mtybilly/Instruct2DS huggingface dataset:
Efficiently Assemble Normalization Layers and Regularization for Federated Domain Generalization
Authors: Khiem Le, Long Ho, Cuong Do, Danh Le-Phuoc, Kok-Seng Wong
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Domain shift is a formidable issue in Machine Learning that causes a model to suffer from performance degradation when tested on unseen domains. Federated Domain Generalization (FedDG) attempts to train a global model using collaborative clients in a privacy-preserving manner that can generalize well to unseen clients possibly with domain shift. However most existing FedDG methods either cause additional privacy risks of data leakage or induce significant costs in client communication and computation which are major concerns in the Federated Learning paradigm. To circumvent these challenges here we introduce a novel architectural method for FedDG namely gPerXAN which relies on a normalization scheme working with a guiding regularizer. In particular we carefully design Personalized eXplicitly Assembled Normalization to enforce client models selectively filtering domain-specific features that are biased towards local data while retaining discrimination of those features. Then we incorporate a simple yet effective regularizer to guide these models in directly capturing domain-invariant representations that the global model's classifier can leverage. Extensive experimental results on two benchmark datasets i.e. PACS and Office-Home and a real-world medical dataset Camelyon17 indicate that our proposed method outperforms other existing methods in addressing this particular problem.
Exploring Pose-Aware Human-Object Interaction via Hybrid Learning
Authors: Eastman Z Y Wu, Yali Li, Yuan Wang, Shengjin Wang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Human-Object Interaction (HOI) detection plays a crucial role in visual scene comprehension. In recent advancements two-stage detectors have taken a prominent position. However they are encumbered by two primary challenges. First the misalignment between feature representation and relation reasoning gives rise to a deficiency in discriminative features crucial for interaction detection. Second due to sparse annotation the second-stage interaction head generates numerous candidate <human object> pairs with only a small fraction receiving supervision. Towards these issues we propose a hybrid learning method based on pose-aware HOI feature refinement. Specifically we devise pose-aware feature refinement that encodes spatial features by considering human body pose characteristics. It can direct attention towards key regions ultimately offering a wealth of fine-grained features imperative for HOI detection. Further we introduce a hybrid learning method that combines HOI triplets with probabilistic soft labels supervision which is regenerated from decoupled verb-object pairs. This method explores the implicit connections between the interactions enhancing model generalization without requiring additional data. Our method establishes state-of-the-art performance on HICO-DET benchmark and excels notably in detecting rare HOIs.
Depth Information Assisted Collaborative Mutual Promotion Network for Single Image Dehazing
Authors: Yafei Zhang, Shen Zhou, Huafeng Li
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Recovering a clear image from a single hazy image is an open inverse problem. Although significant research progress has been made most existing methods ignore the effect that downstream tasks play in promoting upstream dehazing. From the perspective of the haze generation mechanism there is a potential relationship between the depth information of the scene and the hazy image. Based on this we propose a dual-task collaborative mutual promotion framework to achieve the dehazing of a single image. This framework integrates depth estimation and dehazing by a dual-task interaction mechanism and achieves mutual enhancement of their performance. To realize the joint optimization of the two tasks an alternative implementation mechanism with the difference perception is developed. On the one hand the difference perception between the depth maps of the dehazing result and the ideal image is proposed to promote the dehazing network to pay attention to the non-ideal areas of the dehazing. On the other hand by improving the depth estimation performance in the difficult-to-recover areas of the hazy image the dehazing network can explicitly use the depth information of the hazy image to assist the clear image recovery. To promote the depth estimation we propose to use the difference between the dehazed image and the ground truth to guide the depth estimation network to focus on the dehazed unideal areas. It allows dehazing and depth estimation to leverage their strengths in a mutually reinforcing manner. Experimental results show that the proposed method can achieve better performance than that of the state-of-the-art approaches. The source code is released at https://github.com/zhoushen1/DCMPNet.
