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Learning Non-Metric Partial Similarity Based on Maximal Margin Criterion

Learning Non-Metric Partial Similarity Based on Maximal Marg...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Xiaoyang Tan Songcan Chen Jun Li Zhi-Hua Zhou Department of Computer Science and Engineering Nanjing University of Aeronautics & Astronautics Nanjing China National Laboratory for Novel Software Technology Nanjing University Nanjing China

ISBN: (纸本)0769525970

The performance of many computer vision and machine learning algorithms critically depends on the quality of the similarity measure defined over the feature space. Previous works usually utilize metric distances which are ofen epistemologically different from the perceptual distance of human beings. In this paper a novel non-metric partial similarity measure is introduced, which is born to automatically capture the prominent partial similarity between two images while ignoring the confusing unimportant dissimilarity. This measure is potentially useful in face recognition since it can help identify the inherent intra-personal similarity and thus reducing the influence caused by large variations such as expression and occlusions. Moreover; to make this method practical, this paper proposes an automatic and class-dependent similarity threshold setting mechanism based on the maximal margin criterion, and uses a Self- Organization Map-based embedding technique to alleviate the computational problem. Experimental results show the feasibility and effectiveness of the proposed method.

关键词： Extraterrestrial measurements Clustering algorithms computer vision Humans pattern recognition Image matching Space technology Image databases Euclidean distance Robustness

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A Verification Method for Viewpoint Invariant Sign Language recognition

A Verification Method for Viewpoint Invariant Sign Language ...

引用

International conference on pattern recognition

作者： Qi Wang Xilin Chen Chunli Wang Wen Gao School of Computer Science and Technology Harbin Institute of Technology China Institute of Computing Technology CAS China

Viewpoint variance is one of the inevitable problems in vision based sign language recognition. However, most researchers avoid this problem by assuming a special view, especially the front view. In the paper, we propose a verification method for viewpoint invariant sign language recognition. In general, there are two major variances between two video sequences of the same sign: performance variance and viewpoint variance. For small performance variance, DTW can help us eliminate it. When there is only viewpoint variance between two sequences, we can consider the two sequences as obtained synchronously by a stereo vision system. Thus, for the current input, we can judge whether the known template is the matched one by verifying whether the two sequences can be considered as obtained by a stereo vision system. Our experiments demonstrate the efficiency of the proposed method. Furthermore, such verification method can be easily extended to other recognition tasks

关键词： Handicapped aids Stereo vision pattern recognition Video sequences computer science Deafness Content addressable storage Humans Hidden Markov models Feature extraction

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Leveraging Class Hierarchy in Fashion Classification

Leveraging Class Hierarchy in Fashion Classification

引用

International conference on computer vision workshops (ICCV workshops)

作者： Hyunsoo Cho Chaemin Ahn Kang Min Yoo Jinseok Seol Sang-goo Lee Seoul National University Seoul South Korea

ISBN: (数字)9781728150239

ISBN: (纸本)9781728150246

The online commerce market has been growing rapidly, spurring interest in the deep fashion domain from the research community. Among various tasks in the fashion domain, the classification problem is the vital one, because metadata extraction through fashion classification has tremendous industrial value. A flurry of recent deep-learning based models have been proposed for the task and have showed great performances but they fail to capture the hierarchical nature of fashion annotations, such as 'pant' and 'skirt' both having 'bottom' as the superordinate. In this preliminary work, we propose a novel fashion classification model that works in a hierarchical manner. Experimental results on large fashion datasets show that our intuition, taking into account hierarchical dependencies between class labels, can help improve performance.

关键词： Task analysis computer vision conferences Clothing pattern recognition Training

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Improving the Robustness of 3D Human Pose Estimation: A Benchmark Dataset and Learning from Noisy Input

Improving the Robustness of 3D Human Pose Estimation: A Benc...

