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BOTH2Hands: Inferring 3D Hands from Both Text Prompts and Body Dynamics | Wenqian Zhang, Molin Huang, Yuxuan Zhou, Juze Zhang, Jingyi Yu, Jingya Wang, Lan Xu | The recently emerging text-to-motion advances have spired numerous attempts for convenient and interactive human motion generation. Yet existing methods are largely limited to generating body motions only without considering the rich two-hand motions let alone handling various conditions like body dynamics or texts. To break the data bottleneck we propose BOTH57M a novel multi-modal dataset for two-hand motion generation. Our dataset includes accurate motion tracking for the human body and hands and provides pair-wised finger-level hand annotations and body descriptions. We further provide a strong baseline method BOTH2Hands for the novel task: generating vivid two-hand motions from both implicit body dynamics and explicit text prompts. We first warm up two parallel body-to-hand and text-to-hand diffusion models and then utilize the cross-attention transformer for motion blending. Extensive experiments and cross-validations demonstrate the effectiveness of our approach and dataset for generating convincing two-hand motions from the hybrid body-and-textual conditions. Our dataset and code will be disseminated to the community for future research which can be found at https://github.com/Godheritage/BOTH2Hands. | https://openaccess.thecvf.com/content/CVPR2024/papers/Zhang_BOTH2Hands_Inferring_3D_Hands_from_Both_Text_Prompts_and_Body_CVPR_2024_paper.pdf | http://arxiv.org/abs/2312.07937 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Zhang_BOTH2Hands_Inferring_3D_Hands_from_Both_Text_Prompts_and_Body_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Zhang_BOTH2Hands_Inferring_3D_Hands_from_Both_Text_Prompts_and_Body_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Zhang_BOTH2Hands_Inferring_3D_CVPR_2024_supplemental.pdf | null |
EpiDiff: Enhancing Multi-View Synthesis via Localized Epipolar-Constrained Diffusion | Zehuan Huang, Hao Wen, Junting Dong, Yaohui Wang, Yangguang Li, Xinyuan Chen, Yan-Pei Cao, Ding Liang, Yu Qiao, Bo Dai, Lu Sheng | Generating multiview images from a single view facilitates the rapid generation of a 3D mesh conditioned on a single image. Recent methods that introduce 3D global representation into diffusion models have shown the potential to generate consistent multiviews but they have reduced generation speed and face challenges in maintaining generalizability and quality. To address this issue we propose EpiDiff a localized interactive multiview diffusion model. At the core of the proposed approach is to insert a lightweight epipolar attention block into the frozen diffusion model leveraging epipolar constraints to enable cross-view interaction among feature maps of neighboring views. The newly initialized 3D modeling module preserves the original feature distribution of the diffusion model exhibiting compatibility with a variety of base diffusion models. Experiments show that EpiDiff generates 16 multiview images in just 12 seconds and it surpasses previous methods in quality evaluation metrics including PSNR SSIM and LPIPS. Additionally EpiDiff can generate a more diverse distribution of views improving the reconstruction quality from generated multiviews. Please see the project page at https://huanngzh.github.io/EpiDiff/. | https://openaccess.thecvf.com/content/CVPR2024/papers/Huang_EpiDiff_Enhancing_Multi-View_Synthesis_via_Localized_Epipolar-Constrained_Diffusion_CVPR_2024_paper.pdf | http://arxiv.org/abs/2312.06725 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Huang_EpiDiff_Enhancing_Multi-View_Synthesis_via_Localized_Epipolar-Constrained_Diffusion_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Huang_EpiDiff_Enhancing_Multi-View_Synthesis_via_Localized_Epipolar-Constrained_Diffusion_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Huang_EpiDiff_Enhancing_Multi-View_CVPR_2024_supplemental.zip | null |
On the Faithfulness of Vision Transformer Explanations | Junyi Wu, Weitai Kang, Hao Tang, Yuan Hong, Yan Yan | To interpret Vision Transformers post-hoc explanations assign salience scores to input pixels providing human-understandable heatmaps. However whether these interpretations reflect true rationales behind the model's output is still underexplored. To address this gap we study the faithfulness criterion of explanations: the assigned salience scores should represent the influence of the corresponding input pixels on the model's predictions. To evaluate faithfulness we introduce Salience-guided Faithfulness Coefficient (SaCo) a novel evaluation metric leveraging essential information of salience distribution. Specifically we conduct pair-wise comparisons among distinct pixel groups and then aggregate the differences in their salience scores resulting in a coefficient that indicates the explanation's degree of faithfulness. Our explorations reveal that current metrics struggle to differentiate between advanced explanation methods and Random Attribution thereby failing to capture the faithfulness property. In contrast our proposed SaCo offers a reliable faithfulness measurement establishing a robust metric for interpretations. Furthermore our SaCo demonstrates that the use of gradient and multi-layer aggregation can markedly enhance the faithfulness of attention-based explanation shedding light on potential paths for advancing Vision Transformer explainability. | https://openaccess.thecvf.com/content/CVPR2024/papers/Wu_On_the_Faithfulness_of_Vision_Transformer_Explanations_CVPR_2024_paper.pdf | http://arxiv.org/abs/2404.01415 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Wu_On_the_Faithfulness_of_Vision_Transformer_Explanations_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Wu_On_the_Faithfulness_of_Vision_Transformer_Explanations_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Wu_On_the_Faithfulness_CVPR_2024_supplemental.pdf | null |
Pixel-level Semantic Correspondence through Layout-aware Representation Learning and Multi-scale Matching Integration | Yixuan Sun, Zhangyue Yin, Haibo Wang, Yan Wang, Xipeng Qiu, Weifeng Ge, Wenqiang Zhang | Establishing precise semantic correspondence across object instances in different images is a fundamental and challenging task in computer vision. In this task difficulty arises often due to three challenges: confusing regions with similar appearance inconsistent object scale and indistinguishable nearby pixels. Recognizing these challenges our paper proposes a novel semantic matching pipeline named LPMFlow toward extracting fine-grained semantics and geometry layouts for building pixel-level semantic correspondences. LPMFlow consists of three modules each addressing one of the aforementioned challenges. The layout-aware representation learning module uniformly encodes source and target tokens to distinguish pixels or regions with similar appearances but different geometry semantics. The progressive feature superresolution module outputs four sets of 4D correlation tensors to generate accurate semantic flow between objects in different scales. Finally the matching flow integration and refinement module is exploited to fuse matching flow in different scales to give the final flow predictions. The whole pipeline can be trained end-to-end with a balance of computational cost and correspondence details. Extensive experiments based on benchmarks such as SPair-71K PF-PASCAL and PF-WILLOW have proved that the proposed method can well tackle the three challenges and outperform the previous methods especially in more stringent settings. Code is available at https://github.com/YXSUNMADMAX/LPMFlow. | https://openaccess.thecvf.com/content/CVPR2024/papers/Sun_Pixel-level_Semantic_Correspondence_through_Layout-aware_Representation_Learning_and_Multi-scale_Matching_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Sun_Pixel-level_Semantic_Correspondence_through_Layout-aware_Representation_Learning_and_Multi-scale_Matching_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Sun_Pixel-level_Semantic_Correspondence_through_Layout-aware_Representation_Learning_and_Multi-scale_Matching_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Sun_Pixel-level_Semantic_Correspondence_CVPR_2024_supplemental.pdf | null |
Learning Spatial Features from Audio-Visual Correspondence in Egocentric Videos | Sagnik Majumder, Ziad Al-Halah, Kristen Grauman | We propose a self-supervised method for learning representations based on spatial audio-visual correspondences in egocentric videos. Our method uses a masked auto-encoding framework to synthesize masked binaural audio through the synergy of audio and vision thereby learning useful spatial relationships between the two modalities. We use our pretrained features to tackle two downstream video tasks requiring spatial understanding in social scenarios: active speaker detection and spatial audio denoising. Through extensive experiments we show that our features are generic enough to improve over multiple state-of-the-art baselines on both tasks on two challenging egocentric video datasets that offer binaural audio EgoCom and EasyCom. Project: http://vision.cs.utexas.edu/ projects/ego_av_corr. | https://openaccess.thecvf.com/content/CVPR2024/papers/Majumder_Learning_Spatial_Features_from_Audio-Visual_Correspondence_in_Egocentric_Videos_CVPR_2024_paper.pdf | http://arxiv.org/abs/2307.04760 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Majumder_Learning_Spatial_Features_from_Audio-Visual_Correspondence_in_Egocentric_Videos_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Majumder_Learning_Spatial_Features_from_Audio-Visual_Correspondence_in_Egocentric_Videos_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Majumder_Learning_Spatial_Features_CVPR_2024_supplemental.pdf | null |
DreamAvatar: Text-and-Shape Guided 3D Human Avatar Generation via Diffusion Models | Yukang Cao, Yan-Pei Cao, Kai Han, Ying Shan, Kwan-Yee K. Wong | We present DreamAvatar a text-and-shape guided framework for generating high-quality 3D human avatars with controllable poses. While encouraging results have been reported by recent methods on text-guided 3D common object generation generating high-quality human avatars remains an open challenge due to the complexity of the human body's shape pose and appearance. We propose DreamAvatar to tackle this challenge which utilizes a trainable NeRF for predicting density and color for 3D points and pretrained text-to-image diffusion models for providing 2D self-supervision. Specifically we leverage the SMPL model to provide shape and pose guidance for the generation. We introduce a dual-observation-space design that involves the joint optimization of a canonical space and a posed space that are related by a learnable deformation field. This facilitates the generation of more complete textures and geometry faithful to the target pose. We also jointly optimize the losses computed from the full body and from the zoomed-in 3D head to alleviate the common multi-face "Janus" problem and improve facial details in the generated avatars. Extensive evaluations demonstrate that DreamAvatar significantly outperforms existing methods establishing a new state-of-the-art for text-and-shape guided 3D human avatar generation. | https://openaccess.thecvf.com/content/CVPR2024/papers/Cao_DreamAvatar_Text-and-Shape_Guided_3D_Human_Avatar_Generation_via_Diffusion_Models_CVPR_2024_paper.pdf | http://arxiv.org/abs/2304.00916 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Cao_DreamAvatar_Text-and-Shape_Guided_3D_Human_Avatar_Generation_via_Diffusion_Models_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Cao_DreamAvatar_Text-and-Shape_Guided_3D_Human_Avatar_Generation_via_Diffusion_Models_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Cao_DreamAvatar_Text-and-Shape_Guided_CVPR_2024_supplemental.pdf | null |
Dynamic Graph Representation with Knowledge-aware Attention for Histopathology Whole Slide Image Analysis | Jiawen Li, Yuxuan Chen, Hongbo Chu, Qiehe Sun, Tian Guan, Anjia Han, Yonghong He | Histopathological whole slide images (WSIs) classification has become a foundation task in medical microscopic imaging processing. Prevailing approaches involve learning WSIs as instance-bag representations emphasizing significant instances but struggling to capture the interactions between instances. Additionally conventional graph representation methods utilize explicit spatial positions to construct topological structures but restrict the flexible interaction capabilities between instances at arbitrary locations particularly when spatially distant. In response we propose a novel dynamic graph representation algorithm that conceptualizes WSIs as a form of the knowledge graph structure. Specifically we dynamically construct neighbors and directed edge embeddings based on the head and tail relationships between instances. Then we devise a knowledge-aware attention mechanism that can update the head node features by learning the joint attention score of each neighbor and edge. Finally we obtain a graph-level embedding through the global pooling process of the updated head serving as an implicit representation for the WSI classification. Our end-to-end graph representation learning approach has outperformed the state-of-the-art WSI analysis methods on three TCGA benchmark datasets and in-house test sets. Our code is available at https://github.com/WonderLandxD/WiKG. | https://openaccess.thecvf.com/content/CVPR2024/papers/Li_Dynamic_Graph_Representation_with_Knowledge-aware_Attention_for_Histopathology_Whole_Slide_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.07719 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Li_Dynamic_Graph_Representation_with_Knowledge-aware_Attention_for_Histopathology_Whole_Slide_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Li_Dynamic_Graph_Representation_with_Knowledge-aware_Attention_for_Histopathology_Whole_Slide_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Li_Dynamic_Graph_Representation_CVPR_2024_supplemental.pdf | null |
Brain Decodes Deep Nets | Huzheng Yang, James Gee, Jianbo Shi | We developed a tool for visualizing and analyzing large pre-trained vision models by mapping them onto the brain thus exposing their hidden inside. Our innovation arises from a surprising usage of brain encoding: predicting brain fMRI measurements in response to images. We report two findings. First explicit mapping between the brain and deep-network features across dimensions of space layers scales and channels is crucial. This mapping method FactorTopy is plug-and-play for any deep-network; with it one can paint a picture of the network onto the brain (literally!). Second our visualization shows how different training methods matter: they lead to remarkable differences in hierarchical organization and scaling behavior growing with more data or network capacity. It also provides insight into fine-tuning: how pre-trained models change when adapting to small datasets. We found brain-like hierarchically organized network suffer less from catastrophic forgetting after fine-tuned. | https://openaccess.thecvf.com/content/CVPR2024/papers/Yang_Brain_Decodes_Deep_Nets_CVPR_2024_paper.pdf | http://arxiv.org/abs/2312.01280 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Yang_Brain_Decodes_Deep_Nets_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Yang_Brain_Decodes_Deep_Nets_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Yang_Brain_Decodes_Deep_CVPR_2024_supplemental.pdf | null |
Semantics Distortion and Style Matter: Towards Source-free UDA for Panoramic Segmentation | Xu Zheng, Pengyuan Zhou, Athanasios V. Vasilakos, Lin Wang | This paper addresses an interesting yet challenging problem-- source-free unsupervised domain adaptation (SFUDA) for pinhole-to-panoramic semantic segmentation--given only a pinhole image-trained model (i.e. source) and unlabeled panoramic images (i.e. target). Tackling this problem is nontrivial due to the semantic mismatches style discrepancies and inevitable distortion of panoramic images. To this end we propose a novel method that utilizes Tangent Projection (TP) as it has less distortion and meanwhile slits the equirectangular projection (ERP) with a fixed FoV to mimic the pinhole images. Both projections are shown effective in extracting knowledge from the source model. However the distinct projection discrepancies between source and target domains impede the direct knowledge transfer; thus we propose a panoramic prototype adaptation module (PPAM) to integrate panoramic prototypes from the extracted knowledge for adaptation. We then impose the loss constraints on both predictions and prototypes and propose a cross-dual attention module (CDAM) at the feature level to better align the spatial and channel characteristics across the domains and projections. Both knowledge extraction and transfer processes are synchronously updated to reach the best performance. Extensive experiments on the synthetic and real-world benchmarks including outdoor and indoor scenarios demonstrate that our method achieves significantly better performance than prior SFUDA methods for pinhole-to-panoramic adaptation. | https://openaccess.thecvf.com/content/CVPR2024/papers/Zheng_Semantics_Distortion_and_Style_Matter_Towards_Source-free_UDA_for_Panoramic_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.12505 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Zheng_Semantics_Distortion_and_Style_Matter_Towards_Source-free_UDA_for_Panoramic_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Zheng_Semantics_Distortion_and_Style_Matter_Towards_Source-free_UDA_for_Panoramic_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Zheng_Semantics_Distortion_and_CVPR_2024_supplemental.pdf | null |
Bidirectional Autoregessive Diffusion Model for Dance Generation | null | null | null | null | null | https://openaccess.thecvf.com/content/CVPR2024/html/Zhang_Bidirectional_Autoregessive_Diffusion_Model_for_Dance_Generation_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Zhang_Bidirectional_Autoregessive_Diffusion_Model_for_Dance_Generation_CVPR_2024_paper.html | CVPR 2024 | null | null |
Align Before Adapt: Leveraging Entity-to-Region Alignments for Generalizable Video Action Recognition | Yifei Chen, Dapeng Chen, Ruijin Liu, Sai Zhou, Wenyuan Xue, Wei Peng | Large-scale visual-language pre-trained models have achieved significant success in various video tasks. However most existing methods follow an "adapt then align" paradigm which adapts pre-trained image encoders to model video-level representations and utilizes one-hot or text embedding of the action labels for supervision. This paradigm overlooks the challenge of mapping from static images to complicated activity concepts. In this paper we propose a novel "Align before Adapt" (ALT) paradigm. Prior to adapting to video representation learning we exploit the entity-to-region alignments for each frame. The alignments are fulfilled by matching the region-aware image embeddings to an offline-constructed text corpus. With the aligned entities we feed their text embeddings to a transformer-based video adapter as the queries which can help extract the semantics of the most important entities from a video to a vector. This paradigm reuses the visual-language alignment of VLP during adaptation and tries to explain an action by the underlying entities. This helps understand actions by bridging the gap with complex activity semantics particularly when facing unfamiliar or unseen categories. ALT demonstrates competitive performance while maintaining remarkably low computational costs. In fully supervised experiments it achieves 88.1% top-1 accuracy on Kinetics-400 with only 4947 GFLOPs. Moreover ALT outperforms the previous state-of-the-art methods in both zero-shot and few-shot experiments emphasizing its superior generalizability across various learning scenarios. | https://openaccess.thecvf.com/content/CVPR2024/papers/Chen_Align_Before_Adapt_Leveraging_Entity-to-Region_Alignments_for_Generalizable_Video_Action_CVPR_2024_paper.pdf | http://arxiv.org/abs/2311.15619 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Chen_Align_Before_Adapt_Leveraging_Entity-to-Region_Alignments_for_Generalizable_Video_Action_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Chen_Align_Before_Adapt_Leveraging_Entity-to-Region_Alignments_for_Generalizable_Video_Action_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Chen_Align_Before_Adapt_CVPR_2024_supplemental.pdf | null |
GOV-NeSF: Generalizable Open-Vocabulary Neural Semantic Fields | Yunsong Wang, Hanlin Chen, Gim Hee Lee | Recent advancements in vision-language foundation models have significantly enhanced open-vocabulary 3D scene understanding. However the generalizability of existing methods is constrained due to their framework designs and their reliance on 3D data. We address this limitation by introducing Generalizable Open-Vocabulary Neural Semantic Fields (GOV-NeSF) a novel approach offering a generalizable implicit representation of 3D scenes with open-vocabulary semantics. We aggregate the geometry-aware features using a cost volume and propose a Multi-view Joint Fusion module to aggregate multi-view features through a cross-view attention mechanism which effectively predicts view-specific blending weights for both colors and open-vocabulary features. Remarkably our GOV-NeSF exhibits state-of-the-art performance in both 2D and 3D open-vocabulary semantic segmentation eliminating the need for ground truth semantic labels or depth priors and effectively generalize across scenes and datasets without fine-tuning. | https://openaccess.thecvf.com/content/CVPR2024/papers/Wang_GOV-NeSF_Generalizable_Open-Vocabulary_Neural_Semantic_Fields_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Wang_GOV-NeSF_Generalizable_Open-Vocabulary_Neural_Semantic_Fields_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Wang_GOV-NeSF_Generalizable_Open-Vocabulary_Neural_Semantic_Fields_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Wang_GOV-NeSF_Generalizable_Open-Vocabulary_CVPR_2024_supplemental.pdf | null |
FRESCO: Spatial-Temporal Correspondence for Zero-Shot Video Translation | Shuai Yang, Yifan Zhou, Ziwei Liu, Chen Change Loy | The remarkable efficacy of text-to-image diffusion models has motivated extensive exploration of their potential application in video domains. Zero-shot methods seek to extend image diffusion models to videos without necessitating model training. Recent methods mainly focus on incorporating inter-frame correspondence into attention mechanisms. However the soft constraint imposed on determining where to attend to valid features can sometimes be insufficient resulting in temporal inconsistency. In this paper we introduce FRESCO intra-frame correspondence alongside inter-frame correspondence to establish a more robust spatial-temporal constraint. This enhancement ensures a more consistent transformation of semantically similar content across frames. Beyond mere attention guidance our approach involves an explicit update of features to achieve high spatial-temporal consistency with the input video significantly improving the visual coherence of the resulting translated videos. Extensive experiments demonstrate the effectiveness of our proposed framework in producing high-quality coherent videos marking a notable improvement over existing zero-shot methods. | https://openaccess.thecvf.com/content/CVPR2024/papers/Yang_FRESCO_Spatial-Temporal_Correspondence_for_Zero-Shot_Video_Translation_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.12962 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Yang_FRESCO_Spatial-Temporal_Correspondence_for_Zero-Shot_Video_Translation_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Yang_FRESCO_Spatial-Temporal_Correspondence_for_Zero-Shot_Video_Translation_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Yang_FRESCO_Spatial-Temporal_Correspondence_CVPR_2024_supplemental.zip | null |
