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检索条件"任意字段=Conference on Computer Vision and Pattern Recognition"

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Neural Response Interpretation through the Lens of Critical Pathways

Neural Response Interpretation through the Lens of Critical ...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Khakzar, Ashkan Baselizadeh, Soroosh Khanduja, Saurabh Rupprecht, Christian Kim, Seong Tae Navab, Nassir CAMP Tech Univ Munich Munich Germany VGG Univ Oxford Oxford England

ISBN: (纸本)9781665445092

Is critical input information encoded in specific sparse pathways within the neural network? In this work, we discuss the problem of identifying these critical pathways and subsequently leverage them for interpreting the network's response to an input. The pruning objective - selecting the smallest group of neurons for which the response remains equivalent to the original network - has been previously proposed for identifying critical pathways. We demonstrate that sparse pathways derived from pruning do not necessarily encode critical input information. To ensure sparse pathways include critical fragments of the encoded input information, we propose pathway selection via neurons' contribution to the response. We proceed to explain how critical pathways can reveal critical input features. We prove that pathways selected via neuron contribution are locally linear (in an l(2)-ball), a property that we use for proposing a feature attribution method: "pathway gradient". We validate our interpretation method using mainstream evaluation experiments. The validation of pathway gradient interpretation method further confirms that selected pathways using neuron contributions correspond to critical input features. The code(1 2) is publicly available.

关键词： Gradient methods computer vision computer network reliability Neurons pattern recognition Reliability Object recognition

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10th IAPR TC3 International Workshop on Artificial Neural Networks in pattern recognition, ANNPR 2022

10th IAPR TC3 International Workshop on Artificial Neural Ne...

引用

10th IAPR TC3 International Workshop on Artificial Neural Networks in pattern recognition, ANNPR 2022

ISBN: (纸本)9783031206498

The proceedings contain 16 papers. The special focus in this conference is on Artificial Neural Networks in pattern recognition. The topics include: Sequence-to-Sequence CNN-BiLSTM Based Glottal Closure Instant Detection from Raw Speech;preface;graph Augmentation for Neural Networks Using Matching-Graphs;Mono vs Multilingual BERT for Hate Speech Detection and Text Classification: A Case Study in Marathi;transformer-Encoder Generated Context-Aware Embeddings for Spell Correction;assessment of Pharmaceutical Patent Novelty with Siamese Neural Networks;white Blood Cell Classification of Porcine Blood Smear Images;medical Deepfake Detection using 3-Dimensional Neural Learning;a Study on the Autonomous Detection of Impact Craters;utilization of vision Transformer for Classification and Ranking of Video Distortions;a Novel Representation of Graphical patterns for Graph Convolution Networks;minimizing Cross Intersections in Graph Drawing via Linear Splines;multi-stage Bias Mitigation for Individual Fairness in Algorithmic Decisions;do Minimal Complexity Least Squares Support Vector Machines Work?.

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Multi-View Action recognition for Distracted Driver Behavior Localization

Multi-View Action Recognition for Distracted Driver Behavior...

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IEEE computer Society conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Yuehuan Xu Shuai Jiang Zhe Cui Fei Su Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System and Network Culture China

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The detection and recognition of distracted driving behaviors has emerged as a new vision task with the rapid development of computer vision, which is considered as a challenging temporal action localization (TAL) problem in computer vision. The primary goal of temporal localization is to determine the start and end time of actions in untrimmed videos. Currently, most state-of-the-art temporal localization methods adopt complex architectures, which are cumbersome and time-consuming. In this paper, we propose a robust and efficient two-stage framework for distracted behavior classification-localization based on the sliding window approach, which is suitable for untrimmed naturalistic driving videos. To address the issues of high similarity among different behaviors and interference from background classes, we propose a multi-view fusion and adaptive thresholding algorithm, which effectively reduces missing detections. To address the problem of fuzzy behavior boundary localization, we design a post-processing procedure that achieves fine localization from coarse localization through post connection and candidate behavior merging criteria. In the AICITY2024 Task3 TestA, our method performs well, achieving Average Intersection over Union(AIOU) of 0.6080 and ranking eighth in AICITY2024 Task3. Our code will be released in the near future.

关键词： Location awareness computer vision Filtering Splicing Urban areas Merging Interference

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Micro-expression recognition Based on Facial Graph Representation Learning and Facial Action Unit Fusion

Micro-expression Recognition Based on Facial Graph Represent...

