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Facial Hair Area in Face recognition Across Demographics: Small Size, Big Effect

Facial Hair Area in Face Recognition Across Demographics: Sm...

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ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Wu, Haiyu Tian, Sicong Bhatta, Aman Ozturk, Kagan Ricanek, Karl, Jr. Bowyer, Kevin W. Univ Notre Dame Notre Dame IN 46556 USA Univ North Carolina Wilmington Wilmington NC USA Indiana Univ South Bend South Bend IN USA

ISBN: (纸本)9798350370287;9798350370713

Observed variations in face recognition accuracy across demographics, often viewed as "bias", have motivated research into the causes of such variations. Variations in facial hairstyle are an important potential cause of accuracy differences for males. In this work, we first explore how face recognition accuracy is affected by the facial hair region - clean-shaven, mustache, chin-area beard, side-to-side beard. Results show that mustache area facial hair has a greater effect on accuracy than either chin-area beard or side-to-side beard. We then employ a synthetic facial hair method to verify the consistency of the observation across five synthetic facial hair colors and three face matchers. Results of these experiments indicate that, the larger the difference in brightness between facial hair region and skin region, the larger impact of the mustache area. To reduce accuracy differences caused by facial hairstyle, quantified by Delta d '', we adjust the training dataset distribution to have increased representation of facial hair, resulting in an over 40% reduction in accuracy difference.

关键词： Face recognition

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Bias and Diversity in Synthetic-based Face recognition

Bias and Diversity in Synthetic-based Face Recognition

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ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Huber, Marco Anh Thi Luu Boutros, Fadi Kuijper, Arjan Damer, Naser Fraunhofer Inst Comp Graph Res IGD Darmstadt Germany Tech Univ Darmstadt Dept Comp Sci Darmstadt Germany

ISBN: (纸本)9798350318920;9798350318937

Synthetic data is emerging as a substitute for authentic data to solve ethical and legal challenges in handling authentic face data. The current models can create real-looking face images of people who do not exist. However, it is a known and sensitive problem that face recognition systems are susceptible to bias, i.e. performance differences between different demographic and non-demographics attributes, which can lead to unfair decisions. In this work, we investigate how the diversity of synthetic face recognition datasets compares to authentic datasets, and how the distribution of the training data of the generative models affects the distribution of the synthetic data. To do this, we looked at the distribution of gender, ethnicity, age, and head position. Furthermore, we investigated the concrete bias of three recent synthetic-based face recognition models on the studied attributes in comparison to a baseline model trained on authentic data. Our results show that the generator generate a similar distribution as the used training data in terms of the different attributes. With regard to bias, it can be seen that the synthetic-based models share a similar bias behavior with the authentic-based models. However, with the uncovered lower intra-identity attribute consistency seems to be beneficial in reducing bias.

关键词： Algorithms Biometrics body pose face gesture

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Virtual Sparse Convolution for Multimodal 3D Object Detection

Virtual Sparse Convolution for Multimodal 3D Object Detectio...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Wu, Hai Wen, Chenglu Shi, Shaoshuai Li, Xin Wang, Cheng Xiamen Univ Xiamen Peoples R China Max Planck Inst Munich Germany Texas A&M Univ College Stn TX 77843 USA

ISBN: (纸本)9798350301298

Recently, virtual/pseudo-point-based 3D object detection that seamlessly fuses RGB images and LiDAR data by depth completion has gained great attention. However, virtual points generated from an image are very dense, introducing a huge amount of redundant computation during detection. Meanwhile, noises brought by inaccurate depth completion significantly degrade detection precision. This paper proposes a fast yet effective backbone, termed VirConvNet, based on a new operator VirConv (Virtual Sparse Convolution), for virtual-point-based 3D object detection. VirConv consists of two key designs: (1) StVD (Stochastic Voxel Discard) and (2) NRConv (Noise-Resistant Sub-manifold Convolution). StVD alleviates the computation problem by discarding large amounts of nearby redundant voxels. NRConv tackles the noise problem by encoding voxel features in both 2D image and 3D LiDAR space. By integrating VirConv, we first develop an efficient pipeline VirConv-L based on an early fusion design. Then, we build a high-precision pipeline VirConv-T based on a transformed refinement scheme. Finally, we develop a semi-supervised pipeline VirConv-S based on a pseudo-label framework. On the KITTI car 3D detection test leaderboard, our VirConv-L achieves 85% AP with a fast running speed of 56ms. Our VirConv-T and VirConv-S attains a high-precision of 86.3% and 87.2% AP, and currently rank 2nd and 1st(1), respectively. The code is available at https://***/hailanyi/VirConv.