Density-Adaptive Model Based on Motif Matrix for Multi-Agent Trajectory Prediction
Authors: Di Wen, Haoran Xu, Zhaocheng He, Zhe Wu, Guang Tan, Peixi Peng
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Multi-agent trajectory prediction is essential in autonomous driving risk avoidance and traffic flow control. However the heterogeneous traffic density on interactions which caused by physical laws social norms and so on is often overlooked in existing methods. When the density varies the number of agents involved in interactions and the corresponding interaction probability change dynamically. To tackle this issue we propose a new method called \underline D ensity-\underline A daptive Model based on \underline M otif \underline M atrix for Multi-Agent Trajectory Prediction (DAMM) to gain insights into multi-agent systems. Here we leverage the motif matrix to represent dynamic connectivity in a higher-order pattern and distill the interaction information from the perspectives of the spatial and the temporal dimensions. Specifically in spatial dimension we utilize multi-scale feature fusion to adaptively select the optimal range of neighbors participating in interactions for each time slot. In temporal dimension we extract the temporal interaction features and adapt a pyramidal pooling layer to generate the interaction probability for each agent. Experimental results demonstrate that our approach surpasses state-of-the-art methods on autonomous driving dataset.
Contrastive Learning for DeepFake Classification and Localization via Multi-Label Ranking
Authors: Cheng-Yao Hong, Yen-Chi Hsu, Tyng-Luh Liu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: We propose a unified approach to simultaneously addressing the conventional setting of binary deepfake classification and a more challenging scenario of uncovering what facial components have been forged as well as the exact order of the manipulations. To solve the former task we consider multiple instance learning (MIL) that takes each image as a bag and its patches as instances. A positive bag corresponds to a forged image that includes at least one manipulated patch (i.e. a pixel in the feature map). The formulation allows us to estimate the probability of an input image being a fake one and establish the corresponding contrastive MIL loss. On the other hand tackling the component-wise deepfake problem can be reduced to solving multi-label prediction but the requirement to recover the manipulation order further complicates the learning task into a multi-label ranking problem. We resolve this difficulty by designing a tailor-made loss term to enforce that the rank order of the predicted multi-label probabilities respects the ground-truth order of the sequential modifications of a deepfake image. Through extensive experiments and comparisons with other relevant techniques we provide extensive results and ablation studies to demonstrate that the proposed method is an overall more comprehensive solution to deepfake detection.
Unlocking the Potential of Pre-trained Vision Transformers for Few-Shot Semantic Segmentation through Relationship Descriptors
Authors: Ziqin Zhou, Hai-Ming Xu, Yangyang Shu, Lingqiao Liu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: The recent advent of pre-trained vision transformers has unveiled a promising property: their inherent capability to group semantically related visual concepts. In this paper we explore to harnesses this emergent feature to tackle few-shot semantic segmentation a task focused on classifying pixels in a test image with a few example data. A critical hurdle in this endeavor is preventing overfitting to the limited classes seen during training the few-shot segmentation model. As our main discovery we find that the concept of "relationship descriptors" initially conceived for enhancing the CLIP model for zero-shot semantic segmentation offers a potential solution. We adapt and refine this concept to craft a relationship descriptor construction tailored for few-shot semantic segmentation extending its application across multiple layers to enhance performance. Building upon this adaptation we proposed a few-shot semantic segmentation framework that is not only easy to implement and train but also effectively scales with the number of support examples and categories. Through rigorous experimentation across various datasets including PASCAL-5^ i  and COCO-20^ i  we demonstrate a clear advantage of our method in diverse few-shot semantic segmentation scenarios and a range of pre-trained vision transformer models. The findings clearly show that our method significantly outperforms current state-of-the-art techniques highlighting the effectiveness of harnessing the emerging capabilities of vision transformers for few-shot semantic segmentation. We release the code at https://github.com/ZiqinZhou66/FewSegwithRD.git.
