GenAI4ELab/papercli-papers huggingface dataset:
EpiDiff: Enhancing Multi-View Synthesis via Localized Epipolar-Constrained Diffusion
Authors: Zehuan Huang; Hao Wen; Junting Dong; Yaohui Wang; Yangguang Li; Xinyuan Chen; Yan-Pei Cao; Ding Liang; Yu Qiao; Bo Dai; Lu Sheng
Venue: CVPR 2024 (main)

Abstract: 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/.
On the Faithfulness of Vision Transformer Explanations
Authors: Junyi Wu; Weitai Kang; Hao Tang; Yuan Hong; Yan Yan
Venue: CVPR 2024 (main)

Abstract: 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.
Pixel-level Semantic Correspondence through Layout-aware Representation Learning and Multi-scale Matching Integration
Authors: Yixuan Sun; Zhangyue Yin; Haibo Wang; Yan Wang; Xipeng Qiu; Weifeng Ge; Wenqiang Zhang
Venue: CVPR 2024 (main)

Abstract: 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.
Learning Spatial Features from Audio-Visual Correspondence in Egocentric Videos
Authors: Sagnik Majumder; Ziad Al-Halah; Kristen Grauman
Venue: CVPR 2024 (main)

Abstract: 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.
DreamAvatar: Text-and-Shape Guided 3D Human Avatar Generation via Diffusion Models
Authors: Yukang Cao; Yan-Pei Cao; Kai Han; Ying Shan; Kwan-Yee K. Wong
Venue: CVPR 2024 (main)

Abstract: 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.
Dynamic Graph Representation with Knowledge-aware Attention for Histopathology Whole Slide Image Analysis
Authors: Jiawen Li; Yuxuan Chen; Hongbo Chu; Qiehe Sun; Tian Guan; Anjia Han; Yonghong He
Venue: CVPR 2024 (main)

Abstract: 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.
Brain Decodes Deep Nets
Authors: Huzheng Yang; James Gee; Jianbo Shi
Venue: CVPR 2024 (main)

Abstract: 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.
Semantics Distortion and Style Matter: Towards Source-free UDA for Panoramic Segmentation
Authors: Xu Zheng; Pengyuan Zhou; Athanasios V. Vasilakos; Lin Wang
Venue: CVPR 2024 (main)

Abstract: 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.
Bidirectional Autoregessive Diffusion Model for Dance Generation
Authors: Canyu Zhang; Youbao Tang; Ning Zhang; Ruei-Sung Lin; Mei Han; Jing Xiao; Song Wang
Venue: CVPR 2024 (main)

Abstract: Dance serves as a powerful medium for expressing human emotions but the lifelike generation of dance is still a considerable challenge. Recently diffusion models have showcased remarkable generative abilities across various domains. They hold promise for human motion generation due to their adaptable many-to-many nature. Nonetheless current diffusion-based motion generation models often create entire motion sequences directly and unidirectionally lacking focus on the motion with local and bidirectional enhancement. When choreographing high-quality dance movements people need to take into account not only the musical context but also the nearby music-aligned dance motions. To authentically capture human behavior we propose a Bidirectional Autoregressive Diffusion Model (BADM) for music-to-dance generation where a bidirectional encoder is built to enforce that the generated dance is harmonious in both the forward and backward directions. To make the generated dance motion smoother a local information decoder is built for local motion enhancement. The proposed framework is able to generate new motions based on the input conditions and nearby motions which foresees individual motion slices iteratively and consolidates all predictions. To further refine the synchronicity between the generated dance and the beat the beat information is incorporated as an input to generate better music-aligned dance movements. Experimental results demonstrate that the proposed model achieves state-of-the-art performance compared to existing unidirectional approaches on the prominent benchmark for music-to-dance generation.
Align Before Adapt: Leveraging Entity-to-Region Alignments for Generalizable Video Action Recognition
Authors: Yifei Chen; Dapeng Chen; Ruijin Liu; Sai Zhou; Wenyuan Xue; Wei Peng
Venue: CVPR 2024 (main)

Abstract: 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.
GOV-NeSF: Generalizable Open-Vocabulary Neural Semantic Fields
Authors: Yunsong Wang; Hanlin Chen; Gim Hee Lee
Venue: CVPR 2024 (main)

Abstract: 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.
FRESCO: Spatial-Temporal Correspondence for Zero-Shot Video Translation
Authors: Shuai Yang; Yifan Zhou; Ziwei Liu; Chen Change Loy
Venue: CVPR 2024 (main)

Abstract: 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.
Dual-Scale Transformer for Large-Scale Single-Pixel Imaging
Authors: Gang Qu; Ping Wang; Xin Yuan
Venue: CVPR 2024 (main)

Abstract: 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.
Towards Robust 3D Object Detection with LiDAR and 4D Radar Fusion in Various Weather Conditions
Authors: Yujeong Chae; Hyeonseong Kim; Kuk-Jin Yoon
Venue: CVPR 2024 (main)

Abstract: 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.
Enhancing 3D Fidelity of Text-to-3D using Cross-View Correspondences
Authors: Seungwook Kim; Kejie Li; Xueqing Deng; Yichun Shi; Minsu Cho; Peng Wang
Venue: CVPR 2024 (main)

Abstract: 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.
Bezier Everywhere All at Once: Learning Drivable Lanes as Bezier Graphs
Authors: Hugh Blayney; Hanlin Tian; Hamish Scott; Nils Goldbeck; Chess Stetson; Panagiotis Angeloudis
Venue: CVPR 2024 (main)

Abstract: 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