关键词： detection recognition: Categorization retrieval

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Semi-Supervised Semantic Depth Estimation using Symbiotic Transformer and NearFarMix Augmentation

Semi-Supervised Semantic Depth Estimation using Symbiotic Tr...

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ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Rahman, Md Awsafur Fattah, Shaikh Anowarul BUET Dept EEE Dhaka Bangladesh

ISBN: (纸本)9798350318920;9798350318937

In computer vision, depth estimation is crucial for domains like robotics, autonomous vehicles, augmented reality, and virtual reality. Integrating semantics with depth enhances scene understanding through reciprocal information sharing. However, the scarcity of semantic information in datasets poses challenges. Existing convolutional approaches with limited local receptive fields hinder the full utilization of the symbiotic potential between depth and semantics. This paper introduces a dataset-invariant semi-supervised strategy to address the scarcity of semantic information. It proposes the Depth Semantics Symbiosis module, leveraging the Symbiotic Transformer for achieving comprehensive mutual awareness by information exchange within both local and global contexts. Additionally, a novel augmentation, NearFarMix is introduced to combat overfitting and compensate both depth-semantic tasks by strategically merging regions from two images, generating diverse and structurally consistent samples with enhanced control. Extensive experiments on NYU-Depth-V2 and KITTI datasets demonstrate the superiority of our proposed techniques in indoor and outdoor environments.

关键词： Algorithms Algorithms Algorithms and algorithms Computational photography formulations image and video synthesis Image recognition and understanding Machine learning architectures

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OmniVec: Learning robust representations with cross modal sharing

OmniVec: Learning robust representations with cross modal sh...

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ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Srivastava, Siddharth Sharma, Gaurav TensorTour Inc Seattle WA 98164 USA

ISBN: (纸本)9798350318920;9798350318937

Majority of research in learning based methods has been towards designing and training networks for specific tasks. However, many of the learning based tasks, across modalities, share commonalities and could be potentially tackled in a joint framework. We present an approach in such direction, to learn multiple tasks, in multiple modalities, with a unified architecture. The proposed network is composed of task specific encoders, a common trunk in the middle, followed by task specific prediction heads. We first pre-train it by self-supervised masked training, followed by sequential training for the different tasks. We train the network on all major modalities, e.g. visual, audio, text and 3D, and report results on 22 diverse and challenging public benchmarks. We demonstrate empirically that, using a joint network to train across modalities leads to meaningful information sharing and this allows us to achieve state-of-the-art results on most of the benchmarks. We also show generalization of the trained network on cross-modal tasks as well as unseen datasets and tasks.

关键词： Algorithms Algorithms Image recognition and understanding vision + language and/or other modalities

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Perception-Oriented Single Image Super-Resolution using Optimal Objective Estimation

Perception-Oriented Single Image Super-Resolution using Opti...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Park, Seung Ho Moon, Young Su Cho, Nam Ik Seoul Natl Univ Dept Elect & Comp Engn INMC Seoul South Korea Samsung Elect Visual Display Div Suwon South Korea Seoul Natl Univ IPAI Seoul South Korea