引用

IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Lei, Ling Chen, Tong Li, Shigang Li, Jianfeng Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligen Chongqing Peoples R China Hiroshima City Univ Grad Sch Informat Sci Hiroshima Japan

ISBN: (纸本)9781665448994

Micro-expressions recognition is a challenge because it involves subtle variations in facial organs. In this paper, first, we propose a novel pipeline to learn a facial graph (nodes and edges) representation to capture these local subtle variations. We express the micro-expressions with multi-patches based on facial landmarks and then stack these patches into channels while using a depthwise convolution (DConv) to learn the features inside the patches, namely, node learning. Then, the encoder of the transformer (ETran) is utilized to learn the relationships between the nodes, namely, edge learning. Based on node and edge learning, a learned facial graph representation is obtained. Second, because the occurrence of an expression is closely bound to action units, we design an AU-GCN to learn the action unit's matrix by embedding and GCN. Finally, we propose a fusion model to introduce the action unit's matrix into the learned facial graph representation. The experiments are comparing with SOTA on various evaluation criteria, including common classifications on CASME II and SAMM datasets, and also conducted following Micro-expression Grand Challenge 2019 protocol.

关键词： computer vision Protocols Convolution Face recognition conferences Pipelines Feature extraction

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Self-Supervised Wasserstein Pseudo-Labeling for Semi-Supervised Image Classification

Self-Supervised Wasserstein Pseudo-Labeling for Semi-Supervi...

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IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Taherkhani, Fariborz Dabouei, Ali Soleymani, Sobhan Dawson, Jeremy Nasrabadi, Nasser M. West Virginia Univ Morgantown WV 26506 USA

ISBN: (纸本)9781665445092

The goal is to use Wasserstein metric to provide pseudo labels for the unlabeled images to train a Convolutional Neural Networks (CNN) in a Semi-Supervised Learning (SSL) manner for the classification task. The basic premise in our method is that the discrepancy between two discrete empirical measures (e.g., clusters) which come from the same or similar distribution is expected to be less than the case where these measures come from completely two different distributions. In our proposed method, we first pre-train our CNN using a self-supervised learning method to make a cluster assumption on the unlabeled images. Next, inspired by the Wasserstein metric which considers the geometry of the metric space to provide a natural notion of similarity between discrete empirical measures, we leverage it to cluster the unlabeled images and then match the clusters to their similar class of labeled images to provide a pseudo label for the data within each cluster. We have evaluated and compared our method with state-of-the-art SSL methods on the standard datasets to demonstrate its effectiveness.

关键词： Measurement Training Clustering algorithms Semisupervised learning Data models pattern recognition Convolutional neural networks

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You See What I Want You to See: Exploring Targeted Black-Box Transferability Attack for Hash-based Image Retrieval Systems

You See What I Want You to See: Exploring Targeted Black-Box...

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IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Xiao, Yanru Wang, Cong Old Dominion Univ Norfolk VA 23529 USA

ISBN: (纸本)9781665445092

With the large multimedia content online, deep hashing has become a popular method for efficient image retrieval and storage. However, by inheriting the algorithmic back-end from softmax classification, these techniques are vulnerable to the well-known adversarial examples as well. The massive collection of online images into the database also opens up new attack vectors. Attackers can embed adversarial images into the database and target specific categories to be retrieved by user queries. In this paper, we start from an adversarial standpoint to explore and enhance the capacity of targeted black-box transferability attack for deep hashing. We motivate this work by a series of empirical studies to see the unique challenges in image retrieval. We study the relations between adversarial subspace and black-box transferability via utilizing random noise as a proxy. Then we develop a new attack that is simultaneously adversarial and robust to noise to enhance transferability. Our experimental results demonstrate about 1.2-3x improvements of black-box transferability compared with the state-of-the-art mechanisms.

关键词： computer vision Codes Image retrieval Multimedia databases pattern recognition Classification algorithms Image storage

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Regularizing Generative Adversarial Networks under Limited Data

Regularizing Generative Adversarial Networks under Limited D...