Dual-Scale Transformer for Large-Scale Single-Pixel Imaging | Gang Qu, Ping Wang, Xin Yuan | Single-pixel imaging (SPI) is a potential computational imaging technique which produces image by solving an ill-posed reconstruction problem from few measurements captured by a single-pixel detector. Deep learning has achieved impressive success on SPI reconstruction. However previous poor reconstruction performance and impractical imaging model limit its real-world applications. In this paper we propose a deep unfolding network with hybrid-attention Transformer on Kronecker SPI model dubbed HATNet to improve the imaging quality of real SPI cameras. Specifically we unfold the computation graph of the iterative shrinkage-thresholding algorithm (ISTA) into two alternative modules: efficient tensor gradient descent and hybrid-attention multi-scale denoising. By virtue of Kronecker SPI the gradient descent module can avoid high computational overheads rooted in previous gradient descent modules based on vectorized SPI. The denoising module is an encoder-decoder architecture powered by dual-scale spatial attention for high- and low-frequency aggregation and channel attention for global information recalibration. Moreover we build a SPI prototype to verify the effectiveness of the proposed method. Extensive experiments on synthetic and real data demonstrate that our method achieves the state-of-the-art performance. The source code and pre-trained models are available at https://github.com/Gang-Qu/HATNet-SPI. | https://openaccess.thecvf.com/content/CVPR2024/papers/Qu_Dual-Scale_Transformer_for_Large-Scale_Single-Pixel_Imaging_CVPR_2024_paper.pdf | http://arxiv.org/abs/2404.05001 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Qu_Dual-Scale_Transformer_for_Large-Scale_Single-Pixel_Imaging_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Qu_Dual-Scale_Transformer_for_Large-Scale_Single-Pixel_Imaging_CVPR_2024_paper.html | CVPR 2024 | null | null |
Towards Robust 3D Object Detection with LiDAR and 4D Radar Fusion in Various Weather Conditions | Yujeong Chae, Hyeonseong Kim, Kuk-Jin Yoon | Detecting objects in 3D under various (normal and adverse) weather conditions is essential for safe autonomous driving systems. Recent approaches have focused on employing weather-insensitive 4D radar sensors and leveraging them with other modalities such as LiDAR. However they fuse multi-modal information without considering the sensor characteristics and weather conditions and lose some height information which could be useful for localizing 3D objects. In this paper we propose a novel framework for robust LiDAR and 4D radar-based 3D object detection. Specifically we propose a 3D-LRF module that considers the distinct patterns they exhibit in 3D space (e.g. precise 3D mapping of LiDAR and wide-range weather-insensitive measurement of 4D radar) and extract fusion features based on their 3D spatial relationship. Then our weather-conditional radar-flow gating network modulates the information flow of fusion features depending on weather conditions and obtains enhanced feature that effectively incorporates the strength of two domains under various weather conditions. The extensive experiments demonstrate that our model achieves SoTA performance for 3D object detection under various weather conditions. | https://openaccess.thecvf.com/content/CVPR2024/papers/Chae_Towards_Robust_3D_Object_Detection_with_LiDAR_and_4D_Radar_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Chae_Towards_Robust_3D_Object_Detection_with_LiDAR_and_4D_Radar_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Chae_Towards_Robust_3D_Object_Detection_with_LiDAR_and_4D_Radar_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Chae_Towards_Robust_3D_CVPR_2024_supplemental.pdf | null |
Enhancing 3D Fidelity of Text-to-3D using Cross-View Correspondences | Seungwook Kim, Kejie Li, Xueqing Deng, Yichun Shi, Minsu Cho, Peng Wang | Leveraging multi-view diffusion models as priors for 3D optimization have alleviated the problem of 3D consistency e.g. the Janus face problem or the content drift problem in zero-shot text-to-3D models. However the 3D geometric fidelity of the output remains an unresolved issue; albeit the rendered 2D views are realistic the underlying geometry may contain errors such as unreasonable concavities. In this work we propose CorrespondentDream an effective method to leverage annotation-free cross-view correspondences yielded from the diffusion U-Net to provide additional 3D prior to the NeRF optimization process. We find that these correspondences are strongly consistent with human perception and by adopting it in our loss design we are able to produce NeRF models with geometries that are more coherent with common sense e.g. more smoothed object surface yielding higher 3D fidelity. We demonstrate the efficacy of our approach through various comparative qualitative results and a solid user study. | https://openaccess.thecvf.com/content/CVPR2024/papers/Kim_Enhancing_3D_Fidelity_of_Text-to-3D_using_Cross-View_Correspondences_CVPR_2024_paper.pdf | http://arxiv.org/abs/2404.10603 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Kim_Enhancing_3D_Fidelity_of_Text-to-3D_using_Cross-View_Correspondences_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Kim_Enhancing_3D_Fidelity_of_Text-to-3D_using_Cross-View_Correspondences_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Kim_Enhancing_3D_Fidelity_CVPR_2024_supplemental.pdf | null |
Bezier Everywhere All at Once: Learning Drivable Lanes as Bezier Graphs | Hugh Blayney, Hanlin Tian, Hamish Scott, Nils Goldbeck, Chess Stetson, Panagiotis Angeloudis | Knowledge of lane topology is a core problem in autonomous driving. Aerial imagery can provide high resolution quickly updatable lane source data but detecting lanes from such data has so far been an expensive manual process or where automated solutions exist undrivable and requiring of downstream processing. We propose a method for large-scale lane topology extraction from aerial imagery while ensuring that the resulting lanes are realistic and drivable by introducing a novel Bezier Graph shared parameterisation of Bezier curves. We develop a transformer-based model to predict these Bezier Graphs from input aerial images demonstrating competitive results on the UrbanLaneGraph dataset. We demonstrate that our method generates realistic lane graphs which require both minimal input and minimal downstream processing. We make our code publicly available at https://github.com/driskai/BGFormer | https://openaccess.thecvf.com/content/CVPR2024/papers/Blayney_Bezier_Everywhere_All_at_Once_Learning_Drivable_Lanes_as_Bezier_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Blayney_Bezier_Everywhere_All_at_Once_Learning_Drivable_Lanes_as_Bezier_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Blayney_Bezier_Everywhere_All_at_Once_Learning_Drivable_Lanes_as_Bezier_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Blayney_Bezier_Everywhere_All_CVPR_2024_supplemental.pdf | null |
SplattingAvatar: Realistic Real-Time Human Avatars with Mesh-Embedded Gaussian Splatting | Zhijing Shao, Zhaolong Wang, Zhuang Li, Duotun Wang, Xiangru Lin, Yu Zhang, Mingming Fan, Zeyu Wang | We present SplattingAvatar a hybrid 3D representation of photorealistic human avatars with Gaussian Splatting embedded on a triangle mesh which renders over 300 FPS on a modern GPU and 30 FPS on a mobile device. We disentangle the motion and appearance of a virtual human with explicit mesh geometry and implicit appearance modeling with Gaussian Splatting. The Gaussians are defined by barycentric coordinates and displacement on a triangle mesh as Phong surfaces. We extend lifted optimization to simultaneously optimize the parameters of the Gaussians while walking on the triangle mesh. SplattingAvatar is a hybrid representation of virtual humans where the mesh represents low-frequency motion and surface deformation while the Gaussians take over the high-frequency geometry and detailed appearance. Unlike existing deformation methods that rely on an MLP-based linear blend skinning (LBS) field for motion we control the rotation and translation of the Gaussians directly by mesh which empowers its compatibility with various animation techniques e.g. skeletal animation blend shapes and mesh editing. Trainable from monocular videos for both full-body and head avatars SplattingAvatar shows state-of-the-art rendering quality across multiple datasets. | https://openaccess.thecvf.com/content/CVPR2024/papers/Shao_SplattingAvatar_Realistic_Real-Time_Human_Avatars_with_Mesh-Embedded_Gaussian_Splatting_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.05087 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Shao_SplattingAvatar_Realistic_Real-Time_Human_Avatars_with_Mesh-Embedded_Gaussian_Splatting_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Shao_SplattingAvatar_Realistic_Real-Time_Human_Avatars_with_Mesh-Embedded_Gaussian_Splatting_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Shao_SplattingAvatar_Realistic_Real-Time_CVPR_2024_supplemental.pdf | null |
MoSAR: Monocular Semi-Supervised Model for Avatar Reconstruction using Differentiable Shading | Abdallah Dib, Luiz Gustavo Hafemann, Emeline Got, Trevor Anderson, Amin Fadaeinejad, Rafael M. O. Cruz, Marc-André Carbonneau | Reconstructing an avatar from a portrait image has many applications in multimedia but remains a challenging research problem. Extracting reflectance maps and geometry from one image is ill-posed: recovering geometry is a one-to-many mapping problem and reflectance and light are difficult to disentangle. Accurate geometry and reflectance can be captured under the controlled conditions of a light stage but it is costly to acquire large datasets in this fashion. Moreover training solely with this type of data leads to poor generalization with in-the-wild images. This motivates the introduction of MoSAR a method for 3D avatar generation from monocular images. We propose a semi-supervised training scheme that improves generalization by learning from both light stage and in-the-wild datasets. This is achieved using a novel differentiable shading formulation. We show that our approach effectively disentangles the intrinsic face parameters producing relightable avatars. As a result MoSAR estimates a richer set of skin reflectance maps and generates more realistic avatars than existing state-of-the-art methods. We also release a new dataset that provides intrinsic face attributes (diffuse specular ambient occlusion and translucency maps) for 10k subjects. | https://openaccess.thecvf.com/content/CVPR2024/papers/Dib_MoSAR_Monocular_Semi-Supervised_Model_for_Avatar_Reconstruction_using_Differentiable_Shading_CVPR_2024_paper.pdf | http://arxiv.org/abs/2312.13091 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Dib_MoSAR_Monocular_Semi-Supervised_Model_for_Avatar_Reconstruction_using_Differentiable_Shading_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Dib_MoSAR_Monocular_Semi-Supervised_Model_for_Avatar_Reconstruction_using_Differentiable_Shading_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Dib_MoSAR_Monocular_Semi-Supervised_CVPR_2024_supplemental.pdf | null |
Bridging Remote Sensors with Multisensor Geospatial Foundation Models | Boran Han, Shuai Zhang, Xingjian Shi, Markus Reichstein | In the realm of geospatial analysis the diversity of remote sensors encompassing both optical and microwave technologies offers a wealth of distinct observational capabilities. Recognizing this we present msGFM a multisensor geospatial foundation model that effectively unifies data from four key sensor modalities. This integration spans an expansive dataset of two million multisensor images. msGFM is uniquely adept at handling both paired and unpaired sensor data. For data originating from identical geolocations our model employs an innovative cross-sensor pretraining approach in masked image modeling enabling the synthesis of joint representations from diverse sensors. msGFM incorporating four remote sensors upholds strong performance forming a comprehensive model adaptable to various sensor types. msGFM has demonstrated enhanced proficiency in a range of both single-sensor and multisensor downstream tasks. These include scene classification segmentation cloud removal and pan-sharpening. A key discovery of our research is that representations derived from natural images are not always compatible with the distinct characteristics of geospatial remote sensors underscoring the limitations of existing representations in this field. Our work can serve as a guide for developing multisensor geospatial pretraining models paving the way for more advanced geospatial capabilities. Code can be found at \url https://github.com/boranhan/Geospatial_Foundation_Models | https://openaccess.thecvf.com/content/CVPR2024/papers/Han_Bridging_Remote_Sensors_with_Multisensor_Geospatial_Foundation_Models_CVPR_2024_paper.pdf | http://arxiv.org/abs/2404.01260 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Han_Bridging_Remote_Sensors_with_Multisensor_Geospatial_Foundation_Models_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Han_Bridging_Remote_Sensors_with_Multisensor_Geospatial_Foundation_Models_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Han_Bridging_Remote_Sensors_CVPR_2024_supplemental.pdf | null |
Can I Trust Your Answer? Visually Grounded Video Question Answering | Junbin Xiao, Angela Yao, Yicong Li, Tat-Seng Chua | We study visually grounded VideoQA in response to the emerging trends of utilizing pretraining techniques for video- language understanding. Specifically by forcing vision- language models (VLMs) to answer questions and simultane- ously provide visual evidence we seek to ascertain the extent to which the predictions of such techniques are genuinely anchored in relevant video content versus spurious corre- lations from language or irrelevant visual context. Towards this we construct NExT-GQA - an extension of NExT-QA with 10.5K temporal grounding (or location) labels tied to the original QA pairs. With NExT-GQA we scrutinize a series of state-of-the-art VLMs. Through post-hoc atten- tion analysis we find that these models are extremely weak in substantiating the answers despite their strong QA per- formance. This exposes the limitation of current VLMs in making reliable predictions. As a remedy we further explore and propose a grounded-QA method via Gaussian mask optimization and cross-modal learning. Experiments with different backbones demonstrate that this grounding mechanism improves both grounding and QA. With these efforts we aim to push towards trustworthy VLMs in VQA systems. Our dataset and code are available at https://github.com/doc-doc/NExT-GQA. | https://openaccess.thecvf.com/content/CVPR2024/papers/Xiao_Can_I_Trust_Your_Answer_Visually_Grounded_Video_Question_Answering_CVPR_2024_paper.pdf | http://arxiv.org/abs/2309.01327 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Xiao_Can_I_Trust_Your_Answer_Visually_Grounded_Video_Question_Answering_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Xiao_Can_I_Trust_Your_Answer_Visually_Grounded_Video_Question_Answering_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Xiao_Can_I_Trust_CVPR_2024_supplemental.pdf | null |
RankED: Addressing Imbalance and Uncertainty in Edge Detection Using Ranking-based Losses | Bedrettin Cetinkaya, Sinan Kalkan, Emre Akbas | Detecting edges in images suffers from the problems of (P1) heavy imbalance between positive and negative classes as well as (P2) label uncertainty owing to disagreement between different annotators. Existing solutions address P1 using class-balanced cross-entropy loss and dice loss and P2 by only predicting edges agreed upon by most annotators. In this paper we propose RankED a unified ranking-based approach that addresses both the imbalance problem (P1) and the uncertainty problem (P2). RankED tackles these two problems with two components: One component which ranks positive pixels over negative pixels and the second which promotes high confidence edge pixels to have more label certainty. We show that RankED outperforms previous studies and sets a new state-of-the-art on NYUD-v2 BSDS500 and Multi-cue datasets. Code is available at https://ranked-cvpr24.github.io. | https://openaccess.thecvf.com/content/CVPR2024/papers/Cetinkaya_RankED_Addressing_Imbalance_and_Uncertainty_in_Edge_Detection_Using_Ranking-based_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.01795 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Cetinkaya_RankED_Addressing_Imbalance_and_Uncertainty_in_Edge_Detection_Using_Ranking-based_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Cetinkaya_RankED_Addressing_Imbalance_and_Uncertainty_in_Edge_Detection_Using_Ranking-based_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Cetinkaya_RankED_Addressing_Imbalance_CVPR_2024_supplemental.pdf | null |
DiffHuman: Probabilistic Photorealistic 3D Reconstruction of Humans | Akash Sengupta, Thiemo Alldieck, Nikos Kolotouros, Enric Corona, Andrei Zanfir, Cristian Sminchisescu | We present DiffHuman a probabilistic method for photorealistic 3D human reconstruction from a single RGB image. Despite the ill-posed nature of this problem most methods are deterministic and output a single solution often resulting in a lack of geometric detail and blurriness in unseen or uncertain regions. In contrast DiffHuman predicts a probability distribution over 3D reconstructions conditioned on an input 2D image which allows us to sample multiple detailed 3D avatars that are consistent with the image. DiffHuman is implemented as a conditional diffusion model that denoises pixel-aligned 2D observations of an underlying 3D shape representation. During inference we may sample 3D avatars by iteratively denoising 2D renders of the predicted 3D representation. Furthermore we introduce a generator neural network that approximates rendering with considerably reduced runtime (55x speed up) resulting in a novel dual-branch diffusion framework. Our experiments show that DiffHuman can produce diverse and detailed reconstructions for the parts of the person that are unseen or uncertain in the input image while remaining competitive with the state-of-the-art when reconstructing visible surfaces. | https://openaccess.thecvf.com/content/CVPR2024/papers/Sengupta_DiffHuman_Probabilistic_Photorealistic_3D_Reconstruction_of_Humans_CVPR_2024_paper.pdf | http://arxiv.org/abs/2404.00485 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Sengupta_DiffHuman_Probabilistic_Photorealistic_3D_Reconstruction_of_Humans_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Sengupta_DiffHuman_Probabilistic_Photorealistic_3D_Reconstruction_of_Humans_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Sengupta_DiffHuman_Probabilistic_Photorealistic_CVPR_2024_supplemental.pdf | null |
SeeSR: Towards Semantics-Aware Real-World Image Super-Resolution | Rongyuan Wu, Tao Yang, Lingchen Sun, Zhengqiang Zhang, Shuai Li, Lei Zhang | Owe to the powerful generative priors the pre-trained text-to-image (T2I) diffusion models have become increasingly popular in solving the real-world image super-resolution problem. However as a consequence of the heavy quality degradation of input low-resolution (LR) images the destruction of local structures can lead to ambiguous image semantics. As a result the content of reproduced high-resolution image may have semantic errors deteriorating the super-resolution performance. To address this issue we present a semantics-aware approach to better preserve the semantic fidelity of generative real-world image super-resolution. First we train a degradation-aware prompt extractor which can generate accurate soft and hard semantic prompts even under strong degradation. The hard semantic prompts refer to the image tags aiming to enhance the local perception ability of the T2I model while the soft semantic prompts compensate for the hard ones to provide additional representation information. These semantic prompts encourage the T2I model to generate detailed and semantically accurate results. Furthermore during the inference process we integrate the LR images into the initial sampling noise to mitigate the diffusion model's tendency to generate excessive random details. The experiments show that our method can reproduce more realistic image details and hold better the semantics. The source code of our method can be found at https://github.com/cswry/SeeSR | https://openaccess.thecvf.com/content/CVPR2024/papers/Wu_SeeSR_Towards_Semantics-Aware_Real-World_Image_Super-Resolution_CVPR_2024_paper.pdf | http://arxiv.org/abs/2311.16518 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Wu_SeeSR_Towards_Semantics-Aware_Real-World_Image_Super-Resolution_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Wu_SeeSR_Towards_Semantics-Aware_Real-World_Image_Super-Resolution_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Wu_SeeSR_Towards_Semantics-Aware_CVPR_2024_supplemental.pdf | null |
Permutation Equivariance of Transformers and Its Applications | Hengyuan Xu, Liyao Xiang, Hangyu Ye, Dixi Yao, Pengzhi Chu, Baochun Li | Revolutionizing the field of deep learning Transformer-based models have achieved remarkable performance in many tasks. Recent research has recognized these models are robust to shuffling but are limited to inter-token permutation in the forward propagation. In this work we propose our definition of permutation equivariance a broader concept covering both inter- and intra- token permutation in the forward and backward propagation of neural networks. We rigorously proved that such permutation equivariance property can be satisfied on most vanilla Transformer-based models with almost no adaptation. We examine the property over a range of state-of-the-art models including ViT Bert GPT and others with experimental validations. Further as a proof-of-concept we explore how real-world applications including privacy-enhancing split learning and model authorization could exploit the permutation equivariance property which implicates wider intriguing application scenarios. The code is available at https://github.com/Doby-Xu/ST | https://openaccess.thecvf.com/content/CVPR2024/papers/Xu_Permutation_Equivariance_of_Transformers_and_Its_Applications_CVPR_2024_paper.pdf | http://arxiv.org/abs/2304.07735 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Xu_Permutation_Equivariance_of_Transformers_and_Its_Applications_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Xu_Permutation_Equivariance_of_Transformers_and_Its_Applications_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Xu_Permutation_Equivariance_of_CVPR_2024_supplemental.pdf | null |
Polos: Multimodal Metric Learning from Human Feedback for Image Captioning | Yuiga Wada, Kanta Kaneda, Daichi Saito, Komei Sugiura | Establishing an automatic evaluation metric that closely aligns with human judgments is essential for effectively developing image captioning models. Recent data-driven metrics have demonstrated a stronger correlation with human judgments than classic metrics such as CIDEr; however they lack sufficient capabilities to handle hallucinations and generalize across diverse images and texts partially because they compute scalar similarities merely using embeddings learned from tasks unrelated to image captioning evaluation. In this study we propose Polos a supervised automatic evaluation metric for image captioning models. Polos computes scores from multimodal inputs using a parallel feature extraction mechanism that leverages embeddings trained through large-scale contrastive learning. To train Polos we introduce Multimodal Metric Learning from Human Feedback (M2LHF) a framework for developing metrics based on human feedback. We constructed the Polaris dataset which comprises 131K human judgments from 550 evaluators which is approximately ten times larger than standard datasets. Our approach achieved state-of-the-art performance on Composite Flickr8K-Expert Flickr8K-CF PASCAL-50S FOIL and the Polaris dataset thereby demonstrating its effectiveness and robustness. | https://openaccess.thecvf.com/content/CVPR2024/papers/Wada_Polos_Multimodal_Metric_Learning_from_Human_Feedback_for_Image_Captioning_CVPR_2024_paper.pdf | http://arxiv.org/abs/2402.18091 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Wada_Polos_Multimodal_Metric_Learning_from_Human_Feedback_for_Image_Captioning_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Wada_Polos_Multimodal_Metric_Learning_from_Human_Feedback_for_Image_Captioning_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Wada_Polos_Multimodal_Metric_CVPR_2024_supplemental.pdf | null |
