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Towards Visual Saliency Explanations of Face Verification

Towards Visual Saliency Explanations of Face Verification

引用

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

作者： Lu, Yuhang Xu, Zewei Ebrahimi, Touradj Ecole Polytech Fed Lausanne Lausanne Switzerland

ISBN: (纸本)9798350318920;9798350318937

In the past years, deep convolutional neural networks have been pushing the frontier of face recognition (FR) techniques in both verification and identification scenarios. Despite the high accuracy, they are often criticized for lacking explainability. There has been an increasing demand for understanding the decision-making process of deep face recognition systems. Recent studies have investigated the usage of visual saliency maps as an explanation, but they often lack a discussion and analysis in the context of face recognition. This paper concentrates on explainable face verification tasks and conceives a new explanation framework. Firstly, a definition of the saliency-based explanation method is provided, which focuses on the decisions made by the deep FR model. Secondly, a new model-agnostic explanation method named CorrRISE is proposed to produce saliency maps, which reveal both the similar and dissimilar regions of any given pair of face images. Then, an evaluation methodology is designed to measure the performance of general visual saliency explanation methods in face verification. Finally, substantial visual and quantitative results have shown that the proposed CorrRISE method demonstrates promising results in comparison with other state-of-the-art explainable face verification approaches.

关键词： accountable Algorithms Algorithms Biometrics body pose ethical computer vision Explainable face fair gesture privacy-preserving

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Optimizing Long-Term Robot Tracking with Multi-Platform Sensor Fusion

Optimizing Long-Term Robot Tracking with Multi-Platform Sens...

引用

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

作者： Albanese, Giuliano Mitra, Arka Zaech, Jan-Nico Zhao, Yupeng Chhatkuli, Ajad Van Gool, Luc Swiss Fed Inst Technol Zurich Switzerland Katholieke Univ Leuven Leuven Belgium INSAIT Sofia Bulgaria

ISBN: (纸本)9798350318920;9798350318937

Monitoring a fleet of robots requires stable long-term tracking with re-identification, which is yet an unsolved challenge in many scenarios. One application of this is the analysis of autonomous robotic soccer games at RoboCup. Tracking in these games requires handling of identically looking players, strong occlusions, and non-professional video recordings, but also offers state information estimated by the robots. In order to make effective use of the information coming from the robot sensors, we propose a robust tracking and identification pipeline. It fuses external non-calibrated camera data with the robots' internal states using quadratic optimization for tracklet matching. The approach is validated using game recordings from previous RoboCup World Cup tournaments.

关键词： Algorithms Algorithms and algorithms Applications formulations Machine learning architectures Robotics Video recognition and understanding

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AI-Synthesized Voice Detection Using Neural Vocoder Artifacts

AI-Synthesized Voice Detection Using Neural Vocoder Artifact...

引用

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

作者： Sun, Chengzhe Jia, Shan Hou, Shuwei Lyu, Siwei SUNY Buffalo Buffalo NY 14260 USA

ISBN: (纸本)9798350302493

Advancements in AI-synthesized human voices have created a growing threat of impersonation and disinformation, making it crucial to develop methods to detect synthetic human voices. This study proposes a new approach to identifying synthetic human voices by detecting artifacts of vocoders in audio signals. Most DeepFake audio synthesis models use a neural vocoder, a neural network that generates waveforms from temporal-frequency representations like mel-spectrograms. By identifying neural vocoder processing in audio, we can determine if a sample is synthesized. To detect synthetic human voices, we introduce a multi-task learning framework for a binary-class RawNet2 model that shares the feature extractor with a vocoder identification module. By treating vocoder identification as a pretext task, we constrain the feature extractor to focus on vocoder artifacts and provide discriminative features for the final binary classifier. Our experiments show that the improved RawNet2 model based on vocoder identification achieves high classification performance on the binary task overall. Codes and data can be found at https:// github. com/ csun22/SyntheticVoice-Detection- Vocoder-Artifacts.