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IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Tseng, Hung-Yu Jiang, Lu Liu, Ce Yang, Ming-Hsuan Yang, Weilong Google Res Mountain View CA USA Univ Calif Merced Merced CA 95343 USA Waymo Mountain View CA USA Yonsei Univ Seoul South Korea

ISBN: (纸本)9781665445092

Recent years have witnessed the rapid progress of generative adversarial networks (GANs). However, the success of the GAN models hinges on a large amount of training data. This work proposes a regularization approach for training robust GAN models on limited data. We theoretically show a connection between the regularized loss and an f-divergence called LeCam-divergence, which we find is more robust under limited training data. Extensive experiments on several benchmark datasets demonstrate that the proposed regularization scheme 1) improves the generalization performance and stabilizes the learning dynamics of GAN models under limited training data, and 2) complements the recent data augmentation methods. These properties facilitate training GAN models to achieve state-of-the-art performance when only limited training data of the ImageNet benchmark is available. The source code is available at https://***/google/lecam-gan.

关键词： Training computer vision Codes Computational modeling Training data Benchmark testing Fasteners

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Multimodal Motion Prediction with Stacked Transformers

Multimodal Motion Prediction with Stacked Transformers

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IEEE/CVF conference on computer vision and pattern recognition (CVPR)

作者： Liu, Yicheng Zhang, Jinghuai Fang, Liangji Jiang, Qinhong Zhou, Bolei Chinese Univ Hong Kong Hong Kong Peoples R China SenseTime Res Hong Kong Peoples R China

ISBN: (纸本)9781665445092

Predicting multiple plausible future trajectories of the nearby vehicles is crucial for the safety of autonomous driving. Recent motion prediction approaches attempt to achieve such multimodal motion prediction by implicitly regularizing the feature or explicitly generating multiple candidate proposals. However, it remains challenging since the latent features may concentrate on the most frequent mode of the data while the proposal-based methods depend largely on the prior knowledge to generate and select the proposals. In this work, we propose a novel transformer framework for multimodal motion prediction, termed as mmTransformer. A novel network architecture based on stacked transformers is designed to model the multimodality at feature level with a set of fixed independent proposals. A region-based training strategy is then developed to induce the multimodality of the generated proposals. Experiments on Argoverse dataset show that the proposed model achieves the state-of-the-art performance on motion prediction, substantially improving the diversity and the accuracy of the predicted trajectories. Demo video and code are available at https://***/mmTransformer.

关键词： Training computer vision Computational modeling Predictive models Network architecture Transformers Trajectory

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Masked and Shuffled Blind Spot Denoising for Real-World Images

Masked and Shuffled Blind Spot Denoising for Real-World Imag...

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conference on computer vision and pattern recognition (CVPR)

作者： Hamadi Chihaoui Paolo Favaro Computer Vision Group University of Bern Switzerland

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

We introduce a novel approach to single image denoising based on the Blind Spot Denoising principle, which we call MAsked and SHuffled Blind Spot Denoising (MASH). We focus on the case of correlated noise, which often plagues real images. MASH is the result of a careful analysis to determine the relationships between the level of blindness (masking) of the input and the (unknown) noise correlation. Moreover, we introduce a shuffling technique to weaken the local correlation of noise, which in turn yields an additional denoising performance improvement. We evaluate MASH via extensive experiments on real-world noisy image datasets. We demonstrate state-of-the-art results compared to existing self-supervised denoising methods. Website: https://***/mash.

关键词： Multi-stage noise shaping Training computer vision Correlation Image color analysis Noise reduction Noise

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HDQMF: Holographic Feature Decomposition using Quantum Algorithms

HDQMF: Holographic Feature Decomposition using Quantum Algor...

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conference on computer vision and pattern recognition (CVPR)

作者： Prathyush Poduval Zhuowen Zou Mohsen Imani University of California Irvine Irvine CA

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

This paper addresses the decomposition of holographic feature vectors in Hyperdimensional Computing (HDC) aka Vector Symbolic Architectures (VSA). HDC uses high-dimensional vectors with brain-like properties to represent symbolic information, and leverages efficient operators to construct and manipulate complexly structured data in a cognitive fashion. Existing models face challenges in de-composing these structures, a process crucial for under-standing and interpreting a composite hypervector. We ad-dress this challenge by proposing the HDC Memorized-Factorization Problem that captures the common patterns of construction in HDC models. To solve this problem efficiently, we introduce HDQMF, a HyperDimensional Quantum Memorized-Factorization algorithm. HDQMF is unique in its approach, utilizing quantum computing to of-fer efficient solutions. It modifies crucial steps in Grover's algorithm to achieve hypervector decomposition, achieving quadratic speed-up.

关键词： computer vision Quantum algorithm Computational modeling Face recognition computer architecture Brain modeling Vectors

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