CustomListener: Text-guided Responsive Interaction for User-friendly Listening Head Generation
Authors: Xi Liu, Ying Guo, Cheng Zhen, Tong Li, Yingying Ao, Pengfei Yan
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Listening head generation aims to synthesize a non-verbal responsive listener head by modeling the correlation between the speaker and the listener in dynamic conversion. The applications of listener agent generation in virtual interaction have promoted many works achieving diverse and fine-grained motion generation. However they can only manipulate motions through simple emotional labels but cannot freely control the listener's motions. Since listener agents should have human-like attributes (e.g. identity personality) which can be freely customized by users this limits their realism. In this paper we propose a user-friendly framework called CustomListener to realize the free-form text prior guided listener generation. To achieve speaker-listener coordination we design a Static to Dynamic Portrait module (SDP) which interacts with speaker information to transform static text into dynamic portrait token with completion rhythm and amplitude information. To achieve coherence between segments we design a Past Guided Generation module (PGG) to maintain the consistency of customized listener attributes through the motion prior and utilize a diffusion-based structure conditioned on the portrait token and the motion prior to realize the controllable generation. To train and evaluate our model we have constructed two text-annotated listening head datasets based on ViCo and RealTalk which provide text-video paired labels. Extensive experiments have verified the effectiveness of our model.
Projecting Trackable Thermal Patterns for Dynamic Computer Vision
Authors: Mark Sheinin, Aswin C. Sankaranarayanan, Srinivasa G. Narasimhan
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Adding artificial patterns to objects like QR codes can ease tasks such as object tracking robot navigation and conveying information (e.g. a label or a website link). However these patterns require a physical application and they alter the object's appearance. Conversely projected patterns can temporarily change the object's appearance aiding tasks like 3D scanning and retrieving object textures and shading. However projected patterns impede dynamic tasks like object tracking because they do not `stick' to the object's surface. Or do they? This paper introduces a novel approach combining the advantages of projected and persistent physical patterns. Our system projects heat patterns using a laser beam (similar in spirit to a LIDAR) which a thermal camera observes and tracks. Such thermal patterns enable tracking poorly-textured objects whose tracking is highly challenging with standard cameras while not affecting the object's appearance or physical properties. To avail these thermal patterns in existing vision frameworks we train a network to reverse heat diffusion's effects and remove inconsistent pattern points between different thermal frames. We prototyped and tested this approach on dynamic vision tasks like structure from motion optical flow and object tracking of everyday textureless objects.
SG-PGM: Partial Graph Matching Network with Semantic Geometric Fusion for 3D Scene Graph Alignment and Its Downstream Tasks
Authors: Yaxu Xie, Alain Pagani, Didier Stricker
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Scene graphs have been recently introduced into 3D spatial understanding as a comprehensive representation of the scene. The alignment between 3D scene graphs is the first step of many downstream tasks such as scene graph aided point cloud registration mosaicking overlap checking and robot navigation. In this work we treat 3D scene graph alignment as a partial graph-matching problem and propose to solve it with a graph neural network. We reuse the geometric features learned by a point cloud registration method and associate the clustered point-level geometric features with the node-level semantic feature via our designed feature fusion module. Partial matching is enabled by using a learnable method to select the top-k similar node pairs. Subsequent downstream tasks such as point cloud registration are achieved by running a pre-trained registration network within the matched regions. We further propose a point-matching rescoring method that uses the node-wise alignment of the 3D scene graph to reweight the matching candidates from a pre-trained point cloud registration method. It reduces the false point correspondences estimated especially in low-overlapping cases. Experiments show that our method improves the alignment accuracy by 10 20% in low-overlap and random transformation scenarios and outperforms the existing work in multiple downstream tasks. Our code and models are available here (https://github.com/dfki-av/sg-pgm.git).
Fun with Flags: Robust Principal Directions via Flag Manifolds
Authors: Nathan Mankovich, Gustau Camps-Valls, Tolga Birdal
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Principal component analysis (PCA) along with its extensions to manifolds and outlier contaminated data have been indispensable in computer vision and machine learning. In this work we present a unifying formalism for PCA and its variants and introduce a framework based on the flags of linear subspaces i.e. a hierarchy of nested linear subspaces of increasing dimension which not only allows for a common implementation but also yields novel variants not explored previously. We begin by generalizing traditional PCA methods that either maximize variance or minimize reconstruction error. We expand these interpretations to develop a wide array of new dimensionality reduction algorithms by accounting for outliers and the data manifold. To devise a common computational approach we recast robust and dual forms of PCA as optimization problems on flag manifolds. We then integrate tangent space approximations of principal geodesic analysis (tangent-PCA) into this flag-based framework creating novel robust and dual geodesic PCA variations. The remarkable flexibility offered by the `flagification' introduced here enables even more algorithmic variants identified by specific flag types. Last but not least we propose an effective convergent solver for these flag-formulations employing the Stiefel manifold. Our empirical results on both real-world and synthetic scenarios demonstrate the superiority of our novel algorithms especially in terms of robustness to outliers on manifolds.