关键词： Speech recognition

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DropKey for vision Transformer

DropKey for Vision Transformer

引用

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

作者： Li, Bonan Hu, Yinhan Nie, Xuecheng Han, Congying Jiang, Xiangjian Guo, Tiande Liu, Luocji Univ Chinese Acad Sci Beijing Peoples R China Meitu Inc MT Lab Beijing Peoples R China Univ Cambridge Cambridge England

ISBN: (纸本)9798350301298

In this paper, we focus on analyzing and improving the dropout technique for self-attention layers of vision Transformer, which is important while surprisingly ignored by prior works. In particular, we conduct researches on three core questions: First, what to drop in self-attention layers? Different from dropping attention weights in literature, we propose to move dropout operations forward ahead of attention matrix calculation and set the Key as the dropout unit, yielding a novel dropout-before-softmax scheme. We theoretically verify that this scheme helps keep both regularization and probability features of attention weights, alleviating the overfittings problem to specific patterns and enhancing the model to globally capture vital information;Second, how to schedule the drop ratio in consecutive layers? In contrast to exploit a constant drop ratio for all layers, we present a new decreasing schedule that gradually decreases the drop ratio along the stack of self-attention layers. We experimentally validate the proposed schedule can avoid overfittings in low-level features and missing in high-level semantics, thus improving the robustness and stableness of model training;Third, whether need to perform structured dropout operation as CNN? We attempt patch-based block-version of dropout operation and find that this useful trick for CNN is not essential for ViT. Given exploration on the above three questions, we present the novel DropKey method that regards Key as the drop unit and exploits decreasing schedule for drop ratio, improving ViTs in a general way. Comprehensive experiments demonstrate the effectiveness of DropKey for various ViT architectures, e.g. T2T, VOLO, CeiT and DeiT, as well as for various vision tasks, e.g., image classification, object detection, human-object interaction detection and human body shape recovery.

关键词： computer vision theory

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Efficient Explainable Face Verification based on Similarity Score Argument Backpropagation

Efficient Explainable Face Verification based on Similarity ...

引用

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

作者： Huber, Marco Luu, Anh Thi Terhoerst, Philipp Damer, Naser Fraunhofer Inst Comp Graph Res IGD Darmstadt Germany Tech Univ Darmstadt Dept Comp Sci Darmstadt Germany Univ Paderborn Paderborn Germany

ISBN: (纸本)9798350318920;9798350318937

Explainable Face recognition is gaining growing attention as the use of the technology is gaining ground in security-critical applications. Understanding why two face images are matched or not matched by a given face recognition system is important to operators, users, and developers to increase trust, accountability, develop better systems, and highlight unfair behavior. In this work, we propose a similarity score argument backpropagation (xSSAB) approach that supports or opposes the face-matching decision to visualize spatial maps that indicate similar and dissimilar areas as interpreted by the underlying FR model. Furthermore, we present Patch-LFW, a new explainable face verification benchmark that enables along with a novel evaluation protocol, the first quantitative evaluation of the validity of similarity and dissimilarity maps in explainable face recognition approaches. We compare our efficient approach to state-of-the-art approaches demonstrating a superior trade-off between efficiency and performance. The code as well as the proposed Patch-LFW is publicly available at: https://***/marcohuber/xSSAB.

关键词： accountable Algorithms Algorithms Biometrics body pose ethical computer vision Explainable face fair gesture privacy-preserving

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Classifying Cable Tendency with Semantic Segmentation by Utilizing Real and Simulated RGB Data

Classifying Cable Tendency with Semantic Segmentation by Uti...