ISBN: (纸本)9798350301298

Single-image super-resolution (SISR) networks trained with perceptual and adversarial losses provide highcontrast outputs compared to those of networks trained with distortion-oriented losses, such as L1 or L2. However, it has been shown that using a single perceptual loss is insufficient for accurately restoring locally varying diverse shapes in images, often generating undesirable artifacts or unnatural details. For this reason, combinations of various losses, such as perceptual, adversarial, and distortion losses, have been attempted, yet it remains challenging to find optimal combinations. Hence, in this paper, we propose a new SISR framework that applies optimal objectives for each region to generate plausible results in overall areas of high-resolution outputs. Specifically, the framework comprises two models: a predictive model that infers an optimal objective map for a given low-resolution (LR) input and a generative model that applies a target objective map to produce the corresponding SR output. The generative model is trained over our proposed objective trajectory representing a set of essential objectives, which enables the single network to learn various SR results corresponding to combined losses on the trajectory. The predictive model is trained using pairs of LR images and corresponding optimal objective maps searched from the objective trajectory. Experimental results on five benchmarks show that the proposed method outperforms state-of-the-art perception-driven SR methods in LPIPS, DISTS, PSNR, and SSIM metrics. The visual results also demonstrate the superiority of our method in perception-oriented reconstruction. The code is available at https://***/seungho-snu/SROOE.

关键词： Low-level vision

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RefTeacher: A Strong Baseline for Semi-Supervised Referring Expression Comprehension

RefTeacher: A Strong Baseline for Semi-Supervised Referring ...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Sun, Jiamu Luo, Gen Zhou, Yiyi Sun, Xiaoshuai Jiang, Guannan Wang, Zhiyu Ji, Rongrong Xiamen Univ Key Lab Multimedia Trusted Percept & Efficient Co Xiamen Peoples R China Xiamen Univ Inst Artificial Intelligence Xiamen Peoples R China Contemporary Amperex Technol Ningde Peoples R China Xiamen Univ Shenzhen Res Inst Xiamen Peoples R China

ISBN: (纸本)9798350301298

Referring expression comprehension (REC) often requires a large number of instance-level annotations for fully supervised learning, which are laborious and expensive. In this paper, we present the first attempt of semi-supervised learning for REC and propose a strong baseline method called RefTeacher. Inspired by the recent progress in computer vision, RefTeacher adopts a teacher-student learning paradigm, where the teacher REC network predicts pseudo-labels for optimizing the student one. This paradigm allows REC models to exploit massive unlabeled data based on a small fraction of labeled. In particular, we also identify two key challenges in semi-supervised REC, namely, sparse supervision signals and worse pseudo-label noise. To address these issues, we equip RefTeacher with two novel designs called Attention-based Imitation Learning (AIL) and Adaptive Pseudo-label Weighting (APW). AIL can help the student network imitate the recognition behaviors of the teacher, thereby obtaining sufficient supervision signals. APW can help the model adaptively adjust the contributions of pseudo-labels with varying qualities, thus avoiding confirmation bias. To validate RefTeacher, we conduct extensive experiments on three REC benchmark datasets. Experimental results show that RefTeacher obtains obvious gains over the fully supervised methods. More importantly, using only 10% labeled data, our approach allows the model to achieve near 100% fully supervised performance, e.g., only -2.78% on RefCOCO. Project: https://***/.

关键词： language reasoning vision

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Cloud-Device Collaborative Adaptation to Continual Changing Environments in the Real-world

Cloud-Device Collaborative Adaptation to Continual Changing ...

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ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Gan, Yulu Pan, Mingjie Zhang, Rongyu Ling, Zijian Zhao, Lingran Liu, Jiaming Zhang, Shanghang Peking Univ Sch Comp Sci Natl Key Lab Multimedia Informat Proc Beijing Peoples R China Chinese Univ Hong Kong Shenzhen Peoples R China Imperial Coll London London England