Generating Non-Stationary Textures using Self-Rectification
Authors: Yang Zhou, Rongjun Xiao, Dani Lischinski, Daniel Cohen-Or, Hui Huang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: This paper addresses the challenge of example-based non-stationary texture synthesis. We introduce a novel two-step approach wherein users first modify a reference texture using standard image editing tools yielding an initial rough target for the synthesis. Subsequently our proposed method termed "self-rectification" automatically refines this target into a coherent seamless texture while faithfully preserving the distinct visual characteristics of the reference exemplar. Our method leverages a pre-trained diffusion network and uses self-attention mechanisms to gradually align the synthesized texture with the reference ensuring the retention of the structures in the provided target. Through experimental validation our approach exhibits exceptional proficiency in handling non-stationary textures demonstrating significant advancements in texture synthesis when compared to existing state-of-the-art techniques. Code is available at https://github.com/xiaorongjun000/Self-Rectification
SPU-PMD: Self-Supervised Point Cloud Upsampling via Progressive Mesh Deformation
Authors: Yanzhe Liu, Rong Chen, Yushi Li, Yixi Li, Xuehou Tan
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Despite the success of recent upsampling approaches generating high-resolution point sets with uniform distribution and meticulous structures is still challenging. Unlike existing methods that only take spatial information of the raw data into account we regard point cloud upsampling as generating dense point clouds from deformable topology. Motivated by this we present SPU-PMD a self-supervised topological mesh deformation network for 3D densification. As a cascaded framework our architecture is formulated by a series of coarse mesh interpolator and mesh deformers. At each stage the mesh interpolator first produces the initial dense point clouds via mesh interpolation which allows the model to perceive the primitive topology better. Meanwhile the deformer infers the morphing by estimating the movements of mesh nodes and reconstructs the descriptive topology structure. By associating mesh deformation with feature expansion this module progressively refines point clouds' surface uniformity and structural details. To demonstrate the effectiveness of the proposed method extensive quantitative and qualitative experiments are conducted on synthetic and real-scanned 3D data. Also we compare it with state-of-the-art techniques to further illustrate the superiority of our network. The project page is: https://github.com/lyz21/SPU-PMD
Advancing Saliency Ranking with Human Fixations: Dataset Models and Benchmarks
Authors: Bowen Deng, Siyang Song, Andrew P. French, Denis Schluppeck, Michael P. Pound
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Saliency ranking detection (SRD) has emerged as a challenging task in computer vision aiming not only to identify salient objects within images but also to rank them based on their degree of saliency. Existing SRD datasets have been created primarily using mouse-trajectory data which inadequately captures the intricacies of human visual perception. Addressing this gap this paper introduces the first large-scale SRD dataset SIFR constructed using genuine human fixation data thereby aligning more closely with real visual perceptual processes. To establish a baseline for this dataset we propose QAGNet a novel model that leverages salient instance query features from a transformer detector within a tri-tiered nested graph. Through extensive experiments we demonstrate that our approach outperforms existing state-of-the-art methods across two widely used SRD datasets and our newly proposed dataset. Code and dataset are available at https://github.com/EricDengbowen/QAGNet.