引用

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

作者： Trung-Hieu Hoang Mona Zehni Huy Phan Duc Minh Vo Minh N. Do University of Illinois at Urbana-Champaign USA VinUniversity Ha Noi Vietnam The University of Tokyo Japan

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Despite the promising performance of current 3D human pose estimation techniques, understanding and enhancing their robustness on challenging in-the-wild videos remain an open problem. In this work, we focus on building robust 2D-to-3D pose lifters. To this end, we develop two benchmark datasets, namely Human3.6M-C and HumanEva-I-C, to examine the resilience of video-based 3D pose lifters to a wide range of common video corruptions including temporary occlusion, motion blur, and pixel-level noise. We demonstrate the poor generalization of state-of-the-art 3D pose lifters in the presence of corruption and establish two techniques to tackle this issue. First, we introduce Temporal Additive Gaussian Noise (TAGN) as a simple yet effective 2D input pose data augmentation. Additionally, to incorporate the confidence scores output by the 2D pose detectors, we design a confidence-aware convolution (CA-Conv) block. Extensively tested on corrupted videos, the proposed strategies consistently boost the robustness of 3D pose lifters and serve as new baselines for future research.

关键词： Solid modeling Three-dimensional displays Convolution Pose estimation Benchmark testing Robustness pattern recognition

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Systematic Evaluation of Backdoor Data Poisoning Attacks on Image Classifiers

Systematic Evaluation of Backdoor Data Poisoning Attacks on ...

引用

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

作者： Loc Truong Chace Jones Brian Hutchinson Andrew August Brenda Praggastis Robert Jasper Nicole Nichols Aaron Tuor Western Washington University Pacific Northwest National Laboratory

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Backdoor data poisoning attacks have recently been demonstrated in computer vision research as a potential safety risk for machine learning (ML) systems. Traditional data poisoning attacks manipulate training data to induce unreliability of an ML model, whereas backdoor data poisoning attacks maintain system performance unless the ML model is presented with an input containing an embedded "trigger " that provides a predetermined response advantageous to the adversary. Our work builds upon prior backdoor data-poisoning research for ML image classifiers and systematically assesses different experimental conditions including types of trigger patterns, persistence of trigger patterns during retraining, poisoning strategies, architectures (ResNet-50, NasNet, NasNet-Mobile), datasets (Flowers, CIFAR-10), and potential defensive regularization techniques (Contrastive Loss, Logit Squeezing, Manifold Mixup, Soft-Nearest-Neighbors Loss). Experiments yield four key findings. First, the success rate of backdoor poisoning attacks varies widely, depending on several factors, including model architecture, trigger pattern and regularization technique. Second, we find that poisoned models are hard to detect through performance inspection alone. Third, regularization typically reduces backdoor success rate, although it can have no effect or even slightly increase it, depending on the form of regularization. Finally, backdoors inserted through data poisoning can be rendered ineffective after just a few epochs of additional training on a small set of clean data without affecting the model's performance.

关键词： Data models Training Computational modeling Machine learning Training data computer vision Safety

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PitcherNet: Powering the Moneyball Evolution in Baseball Video Analytics

PitcherNet: Powering the Moneyball Evolution in Baseball Vid...

引用

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

作者： Jerrin Bright Bavesh Balaji Yuhao Chen David A Clausi John S Zelek University of Waterloo Waterloo Canada

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In the high-stakes world of baseball, every nuance of a pitcher’s mechanics holds the key to maximizing performance and minimizing runs. Traditional analysis methods often rely on pre-recorded offline numerical data, hindering their application in the dynamic environment of live games. Broadcast video analysis, while seemingly ideal, faces significant challenges due to factors like motion blur and low resolution. To address these challenges, we introduce PitcherNet, an end-to-end automated system that analyzes pitcher kinematics directly from live broadcast video, thereby extracting valuable pitch statistics including velocity, release point, pitch position, and release extension. This system leverages three key components: (1) Player tracking and identification by decoupling actions from player kinematics; (2) Distribution and depth-aware 3D human modeling; and (3) Kinematic-driven pitch statistics. Experimental validation demonstrates that PitcherNet achieves robust analysis results with 96.82% accuracy in pitcher tracklet identification, reduced joint position error by 1.8mm and superior analytics compared to baseline methods. By enabling performance-critical kinematic analysis from broadcast video, PitcherNet paves the way for the future of baseball analytics by optimizing pitching strategies, preventing injuries, and unlocking a deeper understanding of pitcher mechanics, forever transforming the game.

关键词： Solid modeling Three-dimensional displays Tracking Visual analytics Kinematics Games pattern recognition

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A computationally efficient approach to 3D ear recognition employing local and holistic features

A computationally efficient approach to 3D ear recognition e...