Detours for Navigating Instructional Videos | Kumar Ashutosh, Zihui Xue, Tushar Nagarajan, Kristen Grauman | We introduce the video detours problem for navigating instructional videos. Given a source video and a natural language query asking to alter the how-to video's current path of execution in a certain way the goal is to find a related "detour video" that satisfies the requested alteration. To address this challenge we propose VidDetours a novel video-language approach that learns to retrieve the targeted temporal segments from a large repository of how-to's using video-and-text conditioned queries. Furthermore we devise a language-based pipeline that exploits how-to video narration text to create weakly supervised training data. We demonstrate our idea applied to the domain of how-to cooking videos where a user can detour from their current recipe to find steps with alternate ingredients tools and techniques. Validating on a ground truth annotated dataset of 16K samples we show our model's significant improvements over best available methods for video retrieval and question answering with recall rates exceeding the state of the art by 35%. | https://openaccess.thecvf.com/content/CVPR2024/papers/Ashutosh_Detours_for_Navigating_Instructional_Videos_CVPR_2024_paper.pdf | http://arxiv.org/abs/2401.01823 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Ashutosh_Detours_for_Navigating_Instructional_Videos_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Ashutosh_Detours_for_Navigating_Instructional_Videos_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Ashutosh_Detours_for_Navigating_CVPR_2024_supplemental.pdf | null |
Discontinuity-preserving Normal Integration with Auxiliary Edges | Hyomin Kim, Yucheol Jung, Seungyong Lee | Many surface reconstruction methods incorporate normal integration which is a process to obtain a depth map from surface gradients. In this process the input may represent a surface with discontinuities e.g. due to self-occlusion. To reconstruct an accurate depth map from the input normal map hidden surface gradients occurring from the jumps must be handled. To model these jumps correctly we design a novel discretization for the domain of normal integration. Our key idea is to introduce auxiliary edges which bridge between piecewise-smooth planes in the domain so that the magnitude of hidden jumps can be explicitly expressed on finite elements. Using the auxiliary edges we design a novel algorithm to optimize the discontinuity and the depth map from the input normal map. Our method optimizes discontinuities by using a combination of iterative re-weighted least squares and iterative filtering of the jump magnitudes on auxiliary edges to provide strong sparsity regularization. Compared to previous discontinuity-preserving normal integration methods which model the magnitude of jumps only implicitly our method reconstructs subtle discontinuities accurately thanks to our explicit representation allowing for strong sparsity regularization. | https://openaccess.thecvf.com/content/CVPR2024/papers/Kim_Discontinuity-preserving_Normal_Integration_with_Auxiliary_Edges_CVPR_2024_paper.pdf | http://arxiv.org/abs/2404.03138 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Kim_Discontinuity-preserving_Normal_Integration_with_Auxiliary_Edges_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Kim_Discontinuity-preserving_Normal_Integration_with_Auxiliary_Edges_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Kim_Discontinuity-preserving_Normal_Integration_CVPR_2024_supplemental.zip | null |
DrivingGaussian: Composite Gaussian Splatting for Surrounding Dynamic Autonomous Driving Scenes | Xiaoyu Zhou, Zhiwei Lin, Xiaojun Shan, Yongtao Wang, Deqing Sun, Ming-Hsuan Yang | We present DrivingGaussian an efficient and effective framework for surrounding dynamic autonomous driving scenes. For complex scenes with moving objects we first sequentially and progressively model the static background of the entire scene with incremental static 3D Gaussians. We then leverage a composite dynamic Gaussian graph to handle multiple moving objects individually reconstructing each object and restoring their accurate positions and occlusion relationships within the scene. We further use a LiDAR prior for Gaussian Splatting to reconstruct scenes with greater details and maintain panoramic consistency. DrivingGaussian outperforms existing methods in dynamic driving scene reconstruction and enables photorealistic surround-view synthesis with high-fidelity and multi-camera consistency. Our project page is at: https://github.com/VDIGPKU/DrivingGaussian. | https://openaccess.thecvf.com/content/CVPR2024/papers/Zhou_DrivingGaussian_Composite_Gaussian_Splatting_for_Surrounding_Dynamic_Autonomous_Driving_Scenes_CVPR_2024_paper.pdf | http://arxiv.org/abs/2312.07920 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Zhou_DrivingGaussian_Composite_Gaussian_Splatting_for_Surrounding_Dynamic_Autonomous_Driving_Scenes_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Zhou_DrivingGaussian_Composite_Gaussian_Splatting_for_Surrounding_Dynamic_Autonomous_Driving_Scenes_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Zhou_DrivingGaussian_Composite_Gaussian_CVPR_2024_supplemental.pdf | null |
Self-Supervised Multi-Object Tracking with Path Consistency | Zijia Lu, Bing Shuai, Yanbei Chen, Zhenlin Xu, Davide Modolo | In this paper we propose a novel concept of path consistency to learn robust object matching without using manual object identity supervision. Our key idea is that to track a object through frames we can obtain multiple different association results from a model by varying the frames it can observe i.e. skipping frames in observation. As the differences in observations do not alter the identities of objects the obtained association results should be consistent. Based on this rationale we generate multiple observation paths each specifying a different set of frames to be skipped and formulate the Path Consistency Loss that enforces the association results are consistent across different observation paths. We use the proposed loss to train our object matching model with only self-supervision. By extensive experiments on three tracking datasets (MOT17 PersonPath22 KITTI) we demonstrate that our method outperforms existing unsupervised methods with consistent margins on various evaluation metrics and even achieves performance close to supervised methods. | https://openaccess.thecvf.com/content/CVPR2024/papers/Lu_Self-Supervised_Multi-Object_Tracking_with_Path_Consistency_CVPR_2024_paper.pdf | http://arxiv.org/abs/2404.05136 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Lu_Self-Supervised_Multi-Object_Tracking_with_Path_Consistency_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Lu_Self-Supervised_Multi-Object_Tracking_with_Path_Consistency_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Lu_Self-Supervised_Multi-Object_Tracking_CVPR_2024_supplemental.pdf | null |
Unsupervised Keypoints from Pretrained Diffusion Models | Eric Hedlin, Gopal Sharma, Shweta Mahajan, Xingzhe He, Hossam Isack, Abhishek Kar, Helge Rhodin, Andrea Tagliasacchi, Kwang Moo Yi | Unsupervised learning of keypoints and landmarks has seen significant progress with the help of modern neural network architectures but performance is yet to match the supervised counterpart making their practicability questionable. We leverage the emergent knowledge within text-to-image diffusion models towards more robust unsupervised keypoints. Our core idea is to find text embeddings that would cause the generative model to consistently attend to compact regions in images (i.e. keypoints). To do so we simply optimize the text embedding such that the cross-attention maps within the denoising network are localized as Gaussians with small standard deviations. We validate our performance on multiple datasets: the CelebA CUB-200-2011 Tai-Chi-HD DeepFashion and Human3.6m datasets. We achieve significantly improved accuracy sometimes even outperforming supervised ones particularly for data that is non-aligned and less curated. Our code is publicly available at https://stablekeypoints.github.io/. | https://openaccess.thecvf.com/content/CVPR2024/papers/Hedlin_Unsupervised_Keypoints_from_Pretrained_Diffusion_Models_CVPR_2024_paper.pdf | http://arxiv.org/abs/2312.00065 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Hedlin_Unsupervised_Keypoints_from_Pretrained_Diffusion_Models_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Hedlin_Unsupervised_Keypoints_from_Pretrained_Diffusion_Models_CVPR_2024_paper.html | CVPR 2024 | null | null |
Resolution Limit of Single-Photon LiDAR | Stanley H. Chan, Hashan K. Weerasooriya, Weijian Zhang, Pamela Abshire, Istvan Gyongy, Robert K. Henderson | Single-photon Light Detection and Ranging (LiDAR) systems are often equipped with an array of detectors for improved spatial resolution and sensing speed. However given a fixed amount of flux produced by the laser transmitter across the scene the per-pixel Signal-to-Noise Ratio (SNR) will decrease when more pixels are packed in a unit space. This presents a fundamental trade-off between the spatial resolution of the sensor array and the SNR received at each pixel. Theoretical characterization of this fundamental limit is explored. By deriving the photon arrival statistics and introducing a series of new approximation techniques the Mean Squared Error (MSE) of the maximum-likelihood estimator of the time delay is derived. The theoretical predictions align well with simulations and real data. | https://openaccess.thecvf.com/content/CVPR2024/papers/Chan_Resolution_Limit_of_Single-Photon_LiDAR_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.17719 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Chan_Resolution_Limit_of_Single-Photon_LiDAR_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Chan_Resolution_Limit_of_Single-Photon_LiDAR_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Chan_Resolution_Limit_of_CVPR_2024_supplemental.pdf | null |
Flatten Long-Range Loss Landscapes for Cross-Domain Few-Shot Learning | Yixiong Zou, Yicong Liu, Yiman Hu, Yuhua Li, Ruixuan Li | Cross-domain few-shot learning (CDFSL) aims to acquire knowledge from limited training data in the target domain by leveraging prior knowledge transferred from source domains with abundant training samples. CDFSL faces challenges in transferring knowledge across dissimilar domains and fine-tuning models with limited training data. To address these challenges we initially extend the analysis of loss landscapes from the parameter space to the representation space which allows us to simultaneously interpret the transferring and fine-tuning difficulties of CDFSL models. We observe that sharp minima in the loss landscapes of the representation space result in representations that are hard to transfer and fine-tune. Moreover existing flatness-based methods have limited generalization ability due to their short-range flatness. To enhance the transferability and facilitate fine-tuning we introduce a simple yet effective approach to achieve long-range flattening of the minima in the loss landscape. This approach considers representations that are differently normalized as minima in the loss landscape and flattens the high-loss region in the middle by randomly sampling interpolated representations. We implement this method as a new normalization layer that replaces the original one in both CNNs and ViTs. This layer is simple and lightweight introducing only a minimal number of additional parameters. Experimental results on 8 datasets demonstrate that our approach outperforms state-of-the-art methods in terms of average accuracy. Moreover our method achieves performance improvements of up to 9% compared to the current best approaches on individual datasets. Our code will be released. | https://openaccess.thecvf.com/content/CVPR2024/papers/Zou_Flatten_Long-Range_Loss_Landscapes_for_Cross-Domain_Few-Shot_Learning_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.00567 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Zou_Flatten_Long-Range_Loss_Landscapes_for_Cross-Domain_Few-Shot_Learning_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Zou_Flatten_Long-Range_Loss_Landscapes_for_Cross-Domain_Few-Shot_Learning_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Zou_Flatten_Long-Range_Loss_CVPR_2024_supplemental.pdf | null |
Improving Distant 3D Object Detection Using 2D Box Supervision | Zetong Yang, Zhiding Yu, Chris Choy, Renhao Wang, Anima Anandkumar, Jose M. Alvarez | Improving the detection of distant 3d objects is an important yet challenging task. For camera-based 3D perception the annotation of 3d bounding relies heavily on LiDAR for accurate depth information. As such the distance of annotation is often limited due to the sparsity of LiDAR points on distant objects which hampers the capability of existing detectors for long-range scenarios. We address this challenge by considering only 2D box supervision for distant objects since they are easy to annotate. We propose LR3D a framework that learns to recover the missing depth of distant objects. LR3D adopts an implicit projection head to learn the generation of mapping between 2D boxes and depth using the 3D supervision on close objects. This mapping allows the depth estimation of distant objects conditioned on their 2D boxes making long-range 3D detection with 2D supervision feasible. Experiments show that without distant 3D annotations LR3D allows camera-based methods to detect distant objects (over 200m) with comparable accuracy to full 3D supervision. Our framework is general and could widely benefit 3D detection methods to a large extent. | https://openaccess.thecvf.com/content/CVPR2024/papers/Yang_Improving_Distant_3D_Object_Detection_Using_2D_Box_Supervision_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.09230 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Yang_Improving_Distant_3D_Object_Detection_Using_2D_Box_Supervision_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Yang_Improving_Distant_3D_Object_Detection_Using_2D_Box_Supervision_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Yang_Improving_Distant_3D_CVPR_2024_supplemental.pdf | null |
HDQMF: Holographic Feature Decomposition Using Quantum Algorithms | Prathyush Prasanth Poduval, Zhuowen Zou, Mohsen Imani | This paper addresses the decomposition of holographic feature vectors in Hyperdimensional Computing (HDC) aka Vector Symbolic Architectures (VSA). HDC uses high-dimensional vectors with brain-like properties to represent symbolic information and leverages efficient operators to construct and manipulate complexly structured data in a cognitive fashion. Existing models face challenges in decomposing these structures a process crucial for understanding and interpreting a composite hypervector. We address this challenge by proposing the HDC Memorized-Factorization Problem that captures the common patterns of construction in HDC models. To solve this problem efficiently we introduce HDQMF a HyperDimensional Quantum Memorized-Factorization algorithm. HDQMF is unique in its approach utilizing quantum computing to offer efficient solutions. It modifies crucial steps in Grover's algorithm to achieve hypervector decomposition achieving quadratic speed-up. | https://openaccess.thecvf.com/content/CVPR2024/papers/Poduval_HDQMF_Holographic_Feature_Decomposition_Using_Quantum_Algorithms_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Poduval_HDQMF_Holographic_Feature_Decomposition_Using_Quantum_Algorithms_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Poduval_HDQMF_Holographic_Feature_Decomposition_Using_Quantum_Algorithms_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Poduval_HDQMF_Holographic_Feature_CVPR_2024_supplemental.pdf | null |
Diffusion-based Blind Text Image Super-Resolution | Yuzhe Zhang, Jiawei Zhang, Hao Li, Zhouxia Wang, Luwei Hou, Dongqing Zou, Liheng Bian | Recovering degraded low-resolution text images is challenging especially for Chinese text images with complex strokes and severe degradation in real-world scenarios. Ensuring both text fidelity and style realness is crucial for high-quality text image super-resolution. Recently diffusion models have achieved great success in natural image synthesis and restoration due to their powerful data distribution modeling abilities and data generation capabilities. In this work we propose an Image Diffusion Model (IDM) to restore text images with realistic styles. For diffusion models they are not only suitable for modeling realistic image distribution but also appropriate for learning text distribution. Since text prior is important to guarantee the correctness of the restored text structure according to existing arts we also propose a Text Diffusion Model (TDM) for text recognition which can guide IDM to generate text images with correct structures. We further propose a Mixture of Multi-modality module (MoM) to make these two diffusion models cooperate with each other in all the diffusion steps. Extensive experiments on synthetic and real-world datasets demonstrate that our Diffusion-based Blind Text Image Super-Resolution (DiffTSR) can restore text images with more accurate text structures as well as more realistic appearances simultaneously. Code is available at https://github.com/YuzheZhang-1999/DiffTSR. | https://openaccess.thecvf.com/content/CVPR2024/papers/Zhang_Diffusion-based_Blind_Text_Image_Super-Resolution_CVPR_2024_paper.pdf | http://arxiv.org/abs/2312.08886 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Zhang_Diffusion-based_Blind_Text_Image_Super-Resolution_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Zhang_Diffusion-based_Blind_Text_Image_Super-Resolution_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Zhang_Diffusion-based_Blind_Text_CVPR_2024_supplemental.pdf | null |
Consistent Prompting for Rehearsal-Free Continual Learning | Zhanxin Gao, Jun Cen, Xiaobin Chang | Continual learning empowers models to adapt autonomously to the ever-changing environment or data streams without forgetting old knowledge. Prompt-based approaches are built on frozen pre-trained models to learn the task-specific prompts and classifiers efficiently. Existing prompt based methods are inconsistent between training and testing limiting their effectiveness. Two types of inconsistency are revealed. Test predictions are made from all classifiers while training only focuses on the current task classifier without holistic alignment leading to Classifier inconsistency. Prompt inconsistency indicates that the prompt selected during testing may not correspond to the one associated with this task during training. In this paper we propose a novel prompt-based method Consistent Prompting (CPrompt) for more aligned training and testing. Specifically all existing classifiers are exposed to prompt training resulting in classifier consistency learning. In addition prompt consistency learning is proposed to enhance prediction robustness and boost prompt selection accuracy. Our Consistent Prompting surpasses its prompt-based counterparts and achieves state-of-the-art performance on multiple continual learning benchmarks. Detailed analysis shows that improvements come from more consistent training and testing. | https://openaccess.thecvf.com/content/CVPR2024/papers/Gao_Consistent_Prompting_for_Rehearsal-Free_Continual_Learning_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.08568 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Gao_Consistent_Prompting_for_Rehearsal-Free_Continual_Learning_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Gao_Consistent_Prompting_for_Rehearsal-Free_Continual_Learning_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Gao_Consistent_Prompting_for_CVPR_2024_supplemental.pdf | null |
UniPAD: A Universal Pre-training Paradigm for Autonomous Driving | Honghui Yang, Sha Zhang, Di Huang, Xiaoyang Wu, Haoyi Zhu, Tong He, Shixiang Tang, Hengshuang Zhao, Qibo Qiu, Binbin Lin, Xiaofei He, Wanli Ouyang | In the context of autonomous driving the significance of effective feature learning is widely acknowledged. While conventional 3D self-supervised pre-training methods have shown widespread success most methods follow the ideas originally designed for 2D images. In this paper we present UniPAD a novel self-supervised learning paradigm applying 3D volumetric differentiable rendering. UniPAD implicitly encodes 3D space facilitating the reconstruction of continuous 3D shape structures and the intricate appearance characteristics of their 2D projections. The flexibility of our method enables seamless integration into both 2D and 3D frameworks enabling a more holistic comprehension of the scenes. We manifest the feasibility and effectiveness of UniPAD by conducting extensive experiments on various 3D perception tasks. Our method significantly improves lidar- camera- and lidar-camera-based baseline by 9.1 7.7 and 6.9 NDS respectively. Notably our pre-training pipeline achieves 73.2 NDS for 3D object detection and 79.4 mIoU for 3D semantic segmentation on the nuScenes validation set achieving state-of-the-art results in comparison with previous methods. | https://openaccess.thecvf.com/content/CVPR2024/papers/Yang_UniPAD_A_Universal_Pre-training_Paradigm_for_Autonomous_Driving_CVPR_2024_paper.pdf | http://arxiv.org/abs/2310.08370 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Yang_UniPAD_A_Universal_Pre-training_Paradigm_for_Autonomous_Driving_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Yang_UniPAD_A_Universal_Pre-training_Paradigm_for_Autonomous_Driving_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Yang_UniPAD_A_Universal_CVPR_2024_supplemental.pdf | null |
SeD: Semantic-Aware Discriminator for Image Super-Resolution | Bingchen Li, Xin Li, Hanxin Zhu, Yeying Jin, Ruoyu Feng, Zhizheng Zhang, Zhibo Chen | Generative Adversarial Networks (GANs) have been widely used to recover vivid textures in image super-resolution (SR) tasks. In particular one discriminator is utilized to enable the SR network to learn the distribution of real-world high-quality images in an adversarial training manner. However the distribution learning is overly coarse-grained which is susceptible to virtual textures and causes counter-intuitive generation results. To mitigate this we propose the simple and effective Semantic-aware Discriminator (denoted as SeD) which encourages the SR network to learn the fine-grained distributions by introducing the semantics of images as a condition. Concretely we aim to excavate the semantics of images from a well-trained semantic extractor. Under different semantics the discriminator is able to distinguish the real-fake images individually and adaptively which guides the SR network to learn the more fine-grained semantic-aware textures. To obtain accurate and abundant semantics we take full advantage of recently popular pretrained vision models (PVMs) with extensive datasets and then incorporate its semantic features into the discriminator through a well-designed spatial cross-attention module. In this way our proposed semantic-aware discriminator empowered the SR network to produce more photo-realistic and pleasing images. Extensive experiments on two typical tasks i.e. SR and Real SR have demonstrated the effectiveness of our proposed methods. The code will be available at https://github.com/lbc12345/SeD. | https://openaccess.thecvf.com/content/CVPR2024/papers/Li_SeD_Semantic-Aware_Discriminator_for_Image_Super-Resolution_CVPR_2024_paper.pdf | http://arxiv.org/abs/2402.19387 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Li_SeD_Semantic-Aware_Discriminator_for_Image_Super-Resolution_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Li_SeD_Semantic-Aware_Discriminator_for_Image_Super-Resolution_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Li_SeD_Semantic-Aware_Discriminator_CVPR_2024_supplemental.pdf | null |