ISBN: (纸本)9798350301298

When facing changing environments in the real world, the lightweight model on client devices suffers from severe performance drops under distribution shifts. The main limitations of the existing device model lie in (1) unable to update due to the computation limit of the device, (2) the limited generalization ability of the lightweight model. Meanwhile, recent large models have shown strong generalization capability on the cloud while they can not be deployed on client devices due to poor computation constraints. To enable the device model to deal with changing environments, we propose a new learning paradigm of Cloud-Device Collaborative Continual Adaptation, which encourages collaboration between cloud and device and improves the generalization of the device model. Based on this paradigm, we further propose an Uncertainty-based Visual Prompt Adapted (U-VPA) teacher-student model to transfer the generalization capability of the large model on the cloud to the device model. Specifically, we first design the Uncertainty Guided Sampling (UGS) to screen out challenging data continuously and transmit the most out-of-distribution samples from the device to the cloud. Then we propose a Visual Prompt Learning Strategy with Uncertainty guided updating (VPLU) to specifically deal with the selected samples with more distribution shifts. We transmit the visual prompts to the device and concatenate them with the incoming data to pull the device testing distribution closer to the cloud training distribution. We conduct extensive experiments on two object detection datasets with continually changing environments. Our proposed U-VPA teacher-student framework outperforms previous state-of-the-art test time adaptation and device-cloud collaboration methods. The code and datasets will be released.

关键词： vision applications and systems

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vision Transformer for Multispectral Satellite Imagery: Advancing Landcover Classification

Vision Transformer for Multispectral Satellite Imagery: Adva...

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ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Rad, Ryan Northeastern Univ Khoury Coll Comp Sci Boston MA 02115 USA

ISBN: (纸本)9798350318920;9798350318937

Climate change is a global issue with significant impacts on ecosystems and human populations. Accurately classifying land cover from multi-spectral satellite imagery plays a crucial role in understanding the Earth's changing landscape and its implications for environmental processes. However, traditional methods struggle with challenges like limited data availability and capturing complex spatial-spectral relationships. vision Transformers have emerged as a promising alternative to convolutional neural networks (CNN architectures), harnessing the power of self-attention mechanisms to capture global and long-range dependencies. However, their application to multi-spectral images is still limited. In this paper, we propose a novel vision Transformer designed for multi-spectral satellite image datasets of limited size to perform reliable land cover identification with forty-four classes. We conduct extensive experiments on a curated dataset, simulating scenarios with limited data availability, and compare our approach to alternative architectures. The results demonstrate the potential of our vision Transformer-based method in achieving accurate land cover classification, contributing to improving climate change modeling and environmental understanding.

关键词： Algorithms Applications Applications Environmental monitoring / climate change / ecology Image recognition and understanding Remote Sensing

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Occlusion Sensitivity Analysis with Augmentation Subspace Perturbation in Deep Feature Space

Occlusion Sensitivity Analysis with Augmentation Subspace Pe...

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ieee/cvf Winter conference on Applications of computer vision (WACV)

作者： Valois, Pedro H. V. Niinuma, Koichiro Fukui, Kazuhiro Univ Tsukuba Tsukuba Ibaraki 305 Japan Fujitsu Res Amer Santa Clara CA USA

ISBN: (纸本)9798350318920;9798350318937

Deep Learning of neural networks has gained prominence in multiple life-critical applications like medical diagnoses and autonomous vehicle accident investigations. However, concerns about model transparency and biases persist. Explainable methods are viewed as the solution to address these challenges. In this study, we introduce the Occlusion Sensitivity Analysis with Deep Feature Augmentation Subspace (OSA-DAS), a novel perturbation-based interpretability approach for computer vision. While traditional perturbation methods make only use of occlusions to explain the model predictions, OSA-DAS extends standard occlusion sensitivity analysis by enabling the integration with diverse image augmentations. Distinctly, our method utilizes the output vector of a DNN to build low-dimensional subspaces within the deep feature vector space, offering a more precise explanation of the model prediction. The structural similarity between these subspaces encompasses the influence of diverse augmentations and occlusions. We test extensively on the ImageNet-1k, and our class- and model-agnostic approach outperforms commonly used interpreters, setting it apart in the realm of explainable AI.

关键词： accountable Algorithms Algorithms Applications ethical computer vision Explainable fair Image recognition and understanding privacy-preserving Visualization

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