Snap Video: Scaled Spatiotemporal Transformers for Text-to-Video Synthesis
Authors: Willi Menapace, Aliaksandr Siarohin, Ivan Skorokhodov, Ekaterina Deyneka, Tsai-Shien Chen, Anil Kag, Yuwei Fang, Aleksei Stoliar, Elisa Ricci, Jian Ren, Sergey Tulyakov
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Contemporary models for generating images show remarkable quality and versatility. Swayed by these advantages the research community repurposes them to generate videos. Since video content is highly redundant we argue that naively bringing advances of image models to the video generation domain reduces motion fidelity visual quality and impairs scalability. In this work we build Snap Video a video-first model that systematically addresses these challenges. To do that we first extend the EDM framework to take into account spatially and temporally redundant pixels and naturally support video generation. Second we show that a U-Net--a workhorse behind image generation--scales poorly when generating videos requiring significant computational overhead. Hence we propose a new transformer-based architecture that trains 3.31 times faster than U-Nets (and is  4.5 faster at inference). This allows us to efficiently train a text-to-video model with billions of parameters for the first time reach state-of-the-art results on a number of benchmarks and generate videos with substantially higher quality temporal consistency and motion complexity. The user studies showed that our model was favored by a large margin over the most recent methods.
Unsupervised Deep Unrolling Networks for Phase Unwrapping
Authors: Zhile Chen, Yuhui Quan, Hui Ji
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Phase unwrapping (PU) is a technique to reconstruct original phase images from their noisy wrapped counterparts finding many applications in scientific imaging. Although supervised learning has shown promise in PU its utility is limited in ground-truth (GT) scarce scenarios. This paper presents an unsupervised learning approach that eliminates the need for GTs during end-to-end training. Our approach leverages the insight that both the gradients and wrapped gradients of wrapped phases serve as noisy labels for GT phase gradients along with sparse outliers induced by the wrapping operation. A recorruption-based self-reconstruction loss in the gradient domain is proposed to mitigate the adverse effects of label noise complemented with a self-distillation loss for improved generalization. Additionally by unfolding a variational model of PU that utilizes wrapped gradients of wrapped phases for its data-fitting term we develop a deep unrolling network that encodes physics of phase wrapping and incorporates special treatments on outliers. In the experiments on three types of phase data our approach outperforms existing GT-free methods and competes well against the supervised ones.
Federated Generalized Category Discovery
Authors: Nan Pu, Wenjing Li, Xingyuan Ji, Yalan Qin, Nicu Sebe, Zhun Zhong
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Generalized category discovery (GCD) aims at grouping unlabeled samples from known and unknown classes given labeled data of known classes. To meet the recent decentralization trend in the community we introduce a practical yet challenging task Federated GCD (Fed-GCD) where the training data are distributed in local clients and cannot be shared among clients. Fed-GCD aims to train a generic GCD model by client collaboration under the privacy-protected constraint. The Fed-GCD leads to two challenges: 1) representation degradation caused by training each client model with fewer data than centralized GCD learning and 2) highly heterogeneous label spaces across different clients. To this end we propose a novel Associated Gaussian Contrastive Learning (AGCL) framework based on learnable GMMs which consists of a Client Semantics Association (CSA) and a global-local GMM Contrastive Learning (GCL). On the server CSA aggregates the heterogeneous categories of local-client GMMs to generate a global GMM containing more comprehensive category knowledge. On each client GCL builds class-level contrastive learning with both local and global GMMs. The local GCL learns robust representation with limited local data. The global GCL encourages the model to produce more discriminative representation with the comprehensive category relationships that may not exist in local data. We build a benchmark based on six visual datasets to facilitate the study of Fed-GCD. Extensive experiments show that our AGCL outperforms multiple baselines on all datasets.
JointSQ: Joint Sparsification-Quantization for Distributed Learning
Authors: Weiying Xie, Haowei Li, Jitao Ma, Yunsong Li, Jie Lei, Donglai Liu, Leyuan Fang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Gradient sparsification and quantization offer a promising prospect to alleviate the communication overhead problem in distributed learning. However direct combination of the two results in suboptimal solutions due to the fact that sparsification and quantization haven't been learned together. In this paper we propose Joint Sparsification-Quantization (JointSQ) inspired by the discovery that sparsification can be treated as 0-bit quantization regardless of architectures. Specifically we mathematically formulate JointSQ as a mixed-precision quantization problem expanding the solution space. It can be solved by the designed MCKP-Greedy algorithm. Theoretical analysis demonstrates the minimal compression noise of JointSQ and extensive experiments on various network architectures including CNN RNN and Transformer also validate this point. Under the introduction of computation overhead consistent with or even lower than previous methods JointSQ achieves a compression ratio of 1000xon different models while maintaining near-lossless accuracy and brings 1.4xto 2.9xspeedup over existing methods.