SocialCounterfactuals: Probing and Mitigating Intersectional Social Biases in Vision-Language Models with Counterfactual Examples | Phillip Howard, Avinash Madasu, Tiep Le, Gustavo Lujan Moreno, Anahita Bhiwandiwalla, Vasudev Lal | While vision-language models (VLMs) have achieved remarkable performance improvements recently there is growing evidence that these models also posses harmful biases with respect to social attributes such as gender and race. Prior studies have primarily focused on probing such bias attributes individually while ignoring biases associated with intersections between social attributes. This could be due to the difficulty of collecting an exhaustive set of image-text pairs for various combinations of social attributes. To address this challenge we employ text-to-image diffusion models to produce counterfactual examples for probing intersectional social biases at scale. Our approach utilizes Stable Diffusion with cross attention control to produce sets of counterfactual image-text pairs that are highly similar in their depiction of a subject (e.g. a given occupation) while differing only in their depiction of intersectional social attributes (e.g. race & gender). Through our over-generate-then-filter methodology we produce SocialCounterfactuals a high-quality dataset containing 171k image-text pairs for probing intersectional biases related to gender race and physical characteristics. We conduct extensive experiments to demonstrate the usefulness of our generated dataset for probing and mitigating intersectional social biases in state-of-the-art VLMs. | https://openaccess.thecvf.com/content/CVPR2024/papers/Howard_SocialCounterfactuals_Probing_and_Mitigating_Intersectional_Social_Biases_in_Vision-Language_Models_CVPR_2024_paper.pdf | http://arxiv.org/abs/2312.00825 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Howard_SocialCounterfactuals_Probing_and_Mitigating_Intersectional_Social_Biases_in_Vision-Language_Models_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Howard_SocialCounterfactuals_Probing_and_Mitigating_Intersectional_Social_Biases_in_Vision-Language_Models_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Howard_SocialCounterfactuals_Probing_and_CVPR_2024_supplemental.pdf | null |
SVDTree: Semantic Voxel Diffusion for Single Image Tree Reconstruction | Yuan Li, Zhihao Liu, Bedrich Benes, Xiaopeng Zhang, Jianwei Guo | Efficiently representing and reconstructing the 3D geometry of biological trees remains a challenging problem in computer vision and graphics. We propose a novel approach for generating realistic tree models from single-view photographs. We cast the 3D information inference problem to a semantic voxel diffusion process which converts an input image of a tree to a novel Semantic Voxel Structure (SVS) in 3D space. The SVS encodes the geometric appearance and semantic structural information (e.g. classifying trunks branches and leaves) which retains the intricate internal tree features. Tailored to the SVS we present SVDTree a new hybrid tree modeling approach by combining structure-oriented branch reconstruction and self-organization-based foliage reconstruction. We validate SVDTree by using images from both synthetic and real trees. The comparison results show that our approach can better preserve tree details and achieve more realistic and accurate reconstruction results than previous methods. | https://openaccess.thecvf.com/content/CVPR2024/papers/Li_SVDTree_Semantic_Voxel_Diffusion_for_Single_Image_Tree_Reconstruction_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Li_SVDTree_Semantic_Voxel_Diffusion_for_Single_Image_Tree_Reconstruction_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Li_SVDTree_Semantic_Voxel_Diffusion_for_Single_Image_Tree_Reconstruction_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Li_SVDTree_Semantic_Voxel_CVPR_2024_supplemental.pdf | null |
Rethinking FID: Towards a Better Evaluation Metric for Image Generation | Sadeep Jayasumana, Srikumar Ramalingam, Andreas Veit, Daniel Glasner, Ayan Chakrabarti, Sanjiv Kumar | As with many machine learning problems the progress of image generation methods hinges on good evaluation metrics. One of the most popular is the Frechet Inception Distance (FID). FID estimates the distance between a distribution of Inception-v3 features of real images and those of images generated by the algorithm. We highlight important drawbacks of FID: Inception's poor representation of the rich and varied content generated by modern text-to-image models incorrect normality assumptions and poor sample complexity. We call for a reevaluation of FID's use as the primary quality metric for generated images. We empirically demonstrate that FID contradicts human raters it does not reflect gradual improvement of iterative text-to-image models it does not capture distortion levels and that it produces inconsistent results when varying the sample size. We also propose an alternative new metric CMMD based on richer CLIP embeddings and the maximum mean discrepancy distance with the Gaussian RBF kernel. It is an unbiased estimator that does not make any assumptions on the probability distribution of the embeddings and is sample efficient. Through extensive experiments and analysis we demonstrate that FID-based evaluations of text-to-image models may be unreliable and that CMMD offers a more robust and reliable assessment of image quality. | https://openaccess.thecvf.com/content/CVPR2024/papers/Jayasumana_Rethinking_FID_Towards_a_Better_Evaluation_Metric_for_Image_Generation_CVPR_2024_paper.pdf | http://arxiv.org/abs/2401.09603 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Jayasumana_Rethinking_FID_Towards_a_Better_Evaluation_Metric_for_Image_Generation_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Jayasumana_Rethinking_FID_Towards_a_Better_Evaluation_Metric_for_Image_Generation_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Jayasumana_Rethinking_FID_Towards_CVPR_2024_supplemental.pdf | null |
Efficient Privacy-Preserving Visual Localization Using 3D Ray Clouds | Heejoon Moon, Chunghwan Lee, Je Hyeong Hong | The recent success in revealing scene details from sparse 3D point clouds obtained via structure-from-motion has raised significant privacy concerns in visual localization. One prominent approach for mitigating this issue is to lift 3D points to 3D lines thereby reducing the effectiveness of the scene inversion attacks but this comes at the cost of increased algorithmic complexity for camera localization due to weaker geometric constraints induced by line clouds. To overcome this limitation we propose a new lifting approach called "ray cloud" whereby each lifted 3D line intersects at one of two predefined locations depicting omnidirectional rays from two cameras. This yields two benefits i) camera localization can now be cast as relative pose estimation between the query image and the calibrated rig of two perspective cameras which can be efficiently solved using a variant of the 5-point algorithm and ii) the ray cloud introduces erroneous estimations for the density-based inversion attack degrading the quality of scene recovery. Moreover we explore possible modifications of the inversion attack to better recover scenes from the ray clouds and propose a ray sampling technique to reduce the effectiveness of the modified attack. Experimental results on two public datasets show real-time localization speed as well as enhanced privacy-preserving capability over the state-of-the-art without overly sacrificing the localization accuracy. | https://openaccess.thecvf.com/content/CVPR2024/papers/Moon_Efficient_Privacy-Preserving_Visual_Localization_Using_3D_Ray_Clouds_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Moon_Efficient_Privacy-Preserving_Visual_Localization_Using_3D_Ray_Clouds_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Moon_Efficient_Privacy-Preserving_Visual_Localization_Using_3D_Ray_Clouds_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Moon_Efficient_Privacy-Preserving_Visual_CVPR_2024_supplemental.pdf | null |
SuperPrimitive: Scene Reconstruction at a Primitive Level | Kirill Mazur, Gwangbin Bae, Andrew J. Davison | Joint camera pose and dense geometry estimation from a set of images or a monocular video remains a challenging problem due to its computational complexity and inherent visual ambiguities. Most dense incremental reconstruction systems operate directly on image pixels and solve for their 3D positions using multi-view geometry cues. Such pixel-level approaches suffer from ambiguities or violations of multi-view consistency (e.g. caused by textureless or specular surfaces). We address this issue with a new image representation which we call a SuperPrimitive. SuperPrimitives are obtained by splitting images into semantically correlated local regions and enhancing them with estimated surface normal directions both of which are predicted by state-of-the-art single image neural networks. This provides a local geometry estimate per SuperPrimitive while their relative positions are adjusted based on multi-view observations. We demonstrate the versatility of our new representation by addressing three 3D reconstruction tasks: depth completion few-view structure from motion and monocular dense visual odometry. Project page: https://makezur.github.io/SuperPrimitive/ | https://openaccess.thecvf.com/content/CVPR2024/papers/Mazur_SuperPrimitive_Scene_Reconstruction_at_a_Primitive_Level_CVPR_2024_paper.pdf | http://arxiv.org/abs/2312.05889 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Mazur_SuperPrimitive_Scene_Reconstruction_at_a_Primitive_Level_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Mazur_SuperPrimitive_Scene_Reconstruction_at_a_Primitive_Level_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Mazur_SuperPrimitive_Scene_Reconstruction_CVPR_2024_supplemental.zip | null |
ReCoRe: Regularized Contrastive Representation Learning of World Model | Rudra P.K. Poudel, Harit Pandya, Stephan Liwicki, Roberto Cipolla | While recent model-free Reinforcement Learning (RL) methods have demonstrated human-level effectiveness in gaming environments their success in everyday tasks like visual navigation has been limited particularly under significant appearance variations. This limitation arises from (i) poor sample efficiency and (ii) over-fitting to training scenarios. To address these challenges we present a world model that learns invariant features using (i) contrastive unsupervised learning and (ii) an intervention-invariant regularizer. Learning an explicit representation of the world dynamics i.e. a world model improves sample efficiency while contrastive learning implicitly enforces learning of invariant features which improves generalization. However the naive integration of contrastive loss to world models is not good enough as world-model-based RL methods independently optimize representation learning and agent policy. To overcome this issue we propose an intervention-invariant regularizer in the form of an auxiliary task such as depth prediction image denoising image segmentation etc. that explicitly enforces invariance to style interventions. Our method outperforms current state-of-the-art model-based and model-free RL methods and significantly improves on out-of-distribution point navigation tasks evaluated on the iGibson benchmark. With only visual observations we further demonstrate that our approach outperforms recent language-guided foundation models for point navigation which is essential for deployment on robots with limited computation capabilities. Finally we demonstrate that our proposed model excels at the sim-to-real transfer of its perception module on the Gibson benchmark. | https://openaccess.thecvf.com/content/CVPR2024/papers/Poudel_ReCoRe_Regularized_Contrastive_Representation_Learning_of_World_Model_CVPR_2024_paper.pdf | http://arxiv.org/abs/2312.09056 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Poudel_ReCoRe_Regularized_Contrastive_Representation_Learning_of_World_Model_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Poudel_ReCoRe_Regularized_Contrastive_Representation_Learning_of_World_Model_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Poudel_ReCoRe_Regularized_Contrastive_CVPR_2024_supplemental.pdf | null |
TFMQ-DM: Temporal Feature Maintenance Quantization for Diffusion Models | Yushi Huang, Ruihao Gong, Jing Liu, Tianlong Chen, Xianglong Liu | The Diffusion model a prevalent framework for image generation encounters significant challenges in terms of broad applicability due to its extended inference times and substantial memory requirements. Efficient Post-training Quantization (PTQ) is pivotal for addressing these issues in traditional models. Different from traditional models diffusion models heavily depend on the time-step t to achieve satisfactory multi-round denoising. Usually t from the finite set \ 1 \ldots T\ is encoded to a temporal feature by a few modules totally irrespective of the sampling data. However existing PTQ methods do not optimize these modules separately. They adopt inappropriate reconstruction targets and complex calibration methods resulting in a severe disturbance of the temporal feature and denoising trajectory as well as a low compression efficiency. To solve these we propose a Temporal Feature Maintenance Quantization (TFMQ) framework building upon a Temporal Information Block which is just related to the time-step t and unrelated to the sampling data. Powered by the pioneering block design we devise temporal information aware reconstruction (TIAR) and finite set calibration (FSC) to align the full-precision temporal features in a limited time. Equipped with the framework we can maintain the most temporal information and ensure the end-to-end generation quality. Extensive experiments on various datasets and diffusion models prove our state-of-the-art results. Remarkably our quantization approach for the first time achieves model performance nearly on par with the full-precision model under 4-bit weight quantization. Additionally our method incurs almost no extra computational cost and accelerates quantization time by 2.0 xon LSUN-Bedrooms 256 x256 compared to previous works. Our code is publicly available at \href https://github.com/ModelTC/TFMQ-DM https://github.com/ModelTC/TFMQ-DM . | https://openaccess.thecvf.com/content/CVPR2024/papers/Huang_TFMQ-DM_Temporal_Feature_Maintenance_Quantization_for_Diffusion_Models_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Huang_TFMQ-DM_Temporal_Feature_Maintenance_Quantization_for_Diffusion_Models_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Huang_TFMQ-DM_Temporal_Feature_Maintenance_Quantization_for_Diffusion_Models_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Huang_TFMQ-DM_Temporal_Feature_CVPR_2024_supplemental.pdf | null |
CNC-Net: Self-Supervised Learning for CNC Machining Operations | Mohsen Yavartanoo, Sangmin Hong, Reyhaneh Neshatavar, Kyoung Mu Lee | CNC manufacturing is a process that employs computer numerical control (CNC) machines to govern the movements of various industrial tools and machinery encompassing equipment ranging from grinders and lathes to mills and CNC routers. However the reliance on manual CNC programming has become a bottleneck and the requirement for expert knowledge can result in significant costs. Therefore we introduce a pioneering approach named CNC-Net representing the use of deep neural networks (DNNs) to simulate CNC machines and grasp intricate operations when supplied with raw materials. CNC-Net constitutes a self-supervised framework that exclusively takes an input 3D model and subsequently generates the essential operation parameters required by the CNC machine to construct the object. Our method has the potential to transformative automation in manufacturing by offering a cost-effective alternative to the high costs of manual CNC programming while maintaining exceptional precision in 3D object production. Our experiments underscore the effectiveness of our CNC-Net in constructing the desired 3D objects through the utilization of CNC operations. Notably it excels in preserving finer local details exhibiting a marked enhancement in precision compared to the state-of-the-art 3D CAD reconstruction approaches. The codes are available at https://github.com/myavartanoo/CNC-Net_PyTorch. | https://openaccess.thecvf.com/content/CVPR2024/papers/Yavartanoo_CNC-Net_Self-Supervised_Learning_for_CNC_Machining_Operations_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Yavartanoo_CNC-Net_Self-Supervised_Learning_for_CNC_Machining_Operations_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Yavartanoo_CNC-Net_Self-Supervised_Learning_for_CNC_Machining_Operations_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Yavartanoo_CNC-Net_Self-Supervised_Learning_CVPR_2024_supplemental.pdf | null |
JRDB-PanoTrack: An Open-world Panoptic Segmentation and Tracking Robotic Dataset in Crowded Human Environments | Duy Tho Le, Chenhui Gou, Stavya Datta, Hengcan Shi, Ian Reid, Jianfei Cai, Hamid Rezatofighi | Autonomous robot systems have attracted increasing research attention in recent years where environment understanding is a crucial step for robot navigation human-robot interaction and decision. Real-world robot systems usually collect visual data from multiple sensors and are required to recognize numerous objects and their movements in complex human-crowded settings. Traditional benchmarks with their reliance on single sensors and limited object classes and scenarios fail to provide the comprehensive environmental understanding robots need for accurate navigation interaction and decision-making. As an extension of JRDB dataset we unveil JRDB-PanoTrack a novel open-world panoptic segmentation and tracking benchmark towards more comprehensive environmental perception. JRDB-PanoTrack includes (1) various data involving indoor and outdoor crowded scenes as well as comprehensive 2D and 3D synchronized data modalities; (2) high-quality 2D spatial panoptic segmentation and temporal tracking annotations with additional 3D label projections for further spatial understanding; (3) diverse object classes for closed- and open-world recognition benchmarks with OSPA-based metrics for evaluation. Extensive evaluation of leading methods shows significant challenges posed by our dataset. | https://openaccess.thecvf.com/content/CVPR2024/papers/Le_JRDB-PanoTrack_An_Open-world_Panoptic_Segmentation_and_Tracking_Robotic_Dataset_in_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Le_JRDB-PanoTrack_An_Open-world_Panoptic_Segmentation_and_Tracking_Robotic_Dataset_in_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Le_JRDB-PanoTrack_An_Open-world_Panoptic_Segmentation_and_Tracking_Robotic_Dataset_in_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Le_JRDB-PanoTrack_An_Open-world_CVPR_2024_supplemental.pdf | null |
CONFORM: Contrast is All You Need for High-Fidelity Text-to-Image Diffusion Models | Tuna Han Salih Meral, Enis Simsar, Federico Tombari, Pinar Yanardag | Images produced by text-to-image diffusion models might not always faithfully represent the semantic intent of the provided text prompt where the model might overlook or entirely fail to produce certain objects. While recent studies propose various solutions they often require customly tailored functions for each of these problems leading to sub-optimal results especially for complex prompts. Our work introduces a novel perspective by tackling this challenge in a contrastive context. Our approach intuitively promotes the segregation of objects in attention maps while also maintaining that pairs of related attributes are kept close to each other. We conducted extensive experiments across a wide variety of scenarios each involving unique combinations of objects attributes and scenes. These experiments effectively showcase the versatility efficiency and flexibility of our method in working with both latent and pixel-based diffusion models including Stable Diffusion and Imagen. Moreover we publicly share our source code to facilitate further research. | https://openaccess.thecvf.com/content/CVPR2024/papers/Meral_CONFORM_Contrast_is_All_You_Need_for_High-Fidelity_Text-to-Image_Diffusion_CVPR_2024_paper.pdf | http://arxiv.org/abs/2312.06059 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Meral_CONFORM_Contrast_is_All_You_Need_for_High-Fidelity_Text-to-Image_Diffusion_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Meral_CONFORM_Contrast_is_All_You_Need_for_High-Fidelity_Text-to-Image_Diffusion_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Meral_CONFORM_Contrast_is_CVPR_2024_supplemental.pdf | null |
Self-Supervised Facial Representation Learning with Facial Region Awareness | Zheng Gao, Ioannis Patras | Self-supervised pre-training has been proved to be effective in learning transferable representations that benefit various visual tasks. This paper asks this question: can self-supervised pre-training learn general facial representations for various facial analysis tasks? Recent efforts toward this goal are limited to treating each face image as a whole i.e. learning consistent facial representations at the image-level which overlooks the consistency of local facial representations (i.e. facial regions like eyes nose etc). In this work we make a first attempt to propose a novel self-supervised facial representation learning framework to learn consistent global and local facial representations Facial Region Awareness (FRA). Specifically we explicitly enforce the consistency of facial regions by matching the local facial representations across views which are extracted with learned heatmaps highlighting the facial regions. Inspired by the mask prediction in supervised semantic segmentation we obtain the heatmaps via cosine similarity between the per-pixel projection of feature maps and facial mask embeddings computed from learnable positional embeddings which leverage the attention mechanism to globally look up the facial image for facial regions. To learn such heatmaps we formulate the learning of facial mask embeddings as a deep clustering problem by assigning the pixel features from the feature maps to them. The transfer learning results on facial classification and regression tasks show that our FRA outperforms previous pre-trained models and more importantly using ResNet as the unified backbone for various tasks our FRA achieves comparable or even better performance compared with SOTA methods in facial analysis tasks. | https://openaccess.thecvf.com/content/CVPR2024/papers/Gao_Self-Supervised_Facial_Representation_Learning_with_Facial_Region_Awareness_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.02138 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Gao_Self-Supervised_Facial_Representation_Learning_with_Facial_Region_Awareness_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Gao_Self-Supervised_Facial_Representation_Learning_with_Facial_Region_Awareness_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Gao_Self-Supervised_Facial_Representation_CVPR_2024_supplemental.pdf | null |
GaussianDreamer: Fast Generation from Text to 3D Gaussians by Bridging 2D and 3D Diffusion Models | Taoran Yi, Jiemin Fang, Junjie Wang, Guanjun Wu, Lingxi Xie, Xiaopeng Zhang, Wenyu Liu, Qi Tian, Xinggang Wang | In recent times the generation of 3D assets from text prompts has shown impressive results. Both 2D and 3D diffusion models can help generate decent 3D objects based on prompts. 3D diffusion models have good 3D consistency but their quality and generalization are limited as trainable 3D data is expensive and hard to obtain. 2D diffusion models enjoy strong abilities of generalization and fine generation but 3D consistency is hard to guarantee. This paper attempts to bridge the power from the two types of diffusion models via the recent explicit and efficient 3D Gaussian splatting representation. A fast 3D object generation framework named as GaussianDreamer is proposed where the 3D diffusion model provides priors for initialization and the 2D diffusion model enriches the geometry and appearance. Operations of noisy point growing and color perturbation are introduced to enhance the initialized Gaussians. Our GaussianDreamer can generate a high-quality 3D instance or 3D avatar within 15 minutes on one GPU much faster than previous methods while the generated instances can be directly rendered in real time. Demos and code are available at https://taoranyi.com/gaussiandreamer/. | https://openaccess.thecvf.com/content/CVPR2024/papers/Yi_GaussianDreamer_Fast_Generation_from_Text_to_3D_Gaussians_by_Bridging_CVPR_2024_paper.pdf | http://arxiv.org/abs/2310.08529 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Yi_GaussianDreamer_Fast_Generation_from_Text_to_3D_Gaussians_by_Bridging_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Yi_GaussianDreamer_Fast_Generation_from_Text_to_3D_Gaussians_by_Bridging_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Yi_GaussianDreamer_Fast_Generation_CVPR_2024_supplemental.pdf | null |
