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GaitContour: Efficient Gait recognition Based on a Contour-Pose Representation

GaitContour: Efficient Gait Recognition Based on a Contour-P...

引用

ieee Workshop on Applications of computer vision (WACV)

作者： Yuxiang Guo Anshul Shah Jiang Liu Ayush Gupta Rama Chellappa Cheng Peng Johns Hopkins University Baltimore MD USA

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Gait recognition holds the promise to robustly identify subjects based on walking patterns instead of appearance information. In recent years, this field has been dominated by learning methods based on two input formats: silhouette images and sparse keypoints. Compared to image-based approaches, keypoint-based methods can achieve significantly higher efficiency due to their sparsity. However, sparsity also results in information loss, thereby reducing performance. In this work, we propose a novel, keypoint-based Contour-Pose representation, which compactly encodes both body shape and parts information. We further propose a local-to-global architecture, called GaitContour, to leverage this novel representation and efficiently compute subject embedding in two stages. The first stage consists of a local transformer that extracts features from five different body regions. The second stage then aggregates the regional features to estimate a global human gait representation. Such a design significantly reduces the complexity of the attention operation and improves both efficiency and performance. Through large scale experiments, Gait-Contour is shown to perform significantly better than previous keypoint-based methods. Furthermore, the ContourPose representation also achieves new SoTA performances on fusion-based gait recognition methods.

关键词： Legged locomotion Learning systems computer vision Shape Computational modeling Aggregates computer architecture Transformers Feature extraction Gait recognition

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@BENCH: Benchmarking vision-Language Models for Human-centered Assistive Technology

@BENCH: Benchmarking Vision-Language Models for Human-center...

引用

ieee Workshop on Applications of computer vision (WACV)

作者： Xin Jiang Junwei Zheng Ruiping Liu Jiahang Li Jiaming Zhang Sven Matthiesen Rainer Stiefelhagen Karlsruhe Institute of Technology Li Auto Inc.

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

As vision-Language Models (VLMs) advance, human-centered Assistive Technologies (ATs) for helping People with Visual Impairments (PVIs) are evolving into generalists, capable of performing multiple tasks simultaneously. However, benchmarking VLMs for ATs remains under-explored. To bridge this gap, we first create a novel AT benchmark (@ Bench). Guided by a pre-design user study with PVIs, our benchmark includes the five most crucial vision-language tasks: Panoptic Segmentation, Depth Estimation, Optical Character recognition (OCR), Image Captioning, and Visual Question Answering (VQA). Besides, we propose a novel AT model (@MODEL) that addresses all tasks simultaneously and can be expanded to more assistive functions for helping PVIs. Our framework exhibits outstanding performance across tasks by integrating multi-modal information, and it offers PVIs a more comprehensive assistance. Extensive experiments prove the effectiveness and generalizability of our framework.

关键词： Image segmentation Visualization Depth measurement Optical character recognition Visual impairment Benchmark testing Assistive technologies Multitasking Question answering (information retrieval) Next generation networking

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Harmonizing Attention: Training-free Texture-aware Geometry Transfer

Harmonizing Attention: Training-free Texture-aware Geometry ...

引用

ieee Workshop on Applications of computer vision (WACV)

作者： Eito Ikuta Yohan Lee Akihiro Iohara Yu Saito Toshiyuki Tanaka DATAGRID Inc. Graduate School of Informatics Kyoto University

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Creating images where surface patterns of one object - such as cracks, holes, or grooves - are precisely transferred onto objects made of different materials remains a challenging task in computer graphics. For example, recreating the exact pattern of wood grain cracks on a metallic surface, while maintaining the realistic metallic texture, requires sophisticated technical solutions. In this study, we introduce Harmonizing Attention, a new method that can automatically extract these surface patterns from pho-tographs and recreate them with different materials, while preserving natural-looking textures. Our approach achieves this through a novel attention mechanism that can process multiple reference images simultaneously, without requiring additional training. This makes the method both practical and efficient for real-world applications, opening up new possibilities in augmented reality, image editing, and beyond.

关键词： Geometry Training computer vision computer graphics Distance measurement Surface texture Prompt engineering Surface cracks Augmented reality Surface treatment

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Rethinking Low-Rank Adaptation in vision: Exploring Head-Level Responsiveness across Diverse Tasks

Rethinking Low-Rank Adaptation in Vision: Exploring Head-Lev...