引用

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

作者： Chien, Pei-Chun Liao, Powei Fukuzawa, Eiji Ohya, Jun Yazaki Corp Shizuoka Japan Waseda Univ Tokyo Japan

ISBN: (纸本)9798350318920;9798350318937

Cable tendency is the potential shape or characteristic that a cable may possess while being manipulated, of which some are considered erroneous and should be identified as a part of anomaly detection during an automatic manipulation. This research explores the ability of deep-learning models in learning the cable tendencies that, contrary to typical classification tasks of multi-object scenarios, is to differentiate the multiple states displayable by the same object - in this case, cables. By training multiple models with different combinations of self-collected real-world data and self-generated simulation data, a comparative study is carried out to compare the performance of each approach. In conclusion, the effectiveness of detecting three abnormal states and shapes of cables, and using simulation data is certificated in experiments.

关键词： Algorithms Algorithms Applications Computational photography image and video synthesis Image recognition and understanding Robotics

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Event-guided Person Re-Identification via Sparse-Dense Complementary Learning

Event-guided Person Re-Identification via Sparse-Dense Compl...

引用

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

作者： Cao, Chengzhi Fu, Xueyang Liu, Hongjian Huang, Yukun Wang, Kunyu Luo, Jiebo Zha, Zheng-Jun Univ Sci & Technol China Hefei Peoples R China Univ Rochester Rochester NY 14627 USA

ISBN: (纸本)9798350301298

Video-based person re-identification (Re-ID) is a prominent computer vision topic due to its wide range of video surveillance applications. Most existing methods utilize spatial and temporal correlations in frame sequences to obtain discriminative person features. However, inevitable degradation, e.g., motion blur contained in frames, leading to the loss of identity-discriminating cues. Recently, a new bio-inspired sensor called event camera, which can asynchronously record intensity changes, brings new vitality to the Re-ID task. With the microsecond resolution and low latency, it can accurately capture the movements of pedestrians even in the degraded environments. In this work, we propose a Sparse-Dense Complementary Learning (SDCL) Framework, which effectively extracts identity features by fully exploiting the complementary information of dense frames and sparse events. Specifically, for frames, we build a CNN-based module to aggregate the dense features of pedestrian appearance step by step, while for event streams, we design a bio-inspired spiking neural network (SNN) backbone, which encodes event signals into sparse feature maps in a spiking form, to extract the dynamic motion cues of pedestrians. Finally, a cross feature alignment module is constructed to fuse motion information from events and appearance cues from frames to enhance identity representation learning. Experiments on several benchmarks show that by employing events and SNN into Re-ID, our method significantly outperforms competitive methods. The code is available at https://***/ChengzhiCao/SDCL.

关键词： detection recognition: Categorization retrieval

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Learning Intra-class Multimodal Distributions with Orthonormal Matrices

Learning Intra-class Multimodal Distributions with Orthonorm...

引用

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

作者： Goto, Jumpei Nakata, Yohei Abe, Kiyofumi Ishii, Yasunori Yamashita, Takayoshi Panasonic Holdings Corp Kadoma Osaka Japan Chubu Univ Kasugai Aichi Japan

ISBN: (纸本)9798350318920;9798350318937

In this paper, we address the challenges of representing feature distributions which have multimodality within a class in deep neural networks. Existing online clustering methods employ sub-centroids to capture intra-class variations. However, conducting online clustering faces some limitations, i.e., online clustering assigns only a single subcentroid to a feature vector extracted from a backbone and ignores the relationship between the other sub-centroids and the feature vector, and updating sub-centroids in an online clustering manner incurs significant storage costs. To address these limitations, we propose a novel method utilizing orthonormal matrices instead of sub-centroids for relaxing discrete assignments into continuous assignments. We update the orthonormal matrices using a gradient-based method, which eliminates the need for online clustering or additional storage. Experimental results on the CIFAR and ImageNet datasets exhibit that the proposed method outperforms current online clustering techniques in classification accuracy, sub-category discovery, and transferability, providing an efficient solution to the challenges posed by complex recognition targets.