Open-Vocabulary Attention Maps with Token Optimization for Semantic Segmentation in Diffusion Models | Pablo Marcos-Manchón, Roberto Alcover-Couso, Juan C. SanMiguel, José M. Martínez | Diffusion models represent a new paradigm in text-to-image generation. Beyond generating high-quality images from text prompts models such as Stable Diffusion have been successfully extended to the joint generation of semantic segmentation pseudo-masks. However current extensions primarily rely on extracting attentions linked to prompt words used for image synthesis. This approach limits the generation of segmentation masks derived from word tokens not contained in the text prompt. In this work we introduce Open-Vocabulary Attention Maps (OVAM)--a training-free method for text-to-image diffusion models that enables the generation of attention maps for any word. In addition we propose a lightweight optimization process based on OVAM for finding tokens that generate accurate attention maps for an object class with a single annotation. We evaluate these tokens within existing state-of-the-art Stable Diffusion extensions. The best-performing model improves its mIoU from 52.1 to 86.6 for the synthetic images' pseudo-masks demonstrating that our optimized tokens are an efficient way to improve the performance of existing methods without architectural changes or retraining. | https://openaccess.thecvf.com/content/CVPR2024/papers/Marcos-Manchon_Open-Vocabulary_Attention_Maps_with_Token_Optimization_for_Semantic_Segmentation_in_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Marcos-Manchon_Open-Vocabulary_Attention_Maps_with_Token_Optimization_for_Semantic_Segmentation_in_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Marcos-Manchon_Open-Vocabulary_Attention_Maps_with_Token_Optimization_for_Semantic_Segmentation_in_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Marcos-Manchon_Open-Vocabulary_Attention_Maps_CVPR_2024_supplemental.pdf | null |
OPERA: Alleviating Hallucination in Multi-Modal Large Language Models via Over-Trust Penalty and Retrospection-Allocation | Qidong Huang, Xiaoyi Dong, Pan Zhang, Bin Wang, Conghui He, Jiaqi Wang, Dahua Lin, Weiming Zhang, Nenghai Yu | Hallucination posed as a pervasive challenge of multi-modal large language models (MLLMs) has significantly impeded their real-world usage that demands precise judgment. Existing methods mitigate this issue with either training with specific designed data or inferencing with external knowledge from other sources incurring inevitable additional costs. In this paper we present OPERA a novel MLLM decoding method grounded in an Over-trust Penalty and a Retrospection-Allocation strategy serving as a nearly free lunch to alleviate the hallucination issue without additional data knowledge or training. Our approach begins with an interesting observation that most hallucinations are closely tied to the knowledge aggregation patterns manifested in the self-attention matrix i.e. MLLMs tend to generate new tokens by focusing on a few summary tokens but not all the previous tokens. Such partial over-trust inclination results in the neglecting of image tokens and describes the image content with hallucination. Based on the observation OPERA introduces a penalty term on the model logits during the beam-search decoding to mitigate the over-trust issue along with a rollback strategy that retrospects the presence of summary tokens in the previously generated tokens and re-allocate the token selection if necessary. With extensive experiments OPERA shows significant hallucination-mitigating performance on different MLLMs and metrics proving its effectiveness and generality. Our code is available at: https://github.com/shikiw/OPERA. | https://openaccess.thecvf.com/content/CVPR2024/papers/Huang_OPERA_Alleviating_Hallucination_in_Multi-Modal_Large_Language_Models_via_Over-Trust_CVPR_2024_paper.pdf | http://arxiv.org/abs/2311.17911 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Huang_OPERA_Alleviating_Hallucination_in_Multi-Modal_Large_Language_Models_via_Over-Trust_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Huang_OPERA_Alleviating_Hallucination_in_Multi-Modal_Large_Language_Models_via_Over-Trust_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Huang_OPERA_Alleviating_Hallucination_CVPR_2024_supplemental.pdf | null |
Volumetric Environment Representation for Vision-Language Navigation | Rui Liu, Wenguan Wang, Yi Yang | Vision-language navigation (VLN) requires an agent to navigate through an 3D environment based on visual observations and natural language instructions. It is clear that the pivotal factor for successful navigation lies in the comprehensive scene understanding. Previous VLN agents employ monocular frameworks to extract 2D features of perspective views directly. Though straightforward they struggle for capturing 3D geometry and semantics leading to a partial and incomplete environment representation. To achieve a comprehensive 3D representation with fine-grained details we introduce a Volumetric Environment Representation (VER) which voxelizes the physical world into structured 3D cells. For each cell VER aggregates multi-view 2D features into such a unified 3D space via 2D-3D sampling. Through coarse-to-fine feature extraction and multi-task learning for VER our agent predicts 3D occupancy 3D room layout and 3D bounding boxes jointly. Based on online collected VERs our agent performs volume state estimation and builds episodic memory for predicting the next step. Experimental results show our environment representations from multi-task learning lead to evident performance gains on VLN. Our model achieves state-of-the-art performance across VLN benchmarks (R2R REVERIE and R4R). | https://openaccess.thecvf.com/content/CVPR2024/papers/Liu_Volumetric_Environment_Representation_for_Vision-Language_Navigation_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.14158 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Liu_Volumetric_Environment_Representation_for_Vision-Language_Navigation_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Liu_Volumetric_Environment_Representation_for_Vision-Language_Navigation_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Liu_Volumetric_Environment_Representation_CVPR_2024_supplemental.pdf | null |
DreamComposer: Controllable 3D Object Generation via Multi-View Conditions | Yunhan Yang, Yukun Huang, Xiaoyang Wu, Yuan-Chen Guo, Song-Hai Zhang, Hengshuang Zhao, Tong He, Xihui Liu | Utilizing pre-trained 2D large-scale generative models recent works are capable of generating high-quality novel views from a single in-the-wild image. However due to the lack of information from multiple views these works encounter difficulties in generating controllable novel views. In this paper we present DreamComposer a flexible and scalable framework that can enhance existing view-aware diffusion models by injecting multi-view conditions. Specifically DreamComposer first uses a view-aware 3D lifting module to obtain 3D representations of an object from multiple views. Then it renders the latent features of the target view from 3D representations with the multi-view feature fusion module. Finally the target view features extracted from multi-view inputs are injected into a pre-trained diffusion model. Experiments show that DreamComposer is compatible with state-of-the-art diffusion models for zero-shot novel view synthesis further enhancing them to generate high-fidelity novel view images with multi-view conditions ready for controllable 3D object reconstruction and various other applications. | https://openaccess.thecvf.com/content/CVPR2024/papers/Yang_DreamComposer_Controllable_3D_Object_Generation_via_Multi-View_Conditions_CVPR_2024_paper.pdf | http://arxiv.org/abs/2312.03611 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Yang_DreamComposer_Controllable_3D_Object_Generation_via_Multi-View_Conditions_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Yang_DreamComposer_Controllable_3D_Object_Generation_via_Multi-View_Conditions_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Yang_DreamComposer_Controllable_3D_CVPR_2024_supplemental.pdf | null |
Self-Calibrating Vicinal Risk Minimisation for Model Calibration | Jiawei Liu, Changkun Ye, Ruikai Cui, Nick Barnes | Model calibration measuring the alignment between the prediction accuracy and model confidence is an important metric reflecting model trustworthiness. Existing dense binary classification methods without proper regularisation of model confidence are prone to being over-confident. To calibrate Deep Neural Networks (DNNs) we propose a Self-Calibrating Vicinal Risk Minimisation (SCVRM) that explores the vicinity space of labeled data where vicinal images that are farther away from labeled images adopt the groundtruth label with decreasing label confidence. We prove that in the logistic regression problem SCVRM can be seen as a Vicinal Risk Minimisation plus a regularisation term that penalises the over-confident predictions. In practical implementation SCVRM is approximated using Monte Carlo sampling that samples additional augmented training images and labels from the vicinal distributions. Experimental results demonstrate that SCVRM can significantly enhance model calibration for different dense classification tasks on both in-distribution and out-of-distribution data. Code is available at https://github.com/Carlisle-Liu/SCVRM. | https://openaccess.thecvf.com/content/CVPR2024/papers/Liu_Self-Calibrating_Vicinal_Risk_Minimisation_for_Model_Calibration_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Liu_Self-Calibrating_Vicinal_Risk_Minimisation_for_Model_Calibration_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Liu_Self-Calibrating_Vicinal_Risk_Minimisation_for_Model_Calibration_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Liu_Self-Calibrating_Vicinal_Risk_CVPR_2024_supplemental.pdf | null |
NeRFDeformer: NeRF Transformation from a Single View via 3D Scene Flows | Zhenggang Tang, Zhongzheng Ren, Xiaoming Zhao, Bowen Wen, Jonathan Tremblay, Stan Birchfield, Alexander Schwing | We present a method for automatically modifying a NeRF representation based on a single observation of a non-rigid transformed version of the original scene. Our method defines the transformation as a 3D flowspecifically as a weighted linear blending of rigid transformations of 3D anchor points that are defined on the surface of the scene. In order to identify anchor points we introduce a novel correspondence algorithm that first matches RGB-based pairs then leverages multi-view information and 3D reprojection to robustly filter false positives in two steps. We also introduce a new dataset for exploring the problem of modifying a NeRF scene through a single observation. Our dataset contains 113 scenes leveraging 47 3D assets.We show that our proposed method outperforms NeRF editing methods as well as diffusion-based methods and we also explore different methods for filtering correspondences. | https://openaccess.thecvf.com/content/CVPR2024/papers/Tang_NeRFDeformer_NeRF_Transformation_from_a_Single_View_via_3D_Scene_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Tang_NeRFDeformer_NeRF_Transformation_from_a_Single_View_via_3D_Scene_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Tang_NeRFDeformer_NeRF_Transformation_from_a_Single_View_via_3D_Scene_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Tang_NeRFDeformer_NeRF_Transformation_CVPR_2024_supplemental.pdf | null |
LPSNet: End-to-End Human Pose and Shape Estimation with Lensless Imaging | Haoyang Ge, Qiao Feng, Hailong Jia, Xiongzheng Li, Xiangjun Yin, You Zhou, Jingyu Yang, Kun Li | Human pose and shape (HPS) estimation with lensless imaging is not only beneficial to privacy protection but also can be used in covert surveillance scenarios due to the small size and simple structure of this device. However this task presents significant challenges due to the inherent ambiguity of the captured measurements and lacks effective methods for directly estimating human pose and shape from lensless data. In this paper we propose the first end-to-end framework to recover 3D human poses and shapes from lensless measurements to our knowledge. We specifically design a multi-scale lensless feature decoder to decode the lensless measurements through the optically encoded mask for efficient feature extraction. We also propose a double-head auxiliary supervision mechanism to improve the estimation accuracy of human limb ends. Besides we establish a lensless imaging system and verify the effectiveness of our method on various datasets acquired by our lensless imaging system. The code and dataset are available at https://cic.tju.edu.cn/faculty/likun/projects/LPSNet. | https://openaccess.thecvf.com/content/CVPR2024/papers/Ge_LPSNet_End-to-End_Human_Pose_and_Shape_Estimation_with_Lensless_Imaging_CVPR_2024_paper.pdf | http://arxiv.org/abs/2404.01941 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Ge_LPSNet_End-to-End_Human_Pose_and_Shape_Estimation_with_Lensless_Imaging_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Ge_LPSNet_End-to-End_Human_Pose_and_Shape_Estimation_with_Lensless_Imaging_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Ge_LPSNet_End-to-End_Human_CVPR_2024_supplemental.pdf | null |
Embracing Unimodal Aleatoric Uncertainty for Robust Multimodal Fusion | Zixian Gao, Xun Jiang, Xing Xu, Fumin Shen, Yujie Li, Heng Tao Shen | As a fundamental problem in multimodal learning multimodal fusion aims to compensate for the inherent limitations of a single modality. One challenge of multimodal fusion is that the unimodal data in their unique embedding space mostly contains potential noise which leads to corrupted cross-modal interactions. However in this paper we show that the potential noise in unimodal data could be well quantified and further employed to enhance more stable unimodal embeddings via contrastive learning. Specifically we propose a novel generic and robust multimodal fusion strategy termed Embracing Aleatoric Uncertainty (EAU) which is simple and can be applied to kinds of modalities. It consists of two key steps: (1) the Stable Unimodal Feature Augmentation (SUFA) that learns a stable unimodal representation by incorporating the aleatoric uncertainty into self-supervised contrastive learning. (2) Robust Multimodal Feature Integration (RMFI) leveraging an information-theoretic strategy to learn a robust compact joint representation. We evaluate our proposed EAU method on five multimodal datasets where the video RGB image text audio and depth image are involved. Extensive experiments demonstrate the EAU method is more noise-resistant than existing multimodal fusion strategies and establishes new state-of-the-art on several benchmarks. | https://openaccess.thecvf.com/content/CVPR2024/papers/Gao_Embracing_Unimodal_Aleatoric_Uncertainty_for_Robust_Multimodal_Fusion_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Gao_Embracing_Unimodal_Aleatoric_Uncertainty_for_Robust_Multimodal_Fusion_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Gao_Embracing_Unimodal_Aleatoric_Uncertainty_for_Robust_Multimodal_Fusion_CVPR_2024_paper.html | CVPR 2024 | null | null |
Unifying Correspondence Pose and NeRF for Generalized Pose-Free Novel View Synthesis | Sunghwan Hong, Jaewoo Jung, Heeseong Shin, Jiaolong Yang, Seungryong Kim, Chong Luo | This work delves into the task of pose-free novel view synthesis from stereo pairs a challenging and pioneering task in 3D vision. Our innovative framework unlike any before seamlessly integrates 2D correspondence matching camera pose estimation and NeRF rendering fostering a synergistic enhancement of these tasks. We achieve this through designing an architecture that utilizes a shared representation which serves as a foundation for enhanced 3D geometry understanding. Capitalizing on the inherent interplay between the tasks our unified framework is trained end-to-end with the proposed training strategy to improve overall model accuracy. Through extensive evaluations across diverse indoor and outdoor scenes from two real-world datasets we demonstrate that our approach achieves substantial improvement over previous methodologies especially in scenarios characterized by extreme viewpoint changes and the absence of accurate camera poses. | https://openaccess.thecvf.com/content/CVPR2024/papers/Hong_Unifying_Correspondence_Pose_and_NeRF_for_Generalized_Pose-Free_Novel_View_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Hong_Unifying_Correspondence_Pose_and_NeRF_for_Generalized_Pose-Free_Novel_View_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Hong_Unifying_Correspondence_Pose_and_NeRF_for_Generalized_Pose-Free_Novel_View_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Hong_Unifying_Correspondence_Pose_CVPR_2024_supplemental.pdf | null |
Draw Step by Step: Reconstructing CAD Construction Sequences from Point Clouds via Multimodal Diffusion. | Weijian Ma, Shuaiqi Chen, Yunzhong Lou, Xueyang Li, Xiangdong Zhou | Reconstructing CAD construction sequences from raw 3D geometry serves as an interface between real-world objects and digital designs. In this paper we propose CAD-Diffuser a multimodal diffusion scheme aiming at integrating top-down design paradigm into generative reconstruction. In particular we unify CAD point clouds and CAD construction sequences at the token level guiding our proposed multimodal diffusion strategy to understand and link between the geometry and the design intent concentrated in construction sequences. Leveraging the strong decoding abilities of language models the forward process is modeled as a random walk between the original token and the [MASK] token while the reverse process naturally fits the masked token modeling scheme. A volume-based noise schedule is designed to encourage outline-first generation decomposing the top-down design methodology into a machine-understandable procedure. For tokenizing CAD data of multiple modalities we introduce a tokenizer with a self-supervised face segmentation task to compress local and global geometric information for CAD point clouds and the CAD construction sequence is transformed into a primitive token string. Experimental results show that our CAD-Diffuser can perceive geometric details and the results are more likely to be reused by human designers. | https://openaccess.thecvf.com/content/CVPR2024/papers/Ma_Draw_Step_by_Step_Reconstructing_CAD_Construction_Sequences_from_Point_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Ma_Draw_Step_by_Step_Reconstructing_CAD_Construction_Sequences_from_Point_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Ma_Draw_Step_by_Step_Reconstructing_CAD_Construction_Sequences_from_Point_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Ma_Draw_Step_by_CVPR_2024_supplemental.pdf | null |
DiffusionTrack: Point Set Diffusion Model for Visual Object Tracking | Fei Xie, Zhongdao Wang, Chao Ma | Existing Siamese or transformer trackers commonly pose visual object tracking as a one-shot detection problem i.e. locating the target object in a single forward evaluation scheme. Despite the demonstrated success these trackers may easily drift towards distractors with similar appearance due to the single forward evaluation scheme lacking self-correction. To address this issue we cast visual tracking as a point set based denoising diffusion process and propose a novel generative learning based tracker dubbed DiffusionTrack. Our DiffusionTrack possesses two appealing properties: 1) It follows a novel noise-to-target tracking paradigm that leverages multiple denoising diffusion steps to localize the target in a dynamic searching manner per frame. 2) It models the diffusion process using a point set representation which can better handle appearance variations for more precise localization. One side benefit is that DiffusionTrack greatly simplifies the post-processing e.g. removing window penalty scheme. Without bells and whistles our DiffusionTrack achieves leading performance over the state-of-the-art trackers and runs in real-time. The code is in https://github.com/VISION-SJTU/DiffusionTrack. | https://openaccess.thecvf.com/content/CVPR2024/papers/Xie_DiffusionTrack_Point_Set_Diffusion_Model_for_Visual_Object_Tracking_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Xie_DiffusionTrack_Point_Set_Diffusion_Model_for_Visual_Object_Tracking_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Xie_DiffusionTrack_Point_Set_Diffusion_Model_for_Visual_Object_Tracking_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Xie_DiffusionTrack_Point_Set_CVPR_2024_supplemental.pdf | null |
Towards a Simultaneous and Granular Identity-Expression Control in Personalized Face Generation | Renshuai Liu, Bowen Ma, Wei Zhang, Zhipeng Hu, Changjie Fan, Tangjie Lv, Yu Ding, Xuan Cheng | In human-centric content generation the pre-trained text-to-image models struggle to produce user-wanted portrait images which retain the identity of individuals while exhibiting diverse expressions. This paper introduces our efforts towards personalized face generation. To this end we propose a novel multi-modal face generation framework capable of simultaneous identity-expression control and more fine-grained expression synthesis. Our expression control is so sophisticated that it can be specialized by the fine-grained emotional vocabulary. We devise a novel diffusion model that can undertake the task of simultaneously face swapping and reenactment. Due to the entanglement of identity and expression separately and precisely controlling them within one framework is a nontrivial task thus has not been explored yet. To overcome this we propose several innovative designs in the conditional diffusion model including balancing identity and expression encoder improved midpoint sampling and explicitly background conditioning. Extensive experiments have demonstrated the controllability and scalability of the proposed framework in comparison with state-of-the-art text-to-image face swapping and face reenactment methods. | https://openaccess.thecvf.com/content/CVPR2024/papers/Liu_Towards_a_Simultaneous_and_Granular_Identity-Expression_Control_in_Personalized_Face_CVPR_2024_paper.pdf | http://arxiv.org/abs/2401.01207 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Liu_Towards_a_Simultaneous_and_Granular_Identity-Expression_Control_in_Personalized_Face_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Liu_Towards_a_Simultaneous_and_Granular_Identity-Expression_Control_in_Personalized_Face_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Liu_Towards_a_Simultaneous_CVPR_2024_supplemental.pdf | null |