引用

ieee Workshop on Applications of computer vision (WACV)

作者： Yibo Zhong Yao Zhou Sichuan University

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Low-rank adaptation (LoRA) has shifted the paradigm of adapting pre-trained vision Transformers (ViT), achieving great efficiency by updating only a subset of tailored parameters to approximate weight updates. However, the multi-head design of the self-attention mechanism, with the heads working in parallel in the computation flow, exhibiting similar visual patterns and requiring update over all of them, incurs unnecessary storage and computational overhead. In this paper, we propose Head-level responsiveness tuning for low-rank adaptation (Heart-LoRA). The proposed method explores redundancy among the heads and selectively activates task-responsive heads, thus enabling fine-grained head-level tuning. Additionally, given the different responsiveness of heads to diverse visual tasks, our proposed method dynamically activates a subset of the approximated heads that are tailored to the current task. Experimental results show that Heart-LoRA yields superior performance over state-of-the-art PETL approaches on visual adaptation benchmark datasets.

关键词： Visualization computer vision Redundancy Benchmark testing Transformers Character recognition Few shot learning Tuning

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Stratified Domain Adaptation: A Progressive Self-Training Approach for Scene Text recognition

Stratified Domain Adaptation: A Progressive Self-Training Ap...

引用

ieee Workshop on Applications of computer vision (WACV)

作者： Kha Nhat Le Hoang-Tuan Nguyen Hung Tien Tran Thanh Duc Ngo University of Information Technology VNU-HCM Vietnam Vietnam National University Ho Chi Minh City Vietnam

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Unsupervised domain adaptation (UDA) has become increasingly prevalent in scene text recognition (STR), especially where training and testing data reside in different domains. The efficacy of existing UDA approaches tends to degrade when there is a large gap between the source and target domains. To deal with this problem, gradually shifting or progressively learning to shift from domain to domain is the key issue. In this paper, we introduce the Stratified Domain Adaptation (StrDA) approach, which examines the gradual escalation of the domain gap for the learning process. The objective is to partition the target data into subsets so that the progressively self-trained model can adapt to gradual changes. We stratify the target data by evaluating the proximity of each data sample to both the source and target domains. We propose a novel method for employing domain discriminators to estimate the out-of-distribution and domain discriminative levels of data samples. Extensive experiments on benchmark scene-text datasets show that our approach significantly improves the performance of baseline (source-trained) STR models. The source code is available at https://***/KhaLee2307/StrDA.

关键词： Training Adaptation models computer vision Text recognition Computational modeling Source coding Benchmark testing Harmonic analysis Data models

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Semantically Conditioned Prompts for Visual recognition Under Missing Modality Scenarios

Semantically Conditioned Prompts for Visual Recognition Unde...

引用

ieee Workshop on Applications of computer vision (WACV)

作者： Vittorio Pipoli Federico Bolelli Sara Sarto Marcella Cornia Lorenzo Baraldi Costantino Grana Rita Cucchiara Elisa Ficarra University of Modena and Reggio Emilia Italy University of Pisa Italy

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

This paper tackles the domain of multimodal prompting for visual recognition, specifically when dealing with missing modalities through multimodal Transformers. It presents two main contributions: (i) we introduce a novel prompt learning module which is designed to produce sample-specific prompts and (ii) we show that modalityagnostic prompts can effectively adjust to diverse missing modality scenarios. Our model, termed SCP, exploits the semantic representation of available modalities to query a learnable memory bank, which allows the generation of prompts based on the semantics of the input. Notably, SCP distinguishes itself from existing methodologies for its capacity of self-adjusting to both the missing modality scenario and the semantic context of the input, without prior knowledge about the specific missing modality and the number of modalities. Through extensive experiments, we show the effectiveness of the proposed prompt learning framework and demonstrate enhanced performance and robustness across a spectrum of missing modality cases. Our source code is available at https://***/vittoriopipoli/SCP_WACV2025.

关键词： Visualization computer vision Source coding Semantics Transformers Robustness

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CACE: Sim-to-Real Indoor 3D Semantic Segmentation via Context-Aware Augmentation and Consistency Enforcement

CACE: Sim-to-Real Indoor 3D Semantic Segmentation via Contex...

引用

ieee Workshop on Applications of computer vision (WACV)

作者： Tsung-Yu Chen Luyu Yang Tzu-Yu Chuang Shang-Hong Lai Carnegie Mellon University National Tsing Hua University University of Maryland

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Indoor 3D domain adaptation for semantic segmentation is an understudied task. The first unsupervised sim-to-real benchmark was only proposed recently. Existing methods try to modify the source domain data by simulating the occlusion and noise pattern of the target domain. However, this methodology unrealistically demands a clear definition of the real-world data patterns, and is highly dependent on the simulation quality. In this paper, we propose a novel adaptation framework via Context-aware Augmentation and Consistency Enforcement (CACE). Our CACE framework consists of two modules, a space and context-aware augmentation module that is invariant of target data pattern and domain gaps, and a carefully designed self-supervision module that maximizes the utility of the augmented data. Our CACE surpasses the state-of-the-art method by over 6% on the indoor 3D sim-to-real benchmark $3D-FRONT\rightarrow ScanNet$ .

