mtybilly/Instruct2DS huggingface dataset:
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
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

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
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

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
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

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
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

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
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

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
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

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
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

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
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

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
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

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
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

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
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

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
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

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
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

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
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

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
SplattingAvatar: Realistic Real-Time Human Avatars with Mesh-Embedded Gaussian Splatting
Authors: Zhijing Shao, Zhaolong Wang, Zhuang Li, Duotun Wang, Xiangru Lin, Yu Zhang, Mingming Fan, Zeyu Wang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: 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.
MoSAR: Monocular Semi-Supervised Model for Avatar Reconstruction using Differentiable Shading
Authors: Abdallah Dib, Luiz Gustavo Hafemann, Emeline Got, Trevor Anderson, Amin Fadaeinejad, Rafael M. O. Cruz, Marc-André Carbonneau
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: 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.
Bridging Remote Sensors with Multisensor Geospatial Foundation Models
Authors: Boran Han, Shuai Zhang, Xingjian Shi, Markus Reichstein
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: 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
Can I Trust Your Answer? Visually Grounded Video Question Answering
Authors: Junbin Xiao, Angela Yao, Yicong Li, Tat-Seng Chua
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: 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.
RankED: Addressing Imbalance and Uncertainty in Edge Detection Using Ranking-based Losses
Authors: Bedrettin Cetinkaya, Sinan Kalkan, Emre Akbas
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: 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.
DiffHuman: Probabilistic Photorealistic 3D Reconstruction of Humans
Authors: Akash Sengupta, Thiemo Alldieck, Nikos Kolotouros, Enric Corona, Andrei Zanfir, Cristian Sminchisescu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: 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.
SeeSR: Towards Semantics-Aware Real-World Image Super-Resolution
Authors: Rongyuan Wu, Tao Yang, Lingchen Sun, Zhengqiang Zhang, Shuai Li, Lei Zhang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: 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
Permutation Equivariance of Transformers and Its Applications
Authors: Hengyuan Xu, Liyao Xiang, Hangyu Ye, Dixi Yao, Pengzhi Chu, Baochun Li
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: 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
Polos: Multimodal Metric Learning from Human Feedback for Image Captioning
Authors: Yuiga Wada, Kanta Kaneda, Daichi Saito, Komei Sugiura
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: 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.
Detours for Navigating Instructional Videos
Authors: Kumar Ashutosh, Zihui Xue, Tushar Nagarajan, Kristen Grauman
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: 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%.
Discontinuity-preserving Normal Integration with Auxiliary Edges
Authors: Hyomin Kim, Yucheol Jung, Seungyong Lee
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: 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.
DrivingGaussian: Composite Gaussian Splatting for Surrounding Dynamic Autonomous Driving Scenes
Authors: Xiaoyu Zhou, Zhiwei Lin, Xiaojun Shan, Yongtao Wang, Deqing Sun, Ming-Hsuan Yang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: 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.
Self-Supervised Multi-Object Tracking with Path Consistency
Authors: Zijia Lu, Bing Shuai, Yanbei Chen, Zhenlin Xu, Davide Modolo
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: 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.
Unsupervised Keypoints from Pretrained Diffusion Models
Authors: Eric Hedlin, Gopal Sharma, Shweta Mahajan, Xingzhe He, Hossam Isack, Abhishek Kar, Helge Rhodin, Andrea Tagliasacchi, Kwang Moo Yi
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: 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/.
Resolution Limit of Single-Photon LiDAR
Authors: Stanley H. Chan, Hashan K. Weerasooriya, Weijian Zhang, Pamela Abshire, Istvan Gyongy, Robert K. Henderson
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: 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.
Flatten Long-Range Loss Landscapes for Cross-Domain Few-Shot Learning
Authors: Yixiong Zou, Yicong Liu, Yiman Hu, Yuhua Li, Ruixuan Li
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: 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.
Improving Distant 3D Object Detection Using 2D Box Supervision
Authors: Zetong Yang, Zhiding Yu, Chris Choy, Renhao Wang, Anima Anandkumar, Jose M. Alvarez
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: 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.
HDQMF: Holographic Feature Decomposition Using Quantum Algorithms
Authors: Prathyush Prasanth Poduval, Zhuowen Zou, Mohsen Imani
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: 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.