PEEKABOO: Interactive Video Generation via Masked-Diffusion | Yash Jain, Anshul Nasery, Vibhav Vineet, Harkirat Behl | Modern video generation models like Sora have achieved remarkable success in producing high-quality videos. However a significant limitation is their inability to offer interactive control to users a feature that promises to open up unprecedented applications and creativity. In this work we introduce the first solution to equip diffusion-based video generation models with spatio-temporal control. We present Peekaboo a novel masked attention module which seamlessly integrates with current video generation models offering control without the need for additional training or inference overhead. To facilitate future research we also introduce a comprehensive benchmark for interactive video generation. This benchmark offers a standardized framework for the community to assess the efficacy of emerging interactive video generation models. Our extensive qualitative and quantitative assessments reveal that Peekaboo achieves up to a 3.8x improvement in mIoU over baseline models all while maintaining the same latency. Code and benchmark are available on the webpage. | https://openaccess.thecvf.com/content/CVPR2024/papers/Jain_PEEKABOO_Interactive_Video_Generation_via_Masked-Diffusion_CVPR_2024_paper.pdf | http://arxiv.org/abs/2312.07509 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Jain_PEEKABOO_Interactive_Video_Generation_via_Masked-Diffusion_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Jain_PEEKABOO_Interactive_Video_Generation_via_Masked-Diffusion_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Jain_PEEKABOO_Interactive_Video_CVPR_2024_supplemental.zip | null |
Scaling Diffusion Models to Real-World 3D LiDAR Scene Completion | Lucas Nunes, Rodrigo Marcuzzi, Benedikt Mersch, Jens Behley, Cyrill Stachniss | Computer vision techniques play a central role in the perception stack of autonomous vehicles. Such methods are employed to perceive the vehicle surroundings given sensor data. 3D LiDAR sensors are commonly used to collect sparse 3D point clouds from the scene. However compared to human perception such systems struggle to deduce the unseen parts of the scene given those sparse point clouds. In this matter the scene completion task aims at predicting the gaps in the LiDAR measurements to achieve a more complete scene representation. Given the promising results of recent diffusion models as generative models for images we propose extending them to achieve scene completion from a single 3D LiDAR scan. Previous works used diffusion models over range images extracted from LiDAR data directly applying image-based diffusion methods. Distinctly we propose to directly operate on the points reformulating the noising and denoising diffusion process such that it can efficiently work at scene scale. Together with our approach we propose a regularization loss to stabilize the noise predicted during the denoising process. Our experimental evaluation shows that our method can complete the scene given a single LiDAR scan as input producing a scene with more details compared to state-of-the-art scene completion methods. We believe that our proposed diffusion process formulation can support further research in diffusion models applied to scene-scale point cloud data. | https://openaccess.thecvf.com/content/CVPR2024/papers/Nunes_Scaling_Diffusion_Models_to_Real-World_3D_LiDAR_Scene_Completion_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.13470 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Nunes_Scaling_Diffusion_Models_to_Real-World_3D_LiDAR_Scene_Completion_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Nunes_Scaling_Diffusion_Models_to_Real-World_3D_LiDAR_Scene_Completion_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Nunes_Scaling_Diffusion_Models_CVPR_2024_supplemental.pdf | null |
Discriminative Pattern Calibration Mechanism for Source-Free Domain Adaptation | Haifeng Xia, Siyu Xia, Zhengming Ding | Source-free domain adaptation (SFDA) assumes that model adaptation only accesses the well-learned source model and unlabeled target instances for knowledge transfer. However cross-domain distribution shift easily triggers invalid discriminative semantics from source model on recognizing the target samples. Hence understanding the specific content of discriminative pattern and adjusting their representation in target domain become the important key to overcome SFDA. To achieve such a vision this paper proposes a novel explanation paradigm "Discriminative Pattern Calibration (DPC)" mechanism on solving SFDA issue. Concretely DPC first utilizes learning network to infer the discriminative regions on the target images and specifically emphasizes them in feature space to enhance their representation. Moreover DPC relies on the attention-reversed mixup mechanism to augment more samples and improve the robustness of the classifier. Considerable experimental results and studies suggest that the effectiveness of our DPC in enhancing the performance of existing SFDA baselines. | https://openaccess.thecvf.com/content/CVPR2024/papers/Xia_Discriminative_Pattern_Calibration_Mechanism_for_Source-Free_Domain_Adaptation_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Xia_Discriminative_Pattern_Calibration_Mechanism_for_Source-Free_Domain_Adaptation_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Xia_Discriminative_Pattern_Calibration_Mechanism_for_Source-Free_Domain_Adaptation_CVPR_2024_paper.html | CVPR 2024 | null | null |
Deep Generative Model based Rate-Distortion for Image Downscaling Assessment | Yuanbang Liang, Bhavesh Garg, Paul Rosin, Yipeng Qin | In this paper we propose Image Downscaling Assessment by Rate-Distortion (IDA-RD) a novel measure to quantitatively evaluate image downscaling algorithms. In contrast to image-based methods that measure the quality of downscaled images ours is process-based that draws ideas from rate-distortion theory to measure the distortion incurred during downscaling. Our main idea is that downscaling and super-resolution (SR) can be viewed as the encoding and decoding processes in the rate-distortion model respectively and that a downscaling algorithm that preserves more details in the resulting low-resolution (LR) images should lead to less distorted high-resolution (HR) images in SR. In other words the distortion should increase as the downscaling algorithm deteriorates. However it is non-trivial to measure this distortion as it requires the SR algorithm to be blind and stochastic. Our key insight is that such requirements can be met by recent SR algorithms based on deep generative models that can find all matching HR images for a given LR image on their learned image manifolds. Extensive experimental results show the effectiveness of our IDA-RD measure. | https://openaccess.thecvf.com/content/CVPR2024/papers/Liang_Deep_Generative_Model_based_Rate-Distortion_for_Image_Downscaling_Assessment_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.15139 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Liang_Deep_Generative_Model_based_Rate-Distortion_for_Image_Downscaling_Assessment_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Liang_Deep_Generative_Model_based_Rate-Distortion_for_Image_Downscaling_Assessment_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Liang_Deep_Generative_Model_CVPR_2024_supplemental.pdf | null |
Physical Backdoor: Towards Temperature-based Backdoor Attacks in the Physical World | Wen Yin, Jian Lou, Pan Zhou, Yulai Xie, Dan Feng, Yuhua Sun, Tailai Zhang, Lichao Sun | Backdoor attacks have been well-studied in visible light object detection (VLOD) in recent years. However VLOD can not effectively work in dark and temperature-sensitive scenarios. Instead thermal infrared object detection (TIOD) is the most accessible and practical in such environments. In this paper our team is the first to investigate the security vulnerabilities associated with TIOD in the context of backdoor attacks spanning both the digital and physical realms. We introduce two novel types of backdoor attacks on TIOD each offering unique capabilities: Object-affecting Attack and Range-affecting Attack. We conduct a comprehensive analysis of key factors influencing trigger design which include temperature size material and concealment. These factors especially temperature significantly impact the efficacy of backdoor attacks on TIOD. A thorough understanding of these factors will serve as a foundation for designing physical triggers and temperature controlling experiments. Our study includes extensive experiments conducted in both digital and physical environments. In the digital realm we evaluate our approach using benchmark datasets for TIOD achieving an Attack Success Rate (ASR) of up to 98.21%. In the physical realm we test our approach in two real-world settings: a traffic intersection and a parking lot using a thermal infrared camera. Here we attain an ASR of up to 98.38%. | https://openaccess.thecvf.com/content/CVPR2024/papers/Yin_Physical_Backdoor_Towards_Temperature-based_Backdoor_Attacks_in_the_Physical_World_CVPR_2024_paper.pdf | http://arxiv.org/abs/2404.19417 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Yin_Physical_Backdoor_Towards_Temperature-based_Backdoor_Attacks_in_the_Physical_World_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Yin_Physical_Backdoor_Towards_Temperature-based_Backdoor_Attacks_in_the_Physical_World_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Yin_Physical_Backdoor_Towards_CVPR_2024_supplemental.pdf | null |
Make Me a BNN: A Simple Strategy for Estimating Bayesian Uncertainty from Pre-trained Models | Gianni Franchi, Olivier Laurent, Maxence Leguery, Andrei Bursuc, Andrea Pilzer, Angela Yao | Deep Neural Networks (DNNs) are powerful tools for various computer vision tasks yet they often struggle with reliable uncertainty quantification -a critical requirement for real-world applications. Bayesian Neural Networks (BNN) are equipped for uncertainty estimation but cannot scale to large DNNs where they are highly unstable to train. To address this challenge we introduce the Adaptable Bayesian Neural Network (ABNN) a simple and scalable strategy to seamlessly transform DNNs into BNNs in a post-hoc manner with minimal computational and training overheads. ABNN preserves the main predictive properties of DNNs while enhancing their uncertainty quantification abilities through simple BNN adaptation layers (attached to normalization layers) and a few fine-tuning steps on pre-trained models. We conduct extensive experiments across multiple datasets for image classification and semantic segmentation tasks and our results demonstrate that ABNN achieves state-of-the-art performance without the computational budget typically associated with ensemble methods. | https://openaccess.thecvf.com/content/CVPR2024/papers/Franchi_Make_Me_a_BNN_A_Simple_Strategy_for_Estimating_Bayesian_CVPR_2024_paper.pdf | http://arxiv.org/abs/2312.15297 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Franchi_Make_Me_a_BNN_A_Simple_Strategy_for_Estimating_Bayesian_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Franchi_Make_Me_a_BNN_A_Simple_Strategy_for_Estimating_Bayesian_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Franchi_Make_Me_a_CVPR_2024_supplemental.pdf | null |
Language-only Training of Zero-shot Composed Image Retrieval | Geonmo Gu, Sanghyuk Chun, Wonjae Kim, Yoohoon Kang, Sangdoo Yun | Composed image retrieval (CIR) task takes a composed query of image and text aiming to search relative images for both conditions. Conventional CIR approaches need a training dataset composed of triplets of query image query text and target image which is very expensive to collect. Several recent works have worked on the zero-shot (ZS) CIR paradigm to tackle the issue without using pre-collected triplets. However the existing ZS-CIR methods show limited backbone scalability and generalizability due to the lack of diversity of the input texts during training. We propose a novel CIR framework only using language for its training. Our LinCIR (Language-only training for CIR) can be trained only with text datasets by a novel self-supervision named self-masking projection (SMP). We project the text latent embedding to the token embedding space and construct a new text by replacing the keyword tokens of the original text. Then we let the new and original texts have the same latent embedding vector. With this simple strategy LinCIR is surprisingly efficient and highly effective; LinCIR with CLIP ViT-G backbone is trained in 48 minutes and shows the best ZS-CIR performances on four different CIR benchmarks CIRCO GeneCIS FashionIQ and CIRR even outperforming supervised method on FashionIQ. Code is available at https://github.com/navervision/lincir | https://openaccess.thecvf.com/content/CVPR2024/papers/Gu_Language-only_Training_of_Zero-shot_Composed_Image_Retrieval_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Gu_Language-only_Training_of_Zero-shot_Composed_Image_Retrieval_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Gu_Language-only_Training_of_Zero-shot_Composed_Image_Retrieval_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Gu_Language-only_Training_of_CVPR_2024_supplemental.pdf | null |
EFHQ: Multi-purpose ExtremePose-Face-HQ dataset | Trung Tuan Dao, Duc Hong Vu, Cuong Pham, Anh Tran | The existing facial datasets while having plentiful images at near frontal views lack images with extreme head poses leading to the downgraded performance of deep learning models when dealing with profile or pitched faces. This work aims to address this gap by introducing a novel dataset named Extreme Pose Face High-Quality Dataset (EFHQ) which includes a maximum of 450k high-quality images of faces at extreme poses. To produce such a massive dataset we utilize a novel and meticulous dataset processing pipeline to curate two publicly available datasets VFHQ and CelebV-HQ which contain many high-resolution face videos captured in various settings. Our dataset can complement existing datasets on various facial-related tasks such as facial synthesis with 2D/3D-aware GAN diffusion-based text-to-image face generation and face reenactment. Specifically training with EFHQ helps models generalize well across diverse poses significantly improving performance in scenarios involving extreme views confirmed by extensive experiments. Additionally we utilize EFHQ to define a challenging cross-view face verification benchmark in which the performance of SOTA face recognition models drops 5-37% compared to frontal-to-frontal scenarios aiming to stimulate studies on face recognition under severe pose conditions in the wild. | https://openaccess.thecvf.com/content/CVPR2024/papers/Dao_EFHQ_Multi-purpose_ExtremePose-Face-HQ_dataset_CVPR_2024_paper.pdf | http://arxiv.org/abs/2312.17205 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Dao_EFHQ_Multi-purpose_ExtremePose-Face-HQ_dataset_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Dao_EFHQ_Multi-purpose_ExtremePose-Face-HQ_dataset_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Dao_EFHQ_Multi-purpose_ExtremePose-Face-HQ_CVPR_2024_supplemental.pdf | null |
Dynamic Cues-Assisted Transformer for Robust Point Cloud Registration | Hong Chen, Pei Yan, Sihe Xiang, Yihua Tan | Point Cloud Registration is a critical and challenging task in computer vision. Recent advancements have predominantly embraced a coarse-to-fine matching mechanism with the key to matching the superpoints located in patches with inter-frame consistent structures. However previous methods still face challenges with ambiguous matching because the interference information aggregated from irrelevant regions may disturb the capture of inter-frame consistency relations leading to wrong matches. To address this issue we propose Dynamic Cues-Assisted Transformer (DCATr). Firstly the interference from irrelevant regions is greatly reduced by constraining attention to certain cues i.e. regions with highly correlated structures of potential corresponding superpoints. Secondly cues-assisted attention is designed to mine the inter-frame consistency relations while more attention is assigned to pairs with high consistent confidence in feature aggregation. Finally a dynamic updating fashion is proposed to facilitate mining richer consistency information further improving aggregated features' distinctiveness and relieving matching ambiguity. Extensive evaluations on indoor and outdoor standard benchmarks demonstrate that DCATr outperforms all state-of-the-art methods. | https://openaccess.thecvf.com/content/CVPR2024/papers/Chen_Dynamic_Cues-Assisted_Transformer_for_Robust_Point_Cloud_Registration_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Chen_Dynamic_Cues-Assisted_Transformer_for_Robust_Point_Cloud_Registration_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Chen_Dynamic_Cues-Assisted_Transformer_for_Robust_Point_Cloud_Registration_CVPR_2024_paper.html | CVPR 2024 | null | null |
Patch2Self2: Self-supervised Denoising on Coresets via Matrix Sketching | Shreyas Fadnavis, Agniva Chowdhury, Joshua Batson, Petros Drineas, Eleftherios Garyfallidis | Diffusion MRI (dMRI) non-invasively maps brain white matter yet necessitates denoising due to low signal-to-noise ratios. Patch2Self (P2S) employing self-supervised techniques and regression on a Casorati matrix effectively denoises dMRI images and has become the new de-facto standard in this field. P2S however is resource intensive both in terms of running time and memory usage as it uses all voxels (n) from all-but-one held-in volumes (d-1) to learn a linear mapping Phi : \mathbb R ^ n x(d-1) \mapsto \mathbb R ^ n for denoising the held-out volume. The increasing size and dimensionality of higher resolution dMRI acquisitions can make P2S infeasible for large-scale analyses. This work exploits the redundancy imposed by P2S to alleviate its performance issues and inspect regions that influence the noise disproportionately. Specifically this study makes a three-fold contribution: (1) We present Patch2Self2 (P2S2) a method that uses matrix sketching to perform self-supervised denoising. By solving a sub-problem on a smaller sub-space so called coreset we show how P2S2 can yield a significant speedup in training time while using less memory. (2) We present a theoretical analysis of P2S2 focusing on determining the optimal sketch size through rank estimation a key step in achieving a balance between denoising accuracy and computational efficiency. (3) We show how the so-called statistical leverage scores can be used to interpret the denoising of dMRI data a process that was traditionally treated as a black-box. Experimental results on both simulated and real data affirm that P2S2 maintains denoising quality while significantly enhancing speed and memory efficiency achieved by training on a reduced data subset. | https://openaccess.thecvf.com/content/CVPR2024/papers/Fadnavis_Patch2Self2_Self-supervised_Denoising_on_Coresets_via_Matrix_Sketching_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Fadnavis_Patch2Self2_Self-supervised_Denoising_on_Coresets_via_Matrix_Sketching_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Fadnavis_Patch2Self2_Self-supervised_Denoising_on_Coresets_via_Matrix_Sketching_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Fadnavis_Patch2Self2_Self-supervised_Denoising_CVPR_2024_supplemental.pdf | null |
High-fidelity Person-centric Subject-to-Image Synthesis | Yibin Wang, Weizhong Zhang, Jianwei Zheng, Cheng Jin | Current subject-driven image generation methods encounter significant challenges in person-centric image generation. The reason is that they learn the semantic scene and person generation by fine-tuning a common pre-trained diffusion which involves an irreconcilable training imbalance. Precisely to generate realistic persons they need to sufficiently tune the pre-trained model which inevitably causes the model to forget the rich semantic scene prior and makes scene generation over-fit to the training data. Moreover even with sufficient fine-tuning these methods can still not generate high-fidelity persons since joint learning of the scene and person generation also lead to quality compromise. In this paper we propose Face-diffuser an effective collaborative generation pipeline to eliminate the above training imbalance and quality compromise. Specifically we first develop two specialized pre-trained diffusion models i.e. Text-driven Diffusion Model (TDM) and Subject-augmented Diffusion Model (SDM) for scene and person generation respectively. The sampling process is divided into three sequential stages i.e. semantic scene construction subject-scene fusion and subject enhancement. The first and last stages are performed by TDM and SDM respectively. The subject-scene fusion stage that is the collaboration achieved through a novel and highly effective mechanism Saliency-adaptive Noise Fusion (SNF). Specifically it is based on our key observation that there exists a robust link between classifier-free guidance responses and the saliency of generated images. In each time step SNF leverages the unique strengths of each model and allows for the spatial blending of predicted noises from both models automatically in a saliency-aware manner all of which can be seamlessly integrated into the DDIM sampling process. Extensive experiments confirm the impressive effectiveness and robustness of the Face-diffuser in generating high-fidelity person images depicting multiple unseen persons with varying contexts. Code is available at https://github.com/CodeGoat24/Face-diffuser. | https://openaccess.thecvf.com/content/CVPR2024/papers/Wang_High-fidelity_Person-centric_Subject-to-Image_Synthesis_CVPR_2024_paper.pdf | http://arxiv.org/abs/2311.10329 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Wang_High-fidelity_Person-centric_Subject-to-Image_Synthesis_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Wang_High-fidelity_Person-centric_Subject-to-Image_Synthesis_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Wang_High-fidelity_Person-centric_Subject-to-Image_CVPR_2024_supplemental.pdf | null |
The Devil is in the Fine-Grained Details: Evaluating Open-Vocabulary Object Detectors for Fine-Grained Understanding | Lorenzo Bianchi, Fabio Carrara, Nicola Messina, Claudio Gennaro, Fabrizio Falchi | Recent advancements in large vision-language models enabled visual object detection in open-vocabulary scenarios where object classes are defined in free-text formats during inference. In this paper we aim to probe the state-of-the-art methods for open-vocabulary object detection to determine to what extent they understand fine-grained properties of objects and their parts. To this end we introduce an evaluation protocol based on dynamic vocabulary generation to test whether models detect discern and assign the correct fine-grained description to objects in the presence of hard-negative classes. We contribute with a benchmark suite of increasing difficulty and probing different properties like color pattern and material. We further enhance our investigation by evaluating several state-of-the-art open-vocabulary object detectors using the proposed protocol and find that most existing solutions which shine in standard open-vocabulary benchmarks struggle to accurately capture and distinguish finer object details. We conclude the paper by highlighting the limitations of current methodologies and exploring promising research directions to overcome the discovered drawbacks. Data and code are available at https://lorebianchi98.github.io/FG-OVD . | https://openaccess.thecvf.com/content/CVPR2024/papers/Bianchi_The_Devil_is_in_the_Fine-Grained_Details_Evaluating_Open-Vocabulary_Object_CVPR_2024_paper.pdf | http://arxiv.org/abs/2311.17518 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Bianchi_The_Devil_is_in_the_Fine-Grained_Details_Evaluating_Open-Vocabulary_Object_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Bianchi_The_Devil_is_in_the_Fine-Grained_Details_Evaluating_Open-Vocabulary_Object_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Bianchi_The_Devil_is_CVPR_2024_supplemental.pdf | null |
