation with Open-Vocabulary Detection and StructurEd Representation | Ganlong Zhao, Guanbin Li, Weikai Chen, Yizhou Yu | Recent advances in Iterative Vision-and-Language Navigation(IVLN) introduce a more meaningful and practical paradigm of VLN by maintaining the agent's memory across tours of scenes. Although the long-term memory aligns better with the persistent nature of the VLN task it poses more challenges on how to utilize the highly unstructured navigation memory with extremely sparse supervision. Towards this end we propose OVER-NAV which aims to go over and beyond the current arts of IVLN techniques. In particular we propose to incorporate LLMs and open-vocabulary detectors to distill key information and establish correspondence between multi-modal signals. Such a mechanism introduces reliable cross-modal supervision and enables on-the-fly generalization to unseen scenes without the need of extra annotation and re-training. To fully exploit the interpreted navigation data we further introduce a structured representation coded Omnigraph to effectively integrate multi-modal information along the tour. Accompanied with a novel omnigraph fusion mechanism OVER-NAV is able to extract the most relevant knowledge from omnigraph for a more accurate navigating action. In addition OVER-NAV seamlessly supports both discrete and continuous environments under a unified framework. We demonstrate the superiority of OVER-NAV in extensive experiments. | https://openaccess.thecvf.com/content/CVPR2024/papers/Zhao_OVER-NAV_Elevating_Iterative_Vision-and-Language_Navigation_with_Open-Vocabulary_Detection_and_StructurEd_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Zhao_OVER-NAV_Elevating_Iterative_Vision-and-Language_Navigation_with_Open-Vocabulary_Detection_and_StructurEd_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Zhao_OVER-NAV_Elevating_Iterative_Vision-and-Language_Navigation_with_Open-Vocabulary_Detection_and_StructurEd_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Zhao_OVER-NAV_Elevating_Iterative_CVPR_2024_supplemental.pdf | null |
1-Lipschitz Layers Compared: Memory Speed and Certifiable Robustness | Bernd Prach, Fabio Brau, Giorgio Buttazzo, Christoph H. Lampert | The robustness of neural networks against input perturbations with bounded magnitude represents a serious concern in the deployment of deep learning models in safety-critical systems. Recently the scientific community has focused on enhancing certifiable robustness guarantees by crafting \ols neural networks that leverage Lipschitz bounded dense and convolutional layers. Different methods have been proposed in the literature to achieve this goal however comparing the performance of such methods is not straightforward since different metrics can be relevant (e.g. training time memory usage accuracy certifiable robustness) for different applications. Therefore this work provides a thorough comparison between different methods covering theoretical aspects such as computational complexity and memory requirements as well as empirical measurements of time per epoch required memory accuracy and certifiable robust accuracy. The paper also provides some guidelines and recommendations to support the user in selecting the methods that work best depending on the available resources. We provide code at github.com/berndprach/1LipschitzLayersCompared | https://openaccess.thecvf.com/content/CVPR2024/papers/Prach_1-Lipschitz_Layers_Compared_Memory_Speed_and_Certifiable_Robustness_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Prach_1-Lipschitz_Layers_Compared_Memory_Speed_and_Certifiable_Robustness_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Prach_1-Lipschitz_Layers_Compared_Memory_Speed_and_Certifiable_Robustness_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Prach_1-Lipschitz_Layers_Compared_CVPR_2024_supplemental.pdf | null |
All Rivers Run to the Sea: Private Learning with Asymmetric Flows | Yue Niu, Ramy E. Ali, Saurav Prakash, Salman Avestimehr | Data privacy is of great concern in cloud machine-learning service platforms when sensitive data are exposed to service providers. While private computing environments (e.g. secure enclaves) and cryptographic approaches (e.g. homomorphic encryption) provide strong privacy protection their computing performance still falls short compared to cloud GPUs. To achieve privacy protection with high computing performance we propose Delta a new private training and inference framework with comparable model performance as non-private centralized training. Delta features two asymmetric data flows: the main information-sensitive flow and the residual flow. The main part flows into a small model while the residuals are offloaded to a large model. Specifically Delta embeds the information-sensitive representations into a low-dimensional space while pushing the information-insensitive part into high-dimension residuals. To ensure privacy protection the low-dimensional information-sensitive part is secured and fed to a small model in a private environment. On the other hand the residual part is sent to fast cloud GPUs and processed by a large model. To further enhance privacy and reduce the communication cost Delta applies a random binary quantization technique along with a DP-based technique to the residuals before sharing them with the public platform. We theoretically show that Delta guarantees differential privacy in the public environment and greatly reduces the complexity in the private environment. We conduct empirical analyses on CIFAR-10 CIFAR-100 and ImageNet datasets and ResNet-18 and ResNet-34 showing that Delta achieves strong privacy protection fast training and inference without significantly compromising the model utility. | https://openaccess.thecvf.com/content/CVPR2024/papers/Niu_All_Rivers_Run_to_the_Sea_Private_Learning_with_Asymmetric_CVPR_2024_paper.pdf | http://arxiv.org/abs/2312.05264 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Niu_All_Rivers_Run_to_the_Sea_Private_Learning_with_Asymmetric_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Niu_All_Rivers_Run_to_the_Sea_Private_Learning_with_Asymmetric_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Niu_All_Rivers_Run_CVPR_2024_supplemental.pdf | null |
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