A Unified Framework for Human-centric Point Cloud Video Understanding
Authors: Yiteng Xu, Kecheng Ye, Xiao Han, Yiming Ren, Xinge Zhu, Yuexin Ma
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Human-centric Point Cloud Video Understanding (PVU) is an emerging field focused on extracting and interpreting human-related features from sequences of human point clouds further advancing downstream human-centric tasks and applications. Previous works usually focus on tackling one specific task and rely on huge labeled data which has poor generalization capability. Considering that human has specific characteristics including the structural semantics of human body and the dynamics of human motions we propose a unified framework to make full use of the prior knowledge and explore the inherent features in the data itself for generalized human-centric point cloud video understanding. Extensive experiments demonstrate that our method achieves state-of-the-art performance on various human-related tasks including action recognition and 3D pose estimation. All datasets and code will be released soon.
Edge-Aware 3D Instance Segmentation Network with Intelligent Semantic Prior
Authors: Wonseok Roh, Hwanhee Jung, Giljoo Nam, Jinseop Yeom, Hyunje Park, Sang Ho Yoon, Sangpil Kim
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: While recent 3D instance segmentation approaches show promising results based on transformer architectures they often fail to correctly identify instances with similar appearances. They also ambiguously determine edges leading to multiple misclassifications of adjacent edge points. In this work we introduce a novel framework called EASE to overcome these challenges and improve the perception of complex 3D instances. We first propose a semantic guidance network to leverage rich semantic knowledge from a language model as intelligent priors enhancing the functional understanding of real-world instances beyond relying solely on geometrical information. We explicitly instruct the basic instance queries using text embeddings of each instance to learn deep semantic details. Further we utilize the edge prediction module encouraging the segmentation network to be edge-aware. We extract voxel-wise edge maps from point features and use them as auxiliary information for learning edge cues. In our extensive experiments on large-scale benchmarks ScanNetV2 ScanNet200 S3DIS and STPLS3D our EASE outperforms existing state-of-the-art models demonstrating its superior performance.
Coherence As Texture - Passive Textureless 3D Reconstruction by Self-interference
Authors: Wei-Yu Chen, Aswin C. Sankaranarayanan, Anat Levin, Matthew O'Toole
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Passive depth estimation based on stereo or defocus relies on the presence of the texture on an object to resolve its depth. Hence recovering the depth of a textureless object-- for example a large white wall--is not just hard but perhaps even impossible. Or is it? We show that spatial coherence a property of natural light sources can be used to resolve the depth of a scene point even when it is textureless. Our approach relies on the idea that natural light scattered off a scene point is locally coherent with itself while incoherent with the light scattered from other surface points; we use this insight to design an optical setup that uses self-interference as a texture feature for estimating depth. Our lab prototype is capable of resolving the depths of textureless objects in sunlight as well as indoor lights.
Enhancing the Power of OOD Detection via Sample-Aware Model Selection
Authors: Feng Xue, Zi He, Yuan Zhang, Chuanlong Xie, Zhenguo Li, Falong Tan
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: In this work we present a novel perspective on detecting out-of-distribution (OOD) samples and propose an algorithm for sample-aware model selection to enhance the effectiveness of OOD detection. Our algorithm determines for each test input which pre-trained models in the model zoo are capable of identifying the test input as an OOD sample. If no such models exist in the model zoo the test input is classified as an in-distribution (ID) sample. We theoretically demonstrate that our method maintains the true positive rate of ID samples and accurately identifies OOD samples with high probability when there are a sufficient number of diverse pre-trained models in the model zoo. Extensive experiments were conducted to validate our method demonstrating that it leverages the complementarity among single-model detectors to consistently improve the effectiveness of OOD sample identification. Compared to base-line methods our approach improved the relative performance by 65.40% and 37.25% on the CIFAR10 and ImageNet benchmarks respectively.