Efficient and Effective Weakly-Supervised Action Segmentation via Action-Transition-Aware Boundary Alignment | Angchi Xu, Wei-Shi Zheng | Weakly-supervised action segmentation is a task of learning to partition a long video into several action segments where training videos are only accompanied by transcripts (ordered list of actions). Most of existing methods need to infer pseudo segmentation for training by serial alignment between all frames and the transcript which is time-consuming and hard to be parallelized while training. In this work we aim to escape from this inefficient alignment with massive but redundant frames and instead to directly localize a few action transitions for pseudo segmentation generation where a transition refers to the change from an action segment to its next adjacent one in the transcript. As the true transitions are submerged in noisy boundaries due to intra-segment visual variation we propose a novel Action-Transition-Aware Boundary Alignment (ATBA) framework to efficiently and effectively filter out noisy boundaries and detect transitions. In addition to boost the semantic learning in the case that noise is inevitably present in the pseudo segmentation we also introduce video-level losses to utilize the trusted video-level supervision. Extensive experiments show the effectiveness of our approach on both performance and training speed. | https://openaccess.thecvf.com/content/CVPR2024/papers/Xu_Efficient_and_Effective_Weakly-Supervised_Action_Segmentation_via_Action-Transition-Aware_Boundary_Alignment_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.19225 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Xu_Efficient_and_Effective_Weakly-Supervised_Action_Segmentation_via_Action-Transition-Aware_Boundary_Alignment_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Xu_Efficient_and_Effective_Weakly-Supervised_Action_Segmentation_via_Action-Transition-Aware_Boundary_Alignment_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Xu_Efficient_and_Effective_CVPR_2024_supplemental.pdf | null |
Link-Context Learning for Multimodal LLMs | Yan Tai, Weichen Fan, Zhao Zhang, Ziwei Liu | The ability to learn from context with novel concepts and deliver appropriate responses are essential in human conversations. Despite current Multimodal Large Language Models (MLLMs) and Large Language Models (LLMs) being trained on mega-scale datasets recognizing unseen images or understanding novel concepts in a training-free manner remains a challenge. In-Context Learning (ICL) explores training-free few-shot learning where models are encouraged to "learn to learn" from limited tasks and generalize to unseen tasks. In this work we propose link-context learning (LCL) which emphasizes "reasoning from cause and effect" to augment the learning capabilities of MLLMs. LCL goes beyond traditional ICL by explicitly strengthening the causal relationship between the support set and the query set. By providing demonstrations with causal links LCL guides the model to discern not only the analogy but also the underlying causal associations between data points which empowers MLLMs to recognize unseen images and understand novel concepts more effectively. To facilitate the evaluation of this novel approach we introduce the ISEKAI dataset comprising exclusively of unseen generated image-label pairs designed for link-context learning. Extensive experiments show that our LCL-MLLM exhibits strong link-context learning capabilities to novel concepts over vanilla MLLMs. | https://openaccess.thecvf.com/content/CVPR2024/papers/Tai_Link-Context_Learning_for_Multimodal_LLMs_CVPR_2024_paper.pdf | http://arxiv.org/abs/2308.07891 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Tai_Link-Context_Learning_for_Multimodal_LLMs_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Tai_Link-Context_Learning_for_Multimodal_LLMs_CVPR_2024_paper.html | CVPR 2024 | null | null |
Pixel-Aligned Language Model | Jiarui Xu, Xingyi Zhou, Shen Yan, Xiuye Gu, Anurag Arnab, Chen Sun, Xiaolong Wang, Cordelia Schmid | Large language models have achieved great success in recent years so as their variants in vision. Existing vision-language models can describe images in natural languages answer visual-related questions or perform complex reasoning about the image. However it is yet unclear how localization tasks such as word grounding or referring localization can be performed using large language models. In this work we aim to develop a vision-language model that can take locations for example a set of points or boxes as either inputs or outputs. When taking locations as inputs the model performs location-conditioned captioning which generates captions for the indicated object or region. When generating locations as outputs our model regresses pixel coordinates for each output word generated by the language model and thus performs dense word grounding. Our model is pre-trained on the Localized Narrative dataset which contains pixel-word-aligned captioning from human attention. We show our model can be applied to various location-aware vision-language tasks including referring localization location-conditioned captioning and dense object captioning archiving state-of-the-art performance on RefCOCO and Visual Genome. | https://openaccess.thecvf.com/content/CVPR2024/papers/Xu_Pixel-Aligned_Language_Model_CVPR_2024_paper.pdf | http://arxiv.org/abs/2312.09237 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Xu_Pixel-Aligned_Language_Model_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Xu_Pixel-Aligned_Language_Model_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Xu_Pixel-Aligned_Language_Model_CVPR_2024_supplemental.pdf | null |
JeDi: Joint-Image Diffusion Models for Finetuning-Free Personalized Text-to-Image Generation | Yu Zeng, Vishal M. Patel, Haochen Wang, Xun Huang, Ting-Chun Wang, Ming-Yu Liu, Yogesh Balaji | Personalized text-to-image generation models enable users to create images that depict their individual possessions in diverse scenes finding applications in various domains. To achieve the personalization capability existing methods rely on finetuning a text-to-image foundation model on a user's custom dataset which can be non-trivial for general users resource-intensive and time-consuming. Despite attempts to develope finetuning-free methods their generation quality is much lower compared to their finetuning counterparts. In this paper we propose Joint-Image Diffusion (\jedi) an effective technique for learning a finetuning-free personalization model. Our key idea is to learn the joint distribution of multiple related text-image pairs that share a common subject. To facilitate learning we propose a scalable synthetic dataset generation technique. Once trained our model enables fast and easy personalization at test time by simply using reference images as input during the sampling process. Our approach does not require any expensive optimization process or additional modules and can faithfully preserve the identity represented by any number of reference images. Experimental results show that our model achieves state-of-the-art generation quality both quantitatively and qualitatively significantly outperforming both the prior finetuning-based and finetuning-free personalization baselines. | https://openaccess.thecvf.com/content/CVPR2024/papers/Zeng_JeDi_Joint-Image_Diffusion_Models_for_Finetuning-Free_Personalized_Text-to-Image_Generation_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Zeng_JeDi_Joint-Image_Diffusion_Models_for_Finetuning-Free_Personalized_Text-to-Image_Generation_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Zeng_JeDi_Joint-Image_Diffusion_Models_for_Finetuning-Free_Personalized_Text-to-Image_Generation_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Zeng_JeDi_Joint-Image_Diffusion_CVPR_2024_supplemental.pdf | null |
ConsistDreamer: 3D-Consistent 2D Diffusion for High-Fidelity Scene Editing | Jun-Kun Chen, Samuel Rota Bulò, Norman Müller, Lorenzo Porzi, Peter Kontschieder, Yu-Xiong Wang | This paper proposes ConsistDreamer - a novel framework that lifts 2D diffusion models with 3D awareness and 3D consistency thus enabling high-fidelity instruction-guided scene editing. To overcome the fundamental limitation of missing 3D consistency in 2D diffusion models our key insight is to introduce three synergetic strategies that augment the input of the 2D diffusion model to become 3D-aware and to explicitly enforce 3D consistency during the training process. Specifically we design surrounding views as context-rich input for the 2D diffusion model and generate 3D-consistent structured noise instead of image-independent noise. Moreover we introduce self-supervised consistency-enforcing training within the per-scene editing procedure. Extensive evaluation shows that our ConsistDreamer achieves state-of-the-art performance for instruction-guided scene editing across various scenes and editing instructions particularly in complicated large-scale indoor scenes from ScanNet++ with significantly improved sharpness and fine-grained textures. Notably ConsistDreamer stands as the first work capable of successfully editing complex (e.g. plaid/checkered) patterns. | https://openaccess.thecvf.com/content/CVPR2024/papers/Chen_ConsistDreamer_3D-Consistent_2D_Diffusion_for_High-Fidelity_Scene_Editing_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Chen_ConsistDreamer_3D-Consistent_2D_Diffusion_for_High-Fidelity_Scene_Editing_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Chen_ConsistDreamer_3D-Consistent_2D_Diffusion_for_High-Fidelity_Scene_Editing_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Chen_ConsistDreamer_3D-Consistent_2D_CVPR_2024_supplemental.pdf | null |
HandDiff: 3D Hand Pose Estimation with Diffusion on Image-Point Cloud | Wencan Cheng, Hao Tang, Luc Van Gool, Jong Hwan Ko | Extracting keypoint locations from input hand frames known as 3D hand pose estimation is a critical task in various human-computer interaction applications. Essentially the 3D hand pose estimation can be regarded as a 3D point subset generative problem conditioned on input frames. Thanks to the recent significant progress on diffusion-based generative models hand pose estimation can also benefit from the diffusion model to estimate keypoint locations with high quality. However directly deploying the existing diffusion models to solve hand pose estimation is non-trivial since they cannot achieve the complex permutation mapping and precise localization. Based on this motivation this paper proposes HandDiff a diffusion-based hand pose estimation model that iteratively denoises accurate hand pose conditioned on hand-shaped image-point clouds. In order to recover keypoint permutation and accurate location we further introduce joint-wise condition and local detail condition. Experimental results demonstrate that the proposed HandDiff significantly outperforms the existing approaches on four challenging hand pose benchmark datasets. Codes and pre-trained models are publicly available at https://github.com/cwc1260/HandDiff. | https://openaccess.thecvf.com/content/CVPR2024/papers/Cheng_HandDiff_3D_Hand_Pose_Estimation_with_Diffusion_on_Image-Point_Cloud_CVPR_2024_paper.pdf | http://arxiv.org/abs/2404.03159 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Cheng_HandDiff_3D_Hand_Pose_Estimation_with_Diffusion_on_Image-Point_Cloud_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Cheng_HandDiff_3D_Hand_Pose_Estimation_with_Diffusion_on_Image-Point_Cloud_CVPR_2024_paper.html | CVPR 2024 | null | null |
SNIDA: Unlocking Few-Shot Object Detection with Non-linear Semantic Decoupling Augmentation | Yanjie Wang, Xu Zou, Luxin Yan, Sheng Zhong, Jiahuan Zhou | Once only a few-shot annotated samples are available the performance of learning-based object detection would be heavily dropped. Many few-shot object detection (FSOD) methods have been proposed to tackle this issue by adopting image-level augmentations in linear manners. Nevertheless those handcrafted enhancements often suffer from limited diversity and lack of semantic awareness resulting in unsatisfactory performance. To this end we propose a Semantic-guided Non-linear Instance-level Data Augmentation method (SNIDA) for FSOD by decoupling the foreground and background to increase their diversities respectively. We design a semantic awareness enhancement strategy to separate objects from backgrounds. Concretely masks of instances are extracted by an unsupervised semantic segmentation module. Then the diversity of samples would be improved by fusing instances into different backgrounds. Considering the shortcomings of augmenting images in a limited transformation space of existing traditional data augmentation methods we introduce an object reconstruction enhancement module. The aim of this module is to generate sufficient diversity and non-linear training data at the instance level through a semantic-guided masked autoencoder. In this way the potential of data can be fully exploited in various object detection scenarios. Extensive experiments on PASCAL VOC and MS-COCO demonstrate that the proposed method outperforms baselines by a large margin and achieves new state-of-the-art results under different shot settings. | https://openaccess.thecvf.com/content/CVPR2024/papers/Wang_SNIDA_Unlocking_Few-Shot_Object_Detection_with_Non-linear_Semantic_Decoupling_Augmentation_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Wang_SNIDA_Unlocking_Few-Shot_Object_Detection_with_Non-linear_Semantic_Decoupling_Augmentation_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Wang_SNIDA_Unlocking_Few-Shot_Object_Detection_with_Non-linear_Semantic_Decoupling_Augmentation_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Wang_SNIDA_Unlocking_Few-Shot_CVPR_2024_supplemental.pdf | null |
On the Robustness of Large Multimodal Models Against Image Adversarial Attacks | Xuanming Cui, Alejandro Aparcedo, Young Kyun Jang, Ser-Nam Lim | Recent advances in instruction tuning have led to the development of State-of-the-Art Large Multimodal Models (LMMs). Given the novelty of these models the impact of visual adversarial attacks on LMMs has not been thoroughly examined. We conduct a comprehensive study of the robustness of various LMMs against different adversarial attacks evaluated across tasks including image classification image captioning and Visual Question Answer (VQA). We find that in general LMMs are not robust to visual adversarial inputs. However our findings suggest that context provided to the model via prompts--such as questions in a QA pair--helps to mitigate the effects of visual adversarial inputs. Notably the LMMs evaluated demonstrated remarkable resilience to such attacks on the ScienceQA task with only an 8.10% drop in performance compared to their visual counterparts which dropped 99.73%. We also propose a new approach to real-world image classification which we term query decomposition. By incorporating existence queries into our input prompt we observe diminished attack effectiveness and improvements in image classification accuracy. This research highlights a previously under explored facet of LMM robustness and sets the stage for future work aimed at strengthening the resilience of multimodal systems in adversarial environments. | https://openaccess.thecvf.com/content/CVPR2024/papers/Cui_On_the_Robustness_of_Large_Multimodal_Models_Against_Image_Adversarial_CVPR_2024_paper.pdf | http://arxiv.org/abs/2312.03777 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Cui_On_the_Robustness_of_Large_Multimodal_Models_Against_Image_Adversarial_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Cui_On_the_Robustness_of_Large_Multimodal_Models_Against_Image_Adversarial_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Cui_On_the_Robustness_CVPR_2024_supplemental.pdf | null |
SoundingActions: Learning How Actions Sound from Narrated Egocentric Videos | Changan Chen, Kumar Ashutosh, Rohit Girdhar, David Harwath, Kristen Grauman | We propose a novel self-supervised embedding to learn how actions sound from narrated in-the-wild egocentric videos. Whereas existing methods rely on curated data with known audio-visual correspondence our multimodal contrastive-consensus coding (MC3) embedding reinforces the associations between audio language and vision when all modality pairs agree while diminishing those associations when any one pair does not. We show our approach can successfully discover how the long tail of human actions sound from egocentric video outperforming an array of recent multimodal embedding techniques on two datasets (Ego4D and EPIC-Sounds) and multiple cross-modal tasks. | https://openaccess.thecvf.com/content/CVPR2024/papers/Chen_SoundingActions_Learning_How_Actions_Sound_from_Narrated_Egocentric_Videos_CVPR_2024_paper.pdf | http://arxiv.org/abs/2404.05206 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Chen_SoundingActions_Learning_How_Actions_Sound_from_Narrated_Egocentric_Videos_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Chen_SoundingActions_Learning_How_Actions_Sound_from_Narrated_Egocentric_Videos_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Chen_SoundingActions_Learning_How_CVPR_2024_supplemental.zip | null |
Not All Voxels Are Equal: Hardness-Aware Semantic Scene Completion with Self-Distillation | Song Wang, Jiawei Yu, Wentong Li, Wenyu Liu, Xiaolu Liu, Junbo Chen, Jianke Zhu | Semantic scene completion also known as semantic occupancy prediction can provide dense geometric and semantic information for autonomous vehicles which attracts the increasing attention of both academia and industry. Unfortunately existing methods usually formulate this task as a voxel-wise classification problem and treat each voxel equally in 3D space during training. As the hard voxels have not been paid enough attention the performance in some challenging regions is limited. The 3D dense space typically contains a large number of empty voxels which are easy to learn but require amounts of computation due to handling all the voxels uniformly for the existing models. Furthermore the voxels in the boundary region are more challenging to differentiate than those in the interior. In this paper we propose HASSC approach to train the semantic scene completion model with hardness-aware design. The global hardness from the network optimization process is defined for dynamical hard voxel selection. Then the local hardness with geometric anisotropy is adopted for voxel-wise refinement. Besides self-distillation strategy is introduced to make training process stable and consistent. Extensive experiments show that our HASSC scheme can effectively promote the accuracy of the baseline model without incurring the extra inference cost. Source code is available at: https://github.com/songw-zju/HASSC. | https://openaccess.thecvf.com/content/CVPR2024/papers/Wang_Not_All_Voxels_Are_Equal_Hardness-Aware_Semantic_Scene_Completion_with_CVPR_2024_paper.pdf | http://arxiv.org/abs/2404.11958 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Wang_Not_All_Voxels_Are_Equal_Hardness-Aware_Semantic_Scene_Completion_with_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Wang_Not_All_Voxels_Are_Equal_Hardness-Aware_Semantic_Scene_Completion_with_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Wang_Not_All_Voxels_CVPR_2024_supplemental.pdf | null |
3D-LFM: Lifting Foundation Model | Mosam Dabhi, László A. Jeni, Simon Lucey | The lifting of a 3D structure and camera from 2D landmarks is at the cornerstone of the discipline of computer vision. Traditional methods have been confined to specific rigid objects such as those in Perspective-n-Point (PnP) problems but deep learning has expanded our capability to reconstruct a wide range of object classes (e.g. C3DPO [??] and PAUL [??]) with resilience to noise occlusions and perspective distortions. However all these techniques have been limited by the fundamental need to establish correspondences across the 3D training data significantly limiting their utility to applications where one has an abundance of "in-correspondence" 3D data. Our approach harnesses the inherent permutation equivariance of transformers to manage varying numbers of points per 3D data instance withstands occlusions and generalizes to unseen categories. We demonstrate state-of-the-art performance across 2D-3D lifting task benchmarks. Since our approach can be trained across such a broad class of structures we refer to it simply as a 3D Lifting Foundation Model (3D-LFM) -- the first of its kind. | https://openaccess.thecvf.com/content/CVPR2024/papers/Dabhi_3D-LFM_Lifting_Foundation_Model_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Dabhi_3D-LFM_Lifting_Foundation_Model_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Dabhi_3D-LFM_Lifting_Foundation_Model_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Dabhi_3D-LFM_Lifting_Foundation_CVPR_2024_supplemental.zip | null |
VP3D: Unleashing 2D Visual Prompt for Text-to-3D Generation | Yang Chen, Yingwei Pan, Haibo Yang, Ting Yao, Tao Mei | Recent innovations on text-to-3D generation have featured Score Distillation Sampling (SDS) which enables the zero-shot learning of implicit 3D models (NeRF) by directly distilling prior knowledge from 2D diffusion models. However current SDS-based models still struggle with intricate text prompts and commonly result in distorted 3D models with unrealistic textures or cross-view inconsistency issues. In this work we introduce a novel Visual Prompt-guided text-to-3D diffusion model (VP3D) that explicitly unleashes the visual appearance knowledge in 2D visual prompt to boost text-to-3D generation. Instead of solely supervising SDS with text prompt VP3D first capitalizes on 2D diffusion model to generate a high-quality image from input text which subsequently acts as visual prompt to strengthen SDS optimization with explicit visual appearance. Meanwhile we couple the SDS optimization with additional differentiable reward function that encourages rendering images of 3D models to better visually align with 2D visual prompt and semantically match with text prompt. Through extensive experiments we show that the 2D Visual Prompt in our VP3D significantly eases the learning of visual appearance of 3D models and thus leads to higher visual fidelity with more detailed textures. It is also appealing in view that when replacing the self-generating visual prompt with a given reference image VP3D is able to trigger a new task of stylized text-to-3D generation. Our project page is available at https://vp3d-cvpr24.github.io. | https://openaccess.thecvf.com/content/CVPR2024/papers/Chen_VP3D_Unleashing_2D_Visual_Prompt_for_Text-to-3D_Generation_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.17001 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Chen_VP3D_Unleashing_2D_Visual_Prompt_for_Text-to-3D_Generation_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Chen_VP3D_Unleashing_2D_Visual_Prompt_for_Text-to-3D_Generation_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Chen_VP3D_Unleashing_2D_CVPR_2024_supplemental.pdf | null |
MonoHair: High-Fidelity Hair Modeling from a Monocular Video | Keyu Wu, Lingchen Yang, Zhiyi Kuang, Yao Feng, Xutao Han, Yuefan Shen, Hongbo Fu, Kun Zhou, Youyi Zheng | Undoubtedly high-fidelity 3D hair is crucial for achieving realism artistic expression and immersion in computer graphics. While existing 3D hair modeling methods have achieved impressive performance the challenge of achieving high-quality hair reconstruction persists: they either require strict capture conditions making practical applications difficult or heavily rely on learned prior data obscuring fine-grained details in images. To address these challenges we propose MonoHair a generic framework to achieve high-fidelity hair reconstruction from a monocular video without specific requirements for environments. Our approach bifurcates the hair modeling process into two main stages: precise exterior reconstruction and interior structure inference. The exterior is meticulously crafted using our Patch-based Multi-View Optimization PMVO. This method strategically collects and integrates hair information from multiple views independent of prior data to produce a high-fidelity exterior 3D line map. This map not only captures intricate details but also facilitates the inference of the hair's inner structure. For the interior we employ a data-driven multi-view 3D hair reconstruction method. This method utilizes 2D structural renderings derived from the reconstructed exterior mirroring the synthetic 2D inputs used during training. This alignment effectively bridges the domain gap between our training data and real-world data thereby enhancing the accuracy and reliability of our interior structure inference. Lastly we generate a strand model and resolve the directional ambiguity by our hair growth algorithm. Our experiments demonstrate that our method exhibits robustness across diverse hairstyles and achieves state-of-the-art performance. For more results please refer to our project page https://keyuwu-cs.github.io/MonoHair/ | https://openaccess.thecvf.com/content/CVPR2024/papers/Wu_MonoHair_High-Fidelity_Hair_Modeling_from_a_Monocular_Video_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.18356 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Wu_MonoHair_High-Fidelity_Hair_Modeling_from_a_Monocular_Video_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Wu_MonoHair_High-Fidelity_Hair_Modeling_from_a_Monocular_Video_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Wu_MonoHair_High-Fidelity_Hair_CVPR_2024_supplemental.pdf | null |