关键词： computer vision Three-dimensional displays Semantic segmentation Noise Training data Benchmark testing

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Automated Patient Positioning with Learned 3D Hand Gestures

Automated Patient Positioning with Learned 3D Hand Gestures

引用

ieee Workshop on Applications of computer vision (WACV)

作者： Zhongpai Gao Abhishek Sharma Meng Zheng Benjamin Planche Terrence Chen Ziyan Wu United Imaging Intelligence Boston MA USA

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Positioning patients for scanning and interventional procedures is a critical task that requires high precision and accuracy. The conventional workflow involves manually adjusting the patient support to align the center of the target body part with the laser projector or other guiding devices. This process is not only time-consuming but also prone to inaccuracies. In this work, we propose an automated patient positioning system that utilizes a camera to detect specific hand gestures from technicians, allowing users to indicate the target patient region to the system and initiate automated positioning. Our approach relies on a novel multi-stage pipeline to recognize and interpret the technicians' gestures, translating them into precise motions of medical devices. We evaluate our proposed pipeline during actual MRI scanning procedures, using RGB-Depth cameras to capture the process. Results show that our system achieves accurate and precise patient positioning with minimal technician intervention. Furthermore, we validate our method on HaGRID, a large-scale hand gesture dataset, demonstrating its effectiveness in hand detection and gesture recognition.

关键词： Hands computer vision Accuracy Three-dimensional displays Medical devices Magnetic resonance imaging Pipelines Lasers Gesture recognition Cameras

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ComFace: Facial Representation Learning with Synthetic Data for Comparing Faces

ComFace: Facial Representation Learning with Synthetic Data ...

引用

ieee Workshop on Applications of computer vision (WACV)

作者： Yusuke Akamatsu Terumi Umematsu Hitoshi Imaoka Shizuko Gomi Hideo Tsurushima NEC Corporation Japan University of Tsukuba Japan

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Daily monitoring of intra-personal facial changes associated with health and emotional conditions has great potential to be useful for medical, healthcare, and emotion recognition fields. However, the approach for capturing intra-personal facial changes is relatively unexplored due to the difficulty of collecting temporally changing face images. In this paper, we propose a facial representation learning method using synthetic images for comparing faces, called ComFace, which is designed to capture intra-personal facial changes. For effective representation learning, ComFace aims to acquire two feature representations, i.e., inter-personal facial differences and intra-personal facial changes. The key point of our method is the use of synthetic face images to overcome the limitations of collecting real intra-personal face images. Facial representations learned by ComFace are transferred to three extensive downstream tasks for comparing faces: estimating facial expression changes, weight changes, and age changes from two face images of the same individual. Our Com-Face, trained using only synthetic data, achieves comparable to or better transfer performance than general pretraining and state-of-the-art representation learning methods trained using real images.

关键词： Representation learning Emotion recognition computer vision Face recognition Medical services Monitoring Synthetic data Biomedical imaging

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FitDiff: Robust Monocular 3D Facial Shape and Reflectance Estimation using Diffusion Models

FitDiff: Robust Monocular 3D Facial Shape and Reflectance Es...

引用

ieee Workshop on Applications of computer vision (WACV)

作者： Stathis Galanakis Alexandros Lattas Stylianos Moschoglou Stefanos Zafeiriou Imperial College London HUAWEI Noah's Ark Lab

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

The remarkable progress in 3D face reconstruction has resulted in high-detail and photorealistic facial representations. Recently, Diffusion Models have revolutionized the capabilities of generative methods by surpassing the performance of GANs. In this work, we present FitDiff, a diffusion-based 3D facial avatar generative model. Lever-aging diffusion principles, our model accurately generates relightable facial avatars, utilizing an identity embedding extracted from an “in-the-wild” 2D facial image. The introduced multimodal diffusion model is the first to concurrently output facial reflectance maps (diffuse and specular albedo and normals) and shapes, showcasing great generalization capabilities. It is solely trained on an annotated subset of a public facial dataset, paired with 3D reconstructions. We revisit the typical 3D facial fitting approach by guiding a reverse diffusion process using perceptual and face recognition losses. Being the first 3D LDM conditioned on face recognition embeddings, FitDiff reconstructs relightable human avatars, that can be used as-is in common rendering engines, starting only from an unconstrained facial image, and achieving state-of-the-art performance.

关键词： Reflectivity Solid modeling computer vision Three-dimensional displays Shape Face recognition Avatars Lighting Diffusion models Image reconstruction

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