Collaborative Semantic Occupancy Prediction with Hybrid Feature Fusion in Connected Automated Vehicles
Authors: Rui Song, Chenwei Liang, Hu Cao, Zhiran Yan, Walter Zimmer, Markus Gross, Andreas Festag, Alois Knoll
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Collaborative perception in automated vehicles leverages the exchange of information between agents aiming to elevate perception results. Previous camera-based collaborative 3D perception methods typically employ 3D bounding boxes or bird's eye views as representations of the environment. However these approaches fall short in offering a comprehensive 3D environmental prediction. To bridge this gap we introduce the first method for collaborative 3D semantic occupancy prediction. Particularly it improves local 3D semantic occupancy predictions by hybrid fusion of (i) semantic and occupancy task features and (ii) compressed orthogonal attention features shared between vehicles. Additionally due to the lack of a collaborative perception dataset designed for semantic occupancy prediction we augment a current collaborative perception dataset to include 3D collaborative semantic occupancy labels for a more robust evaluation. The experimental findings highlight that: (i) our collaborative semantic occupancy predictions excel above the results from single vehicles by over 30% and (ii) models anchored on semantic occupancy outpace state-of-the-art collaborative 3D detection techniques in subsequent perception applications showcasing enhanced accuracy and enriched semantic-awareness in road environments.
Generative Multi-modal Models are Good Class Incremental Learners
Authors: Xusheng Cao, Haori Lu, Linlan Huang, Xialei Liu, Ming-Ming Cheng
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: In class incremental learning (CIL) scenarios the phenomenon of catastrophic forgetting caused by the classifier's bias towards the current task has long posed a significant challenge. It is mainly caused by the characteristic of discriminative models. With the growing popularity of the generative multi-modal models we would explore replacing discriminative models with generative ones for CIL. However transitioning from discriminative to generative models requires addressing two key challenges. The primary challenge lies in transferring the generated textual information into the classification of distinct categories. Additionally it requires formulating the task of CIL within a generative framework. To this end we propose a novel generative multi-modal model (GMM) framework for class incremental learning. Our approach directly generates labels for images using an adapted generative model. After obtaining the detailed text we use a text encoder to extract text features and employ feature matching to determine the most similar label as the classification prediction. In the conventional CIL settings we achieve significantly better results in long-sequence task scenarios. Under the Few-shot CIL setting we have improved by at least 14% over the current state-of-the-art methods with significantly less forgetting.
Low-Resource Vision Challenges for Foundation Models
Authors: Yunhua Zhang, Hazel Doughty, Cees G. M. Snoek
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Low-resource settings are well-established in natural lan- guage processing where many languages lack sufficient data for deep learning at scale. However low-resource problems are under-explored in computer vision. In this paper we address this gap and explore the challenges of low-resource image tasks with vision foundation models. We first collect a benchmark of genuinely low-resource image data covering historic maps circuit diagrams and mechanical drawings. These low-resource settings all share three challenges: data scarcity fine-grained differences and the distribution shift from natural images to the specialized domain of interest. While existing foundation models have shown impressive generalizability we find they cannot transfer well to our low-resource tasks. To begin to tackle the challenges of low-resource vision we introduce one simple baseline per challenge. Specifically we i) enlarge the data space by generative models ii) adopt the best sub-kernels to encode local regions for fine-grained difference discovery and iii) learn attention for specialized domains. Experiments on our three low-resource tasks demonstrate our proposals already provide a better baseline than transfer learning data aug- mentation and fine-grained methods. This highlights the unique characteristics and challenges of low-resource vision for foundation models that warrant further investigation. Project page: https://xiaobai1217.github.io/ Low-Resource-Vision/.
RGBD Objects in the Wild: Scaling Real-World 3D Object Learning from RGB-D Videos
Authors: Hongchi Xia, Yang Fu, Sifei Liu, Xiaolong Wang
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: We introduce a new RGB-D object dataset captured in the wild called WildRGB-D. Unlike most existing real-world object-centric datasets which only come with RGB capturing the direct capture of the depth channel allows better 3D annotations and broader downstream applications. WildRGB-D comprises large-scale category-level RGB-D object videos which are taken using an iPhone to go around the objects in 360 degrees. It contains around 8500 recorded objects and nearly 20000 RGB-D videos across 46 common object categories. These videos are taken with diverse cluttered backgrounds with three setups to cover as many real-world scenarios as possible: (i) a single object in one video; (ii) multiple objects in one video; and (iii) an object with a static hand in one video. The dataset is annotated with object masks real-world scale camera poses and reconstructed aggregated point clouds from RGBD videos. We benchmark four tasks with WildRGB-D including novel view synthesis camera pose estimation object 6d pose estimation and object surface reconstruction. Our experiments show that the large-scale capture of RGB-D objects provides a large potential to advance 3D object learning. Our project page is https://wildrgbd.github.io/.