Content-Style Decoupling for Unsupervised Makeup Transfer without Generating Pseudo Ground Truth | Zhaoyang Sun, Shengwu Xiong, Yaxiong Chen, Yi Rong | The absence of real targets to guide the model training is one of the main problems with the makeup transfer task. Most existing methods tackle this problem by synthesizing pseudo ground truths (PGTs). However the generated PGTs are often sub-optimal and their imprecision will eventually lead to performance degradation. To alleviate this issue in this paper we propose a novel Content-Style Decoupled Makeup Transfer (CSD-MT) method which works in a purely unsupervised manner and thus eliminates the negative effects of generating PGTs. Specifically based on the frequency characteristics analysis we assume that the low-frequency (LF) component of a face image is more associated with its makeup style information while the high-frequency (HF) component is more related to its content details. This assumption allows CSD-MT to decouple the content and makeup style information in each face image through the frequency decomposition. After that CSD-MT realizes makeup transfer by maximizing the consistency of these two types of information between the transferred result and input images respectively. Two newly designed loss functions are also introduced to further improve the transfer performance. Extensive quantitative and qualitative analyses show the effectiveness of our CSD-MT method. Our code is available at https://github.com/Snowfallingplum/CSD-MT. | https://openaccess.thecvf.com/content/CVPR2024/papers/Sun_Content-Style_Decoupling_for_Unsupervised_Makeup_Transfer_without_Generating_Pseudo_Ground_CVPR_2024_paper.pdf | http://arxiv.org/abs/2405.17240 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Sun_Content-Style_Decoupling_for_Unsupervised_Makeup_Transfer_without_Generating_Pseudo_Ground_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Sun_Content-Style_Decoupling_for_Unsupervised_Makeup_Transfer_without_Generating_Pseudo_Ground_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Sun_Content-Style_Decoupling_for_CVPR_2024_supplemental.pdf | null |
One Prompt Word is Enough to Boost Adversarial Robustness for Pre-trained Vision-Language Models | Lin Li, Haoyan Guan, Jianing Qiu, Michael Spratling | Large pre-trained Vision-Language Models (VLMs) like CLIP despite having remarkable generalization ability are highly vulnerable to adversarial examples. This work studies the adversarial robustness of VLMs from the novel perspective of the text prompt instead of the extensively studied model weights (frozen in this work). We first show that the effectiveness of both adversarial attack and defense are sensitive to the used text prompt. Inspired by this we propose a method to improve resilience to adversarial attacks by learning a robust text prompt for VLMs. The proposed method named Adversarial Prompt Tuning (APT) is effective while being both computationally and data efficient. Extensive experiments are conducted across 15 datasets and 4 data sparsity schemes (from 1-shot to full training data settings) to show APT's superiority over hand-engineered prompts and other state-of-the-art adaption methods. APT demonstrated excellent abilities in terms of the in-distribution performance and the generalization under input distribution shift and across datasets. Surprisingly by simply adding one learned word to the prompts APT can significantly boost the accuracy and robustness (epsilon=4/255) over the hand-engineered prompts by +13% and +8.5% on average respectively. The improvement further increases in our most effective setting to +26.4% for accuracy and +16.7% for robustness. Code is available at https://github.com/TreeLLi/APT. | https://openaccess.thecvf.com/content/CVPR2024/papers/Li_One_Prompt_Word_is_Enough_to_Boost_Adversarial_Robustness_for_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.01849 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Li_One_Prompt_Word_is_Enough_to_Boost_Adversarial_Robustness_for_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Li_One_Prompt_Word_is_Enough_to_Boost_Adversarial_Robustness_for_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Li_One_Prompt_Word_CVPR_2024_supplemental.pdf | null |
A Versatile Framework for Continual Test-Time Domain Adaptation: Balancing Discriminability and Generalizability | Xu Yang, Xuan Chen, Moqi Li, Kun Wei, Cheng Deng | Continual test-time domain adaptation (CTTA) aims to adapt the source pre-trained model to a continually changing target domain without additional data acquisition or labeling costs. This issue necessitates an initial performance enhancement within the present domain without labels while concurrently averting an excessive bias toward the current domain. Such bias exacerbates catastrophic forgetting and diminishes the generalization ability to future domains. To tackle the problem this paper designs a versatile framework to capture high-quality supervision signals from three aspects: 1) The adaptive thresholds are employed to determine the reliability of pseudo-labels; 2) The knowledge from the source pre-trained model is utilized to adjust the unreliable one and 3) By evaluating past supervision signals we calculate a diversity score to ensure subsequent generalization. In this way we form a complete supervisory signal generation framework which can capture the current domain discriminative and reserve generalization in future domains. Finally to avoid catastrophic forgetting we design a weighted soft parameter alignment method to explore the knowledge from the source model. Extensive experimental results demonstrate that our method performs well on several benchmark datasets. | https://openaccess.thecvf.com/content/CVPR2024/papers/Yang_A_Versatile_Framework_for_Continual_Test-Time_Domain_Adaptation_Balancing_Discriminability_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Yang_A_Versatile_Framework_for_Continual_Test-Time_Domain_Adaptation_Balancing_Discriminability_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Yang_A_Versatile_Framework_for_Continual_Test-Time_Domain_Adaptation_Balancing_Discriminability_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Yang_A_Versatile_Framework_CVPR_2024_supplemental.pdf | null |
Quantifying Uncertainty in Motion Prediction with Variational Bayesian Mixture | Juanwu Lu, Can Cui, Yunsheng Ma, Aniket Bera, Ziran Wang | Safety and robustness are crucial factors in developing trustworthy autonomous vehicles. One essential aspect of addressing these factors is to equip vehicles with the capability to predict future trajectories for all moving objects in the surroundings and quantify prediction uncertainties. In this paper we propose the Sequential Neural Variational Agent (SeNeVA) a generative model that describes the distribution of future trajectories for a single moving object. Our approach can distinguish Out-of-Distribution data while quantifying uncertainty and achieving competitive performance compared to state-of-the-art methods on the Argoverse 2 and INTERACTION datasets. Specifically a 0.446 meters minimum Final Displacement Error a 0.203 meters minimum Average Displacement Error and a 5.35% Miss Rate are achieved on the INTERACTION test set. Extensive qualitative and quantitative analysis is also provided to evaluate the proposed model. Our open-source code is available at https://github.com/PurdueDigitalTwin/seneva. | https://openaccess.thecvf.com/content/CVPR2024/papers/Lu_Quantifying_Uncertainty_in_Motion_Prediction_with_Variational_Bayesian_Mixture_CVPR_2024_paper.pdf | http://arxiv.org/abs/2404.03789 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Lu_Quantifying_Uncertainty_in_Motion_Prediction_with_Variational_Bayesian_Mixture_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Lu_Quantifying_Uncertainty_in_Motion_Prediction_with_Variational_Bayesian_Mixture_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Lu_Quantifying_Uncertainty_in_CVPR_2024_supplemental.pdf | null |
You Only Need Less Attention at Each Stage in Vision Transformers | Shuoxi Zhang, Hanpeng Liu, Stephen Lin, Kun He | The advent of Vision Transformers (ViTs) marks a substantial paradigm shift in the realm of computer vision. ViTs capture the global information of images through self-attention modules which perform dot product computations among patchified image tokens. While self-attention modules empower ViTs to capture long-range dependencies the computational complexity grows quadratically with the number of tokens which is a major hindrance to the practical application of ViTs. Moreover the self-attention mechanism in deep ViTs is also susceptible to the attention saturation issue. Accordingly we argue against the necessity of computing the attention scores in every layer and we propose the Less-Attention Vision Transformer (LaViT) which computes only a few attention operations at each stage and calculates the subsequent feature alignments in other layers via attention transformations that leverage the previously calculated attention scores. This novel approach can mitigate two primary issues plaguing traditional self-attention modules: the heavy computational burden and attention saturation. Our proposed architecture offers superior efficiency and ease of implementation merely requiring matrix multiplications that are highly optimized in contemporary deep learning frameworks. Moreover our architecture demonstrates exceptional performance across various vision tasks including classification detection and segmentation. | https://openaccess.thecvf.com/content/CVPR2024/papers/Zhang_You_Only_Need_Less_Attention_at_Each_Stage_in_Vision_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Zhang_You_Only_Need_Less_Attention_at_Each_Stage_in_Vision_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Zhang_You_Only_Need_Less_Attention_at_Each_Stage_in_Vision_CVPR_2024_paper.html | CVPR 2024 | null | null |
Sieve: Multimodal Dataset Pruning using Image Captioning Models | Anas Mahmoud, Mostafa Elhoushi, Amro Abbas, Yu Yang, Newsha Ardalani, Hugh Leather, Ari S. Morcos | Vision-Language Models (VLMs) are pretrained on large diverse and noisy web-crawled datasets. This underscores the critical need for dataset pruning as the quality of these datasets is strongly correlated with the performance of VLMs on downstream tasks. Using CLIPScore from a pretrained model to only train models using highly-aligned samples is one of the most successful methods for pruning. We argue that this approach suffers from multiple limitations including: false positives and negatives due to CLIP's pretraining on noisy labels. We propose a pruning signal Sieve that employs synthetic captions generated by image-captioning models pretrained on small diverse and well-aligned image-text pairs to evaluate the alignment of noisy image-text pairs. To bridge the gap between the limited diversity of generated captions and the high diversity of alternative text (alt-text) we estimate the semantic textual similarity in the embedding space of a language model pretrained on unlabeled text corpus. Using DataComp a multimodal dataset filtering benchmark when evaluating on 38 downstream tasks our pruning approach surpasses CLIPScore by 2.6% and 1.7% on medium and large scale respectively. In addition on retrieval tasks Sieve leads to a significant improvement of 2.7% and 4.5% on medium and large scale respectively. | https://openaccess.thecvf.com/content/CVPR2024/papers/Mahmoud_Sieve_Multimodal_Dataset_Pruning_using_Image_Captioning_Models_CVPR_2024_paper.pdf | http://arxiv.org/abs/2310.02110 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Mahmoud_Sieve_Multimodal_Dataset_Pruning_using_Image_Captioning_Models_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Mahmoud_Sieve_Multimodal_Dataset_Pruning_using_Image_Captioning_Models_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Mahmoud_Sieve_Multimodal_Dataset_CVPR_2024_supplemental.pdf | null |
Generalizable Novel-View Synthesis using a Stereo Camera | Haechan Lee, Wonjoon Jin, Seung-Hwan Baek, Sunghyun Cho | In this paper we propose the first generalizable view synthesis approach that specifically targets multi-view stereo-camera images. Since recent stereo matching has demonstrated accurate geometry prediction we introduce stereo matching into novel-view synthesis for high-quality geometry reconstruction. To this end this paper proposes a novel framework dubbed StereoNeRF which integrates stereo matching into a NeRF-based generalizable view synthesis approach. StereoNeRF is equipped with three key components to effectively exploit stereo matching in novel-view synthesis: a stereo feature extractor a depth-guided plane-sweeping and a stereo depth loss. Moreover we propose the StereoNVS dataset the first multi-view dataset of stereo-camera images encompassing a wide variety of both real and synthetic scenes. Our experimental results demonstrate that StereoNeRF surpasses previous approaches in generalizable view synthesis. | https://openaccess.thecvf.com/content/CVPR2024/papers/Lee_Generalizable_Novel-View_Synthesis_using_a_Stereo_Camera_CVPR_2024_paper.pdf | http://arxiv.org/abs/2404.13541 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Lee_Generalizable_Novel-View_Synthesis_using_a_Stereo_Camera_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Lee_Generalizable_Novel-View_Synthesis_using_a_Stereo_Camera_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Lee_Generalizable_Novel-View_Synthesis_CVPR_2024_supplemental.pdf | null |
Dynamic LiDAR Re-simulation using Compositional Neural Fields | Hanfeng Wu, Xingxing Zuo, Stefan Leutenegger, Or Litany, Konrad Schindler, Shengyu Huang | We introduce DyNFL a novel neural field-based approach for high-fidelity re-simulation of LiDAR scans in dynamic driving scenes. DyNFL processes LiDAR measurements from dynamic environments accompanied by bounding boxes of moving objects to construct an editable neural field. This field comprising separately reconstructed static background and dynamic objects allows users to modify viewpoints adjust object positions and seamlessly add or remove objects in the re-simulated scene. A key innovation of our method is the neural field composition technique which effectively integrates reconstructed neural assets from various scenes through a ray drop test accounting for occlusions and transparent surfaces. Our evaluation with both synthetic and real-world environments demonstrates that DyNFL substantially improves dynamic scene LiDAR simulation offering a combination of physical fidelity and flexible editing capabilities. Project page: https://shengyuh.github.io/dynfl | https://openaccess.thecvf.com/content/CVPR2024/papers/Wu_Dynamic_LiDAR_Re-simulation_using_Compositional_Neural_Fields_CVPR_2024_paper.pdf | http://arxiv.org/abs/2312.05247 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Wu_Dynamic_LiDAR_Re-simulation_using_Compositional_Neural_Fields_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Wu_Dynamic_LiDAR_Re-simulation_using_Compositional_Neural_Fields_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Wu_Dynamic_LiDAR_Re-simulation_CVPR_2024_supplemental.zip | null |
Explaining CLIP's Performance Disparities on Data from Blind/Low Vision Users | Daniela Massiceti, Camilla Longden, Agnieszka Slowik, Samuel Wills, Martin Grayson, Cecily Morrison | Large multi-modal models (LMMs) hold the potential to usher in a new era of automated visual assistance for people who are blind or low vision (BLV). Yet these models have not been systematically evaluated on data captured by BLV users. We address this by empirically assessing CLIP a widely-used LMM likely to underpin many assistive technologies. Testing 25 CLIP variants in a zero-shot classification task we find that their accuracy is 15 percentage points lower on average for images captured by BLV users than web-crawled images. This disparity stems from CLIP's sensitivities to 1) image content (e.g. not recognizing disability objects as well as other objects); 2) image quality (e.g. not being robust to lighting variation); and 3) text content (e.g. not recognizing objects described by tactile adjectives as well as visual ones). We delve deeper with a textual analysis of three common pre-training datasets: LAION-400M LAION-2B and DataComp-1B showing that disability content is rarely mentioned. We then provide three examples that illustrate how the performance disparities extend to three downstream models underpinned by CLIP: OWL-ViT CLIPSeg and DALL-E2. We find that few-shot learning with as few as 5 images can mitigate CLIP's quality-of-service disparities for BLV users in some scenarios which we discuss alongside a set of other possible mitigations. | https://openaccess.thecvf.com/content/CVPR2024/papers/Massiceti_Explaining_CLIPs_Performance_Disparities_on_Data_from_BlindLow_Vision_Users_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Massiceti_Explaining_CLIPs_Performance_Disparities_on_Data_from_BlindLow_Vision_Users_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Massiceti_Explaining_CLIPs_Performance_Disparities_on_Data_from_BlindLow_Vision_Users_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Massiceti_Explaining_CLIPs_Performance_CVPR_2024_supplemental.pdf | null |
AETTA: Label-Free Accuracy Estimation for Test-Time Adaptation | Taeckyung Lee, Sorn Chottananurak, Taesik Gong, Sung-Ju Lee | Test-time adaptation (TTA) has emerged as a viable solution to adapt pre-trained models to domain shifts using unlabeled test data. However TTA faces challenges of adaptation failures due to its reliance on blind adaptation to unknown test samples in dynamic scenarios. Traditional methods for out-of-distribution performance estimation are limited by unrealistic assumptions in the TTA context such as requiring labeled data or re-training models. To address this issue we propose AETTA a label-free accuracy estimation algorithm for TTA. We propose the prediction disagreement as the accuracy estimate calculated by comparing the target model prediction with dropout inferences. We then improve the prediction disagreement to extend the applicability of AETTA under adaptation failures. Our extensive evaluation with four baselines and six TTA methods demonstrates that AETTA shows an average of 19.8%p more accurate estimation compared with the baselines. We further demonstrate the effectiveness of accuracy estimation with a model recovery case study showcasing the practicality of our model recovery based on accuracy estimation. The source code is available at https://github.com/taeckyung/AETTA. | https://openaccess.thecvf.com/content/CVPR2024/papers/Lee_AETTA_Label-Free_Accuracy_Estimation_for_Test-Time_Adaptation_CVPR_2024_paper.pdf | http://arxiv.org/abs/2404.01351 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Lee_AETTA_Label-Free_Accuracy_Estimation_for_Test-Time_Adaptation_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Lee_AETTA_Label-Free_Accuracy_Estimation_for_Test-Time_Adaptation_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Lee_AETTA_Label-Free_Accuracy_CVPR_2024_supplemental.pdf | null |
Digital Life Project: Autonomous 3D Characters with Social Intelligence | Zhongang Cai, Jianping Jiang, Zhongfei Qing, Xinying Guo, Mingyuan Zhang, Zhengyu Lin, Haiyi Mei, Chen Wei, Ruisi Wang, Wanqi Yin, Liang Pan, Xiangyu Fan, Han Du, Peng Gao, Zhitao Yang, Yang Gao, Jiaqi Li, Tianxiang Ren, Yukun Wei, Xiaogang Wang, Chen Change Loy, Lei Yang, Ziwei Liu | In this work we present Digital Life Project a framework utilizing language as the universal medium to build autonomous 3D characters who are capable of engaging in social interactions and expressing with articulated body motions thereby simulating life in a digital environment. Our framework comprises two primary components: 1) SocioMind: a meticulously crafted digital brain that models personalities with systematic few-shot exemplars incorporates a reflection process based on psychology principles and emulates autonomy by initiating dialogue topics; 2) MoMat-MoGen: a text-driven motion synthesis paradigm for controlling the character's digital body. It integrates motion matching a proven industry technique to ensure motion quality with cutting-edge advancements in motion generation for diversity. Extensive experiments demonstrate that each module achieves state-of-the-art performance in its respective domain. Collectively they enable virtual characters to initiate and sustain dialogues autonomously while evolving their socio-psychological states. Concurrently these characters can perform contextually relevant bodily movements. Additionally an extension of DLP enables a virtual character to recognize and appropriately respond to human players' actions. | https://openaccess.thecvf.com/content/CVPR2024/papers/Cai_Digital_Life_Project_Autonomous_3D_Characters_with_Social_Intelligence_CVPR_2024_paper.pdf | http://arxiv.org/abs/2312.04547 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Cai_Digital_Life_Project_Autonomous_3D_Characters_with_Social_Intelligence_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Cai_Digital_Life_Project_Autonomous_3D_Characters_with_Social_Intelligence_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Cai_Digital_Life_Project_CVPR_2024_supplemental.pdf | null |
An Empirical Study of the Generalization Ability of Lidar 3D Object Detectors to Unseen Domains | George Eskandar | 3D Object Detectors (3D-OD) are crucial for understanding the environment in many robotic tasks especially autonomous driving. Including 3D information via Lidar sensors improves accuracy greatly. However such detectors perform poorly on domains they were not trained on i.e. different locations sensors weather etc. limiting their reliability in safety-critical applications. There exist methods to adapt 3D-ODs to these domains; however these methods treat 3D-ODs as a black box neglecting underlying architectural decisions and source-domain training strategies. Instead we dive deep into the details of 3D-ODs focusing our efforts on fundamental factors that influence robustness prior to domain adaptation. We systematically investigate four design choices (and the interplay between them) often overlooked in 3D-OD robustness and domain adaptation: architecture voxel encoding data augmentations and anchor strategies. We assess their impact on the robustness of nine state-of-the-art 3D-ODs across six benchmarks encompassing three types of domain gaps - sensor type weather and location. Our main findings are: (1) transformer backbones with local point features are more robust than 3D CNNs (2) test-time anchor size adjustment is crucial for adaptation across geographical locations significantly boosting scores without retraining (3) source-domain augmentations allow the model to generalize to low-resolution sensors and (4) surprisingly robustness to bad weather is improved when training directly on more clean weather data than on training with bad weather data. We outline our main conclusions and findings to provide practical guidance on developing more robust 3D-ODs. | https://openaccess.thecvf.com/content/CVPR2024/papers/Eskandar_An_Empirical_Study_of_the_Generalization_Ability_of_Lidar_3D_CVPR_2024_paper.pdf | http://arxiv.org/abs/2402.17562 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Eskandar_An_Empirical_Study_of_the_Generalization_Ability_of_Lidar_3D_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Eskandar_An_Empirical_Study_of_the_Generalization_Ability_of_Lidar_3D_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Eskandar_An_Empirical_Study_CVPR_2024_supplemental.pdf | null |
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