关键词： accountable Algorithms Algorithms Algorithms and algorithms ethical computer vision Explainable fair formulations Image recognition and understanding Machine learning architectures privacy-preserving

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Evaluation of Video Masked Autoencoders' Performance and Uncertainty Estimations for Driver Action and Intention recognition

Evaluation of Video Masked Autoencoders' Performance and Unc...

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

作者： Vellenga, Koen Steinhauer, H. Joe Falkman, Goran Bjorklund, Tomas Univ Skovde Skovde Sweden Volvo Car Corp Gothenburg Sweden

ISBN: (纸本)9798350318920;9798350318937

Traffic fatalities remain among the leading death causes worldwide. To reduce this figure, car safety is listed as one of the most important factors. To actively support human drivers, it is essential for advanced driving assistance systems to be able to recognize the driver's actions and intentions. Prior studies have demonstrated various approaches to recognize driving actions and intentions based on in-cabin and external video footage. Given the performance of self-supervised video pre-trained (SSVP) Video Masked Autoencoders (VMAEs) on multiple action recognition datasets, we evaluate the performance of SSVP VMAEs on the Honda Research Institute Driving Dataset for driver action recognition (DAR) and on the Brain4Cars dataset for driver intention recognition (DIR). Besides the performance, the application of an artificial intelligence system in a safety-critical environment must be capable to express when it is uncertain about the produced results. Therefore, we also analyze uncertainty estimations produced by a Bayes-by-Backprop last-layer (BBB-LL) and Monte-Carlo (MC) dropout variants of an VMAE. Our experiments show that an VMAE achieves a higher overall performance for both offline DAR and end-to-end DIR compared to the state-of-the-art. The analysis of the BBB-LL and MC dropout models show higher uncertainty estimates for incorrectly classified test instances compared to correctly predicted test instances.

关键词： Applications Applications Autonomous Driving Robotics

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SynthVSR: Scaling Up Visual Speech recognition With Synthetic Supervision

SynthVSR: Scaling Up Visual Speech Recognition With Syntheti...

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

作者： Liu, Xubo Lakomkin, Egor Vougioukas, Konstantinos Ma, Pingchuan Chen, Honglie Xie, Ruiming Doulaty, Morrie Moritz, Niko Kolar, Jachym Petridis, Stavros Pantic, Maja Fuegen, Christian Univ Surrey Guildford Surrey England Meta AI New York NY 58051 USA

ISBN: (纸本)9798350301298

Recently reported state-of-the-art results in visual speech recognition (VSR) often rely on increasingly large amounts of video data, while the publicly available transcribed video datasets are limited in size. In this paper, for the first time, we study the potential of leveraging synthetic visual data for VSR. Our method, termed SynthVSR, substantially improves the performance of VSR systems with synthetic lip movements. The key idea behind SynthVSR is to leverage a speech-driven lip animation model that generates lip movements conditioned on the input speech. The speech-driven lip animation model is trained on an unlabeled audio-visual dataset and could be further optimized towards a pre-trained VSR model when labeled videos are available. As plenty of transcribed acoustic data and face images are available, we are able to generate large-scale synthetic data using the proposed lip animation model for semi-supervised VSR training. We evaluate the performance of our approach on the largest public VSR benchmark - Lip Reading Sentences 3 (LRS3). SynthVSR achieves a WER of 43.3% with only 30 hours of real labeled data, outperforming off-the-shelf approaches using thousands of hours of video. The WER is further reduced to 27.9% when using all 438 hours of labeled data from LRS3, which is on par with the state-of-the-art self-supervised AV-HuBERT method. Furthermore, when combined with large-scale pseudo-labeled audio-visual data SynthVSR yields a new state-of-the-art VSR WER of 16.9% using publicly available data only, surpassing the recent state-of-the-art approaches trained with 29 times more non-public machine-transcribed video data (90,000 hours). Finally, we perform extensive ablation studies to understand the effect of each component in our proposed method.

关键词： and reasoning language vision

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