Shadow-Enlightened Image Outpainting
Authors: Hang Yu, Ruilin Li, Shaorong Xie, Jiayan Qiu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Conventional image outpainting methods usually treat unobserved areas as unknown and extend the scene only in terms of semantic consistency thus overlooking the hidden information in shadows cast by unobserved areas such as the invisible shapes and semantics. In this paper we propose to extract and utilize the hidden information of unobserved areas from their shadows to enhance image outpainting. To this end we propose an end-to-end deep approach that explicitly looks into the shadows within the image. Specifically we extract shadows from the input image and identify instance-level shadow regions cast by the unobserved areas. Then the instance-level shadow representations are concatenated to predict the scene layout of each unobserved instance and outpaint the unobserved areas. Finally two discriminators are implemented to enhance alignment between the extended semantics and their shadows. In the experiments we show that our proposed approach provides complementary cues for outpainting and achieves considerable improvement on all datasets by adopting our approach as a plug-in module.
Towards Generalizable Tumor Synthesis
Authors: Qi Chen, Xiaoxi Chen, Haorui Song, Zhiwei Xiong, Alan Yuille, Chen Wei, Zongwei Zhou
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: Tumor synthesis enables the creation of artificial tumors in medical images facilitating the training of AI models for tumor detection and segmentation. However success in tumor synthesis hinges on creating visually realistic tumors that are generalizable across multiple organs and furthermore the resulting AI models being capable of detecting real tumors in images sourced from different domains (e.g. hospitals). This paper made a progressive stride toward generalizable tumor synthesis by leveraging a critical observation: early-stage tumors (< 2cm) tend to have similar imaging characteristics in computed tomography (CT) whether they originate in the liver pancreas or kidneys. We have ascertained that generative AI models e.g. Diffusion Models can create realistic tumors generalized to a range of organs even when trained on a limited number of tumor examples from only one organ. Moreover we have shown that AI models trained on these synthetic tumors can be generalized to detect and segment real tumors from CT volumes encompassing a broad spectrum of patient demographics imaging protocols and healthcare facilities.
Low-Res Leads the Way: Improving Generalization for Super-Resolution by Self-Supervised Learning
Authors: Haoyu Chen, Wenbo Li, Jinjin Gu, Jingjing Ren, Haoze Sun, Xueyi Zou, Zhensong Zhang, Youliang Yan, Lei Zhu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

Abstract: For image super-resolution (SR) bridging the gap between the performance on synthetic datasets and real-world degradation scenarios remains a challenge. This work introduces a novel "Low-Res Leads the Way" (LWay) training framework merging Supervised Pre-training with Self-supervised Learning to enhance the adaptability of SR models to real-world images. Our approach utilizes a low-resolution (LR) reconstruction network to extract degradation embeddings from LR images merging them with super-resolved outputs for LR reconstruction. Leveraging unseen LR images for self-supervised learning guides the model to adapt its modeling space to the target domain facilitating fine-tuning of SR models without requiring paired high-resolution (HR) images. The integration of Discrete Wavelet Transform (DWT) further refines the focus on high-frequency details. Extensive evaluations show that our method significantly improves the generalization and detail restoration capabilities of SR models on unseen real-world datasets outperforming existing methods. Our training regime is universally compatible requiring no network architecture modifications making it a practical solution for real-world SR applications.
BOTH2Hands: Inferring 3D Hands from Both Text Prompts and Body Dynamics
Authors: Wenqian Zhang, Molin Huang, Yuxuan Zhou, Juze Zhang, Jingyi Yu, Jingya Wang, Lan Xu
Published in: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024 (Main Conference Track), 2024

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

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

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.