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

作者： Jindan Zhou Steven Cadavid Mohamed Abdel-Mottaleb Department of Electrical and Computer Engineering University of Miami Coral Gables FL USA

We present a complete, Three-Dimensional (3D) object recognition system combining local and holistic features in a computationally efficient manner. An evaluation of the proposed system is conducted on a 3D ear recognition task. The ear provides a challenging case study because of its high degree of inter-subject similarity. In this work, we focus primarily on the local and holistic feature extraction and matching components, as well as the fusion framework used to combine these features at the match score level. Experimental results conducted on the University of Notre Dame (UND) collection G dataset, containing range images of 415 subjects, yielded a rank-one recognition rate of 98.6% and an equal error rate of 1.6%. These results demonstrate that the proposed system outperforms state-of-the-art 3D ear biometric systems.

关键词： Shape Ear Three dimensional displays Feature extraction Indexes Probes Face

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A Method of Moments Embedding Constraint and its Application to Semi-Supervised Learning

A Method of Moments Embedding Constraint and its Application...

引用

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

作者： Michael Majurski Sumeet Menon Parniyan Farvardin David Chapman Information Technology Lab NIST University of Maryland University of Maryland University of Miami

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Discriminative deep learning models with a linear+softmax final layer have a problem: the latent space only predicts the conditional probabilities p(Y |X) but not the full joint distribution p(Y,X), which necessitates a generative approach. The conditional probability cannot detect outliers, causing outlier sensitivity in softmax networks. This exacerbates model over-confidence impacting many problems, such as hallucinations, confounding biases, and dependence on large datasets. To address this we introduce a novel embedding constraint based on the Method of Moments (MoM). We investigate the use of polynomial moments ranging from 1st through 4th order hyper-covariance matrices. Furthermore, we use this embedding constraint to train an Axis-Aligned Gaussian Mixture Model (AAGMM) final layer, which learns not only the conditional, but also the joint distribution of the latent space. We apply this method to the domain of semi-supervised image classification by extending FlexMatch with our technique. We find our MoM constraint with the AAGMM layer is able to match the reported FlexMatch accuracy, while also modeling the joint distribution, thereby reducing outlier sensitivity. We also present a preliminary outlier detection strategy based on Mahalanobis distance and discuss future improvements to this strategy. Code is available at: https://***/mmajurski/ssl-gmm

关键词： Deep learning Sensitivity Semisupervised learning Predictive models Polynomials Distance measurement pattern recognition

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Robust Visual Tracking via Collaborative and Reinforced Convolutional Feature Learning

Robust Visual Tracking via Collaborative and Reinforced Conv...

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

作者： Dongdong Li Yangliu Kuai Gongjian Wen Li Liu College of Electronic Science and Technology National University of Defense Technology China College of System Engineering National University of Defense Technology China Center for Machine Vision and Signal Analysis University of Oulu Finland

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

Convolutional neural networks are potent models that yield hierarchies of features and have drawn increasing interest in the visual tracking field. In the paper, we design an end-to-end trainable tracking framework based on Siamese network, which proposes to learn the low-level fine-grained and high-level semantic representations simultaneously with the aim of mutual benefit. Due to the distinct and complementary characteristics of the feature hierarchies, different tracking mechanisms are adopted for different feature layers. The low-level features are exploited and updated with a correlation filter layer for adaptive tracking and the high-level features are compared through cross-correlation directly for robust tracking. The two-level features are jointly trained with a multi-task loss function end-to-end. The proposed tracker takes full advantage of the adaptability of the low-level features and the generalization ability of the high-level features. Extensive experimental tracking results on the widely used OTB and TC128 benchmarks demonstrate the superiority of our tracker. Meanwhile, our proposed tracker can achieve a real-time tracking speed.

关键词： Target tracking Correlation Semantics Feature extraction Robustness Visualization Real-time systems

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A Multimodal Approach Integrating Convolutional and Recurrent Neural Networks for Alzheimer’s Disease Temporal Progression Prediction

A Multimodal Approach Integrating Convolutional and Recurren...

引用

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

作者： Durga Supriya HL Swetha Mary Thomas Sowmya Kamath S National Institute of Technology Karnataka India Mangalore Karnataka India Dept. of Information Technology Healthcare Analytics and Language Engineering (HALE) Lab National Institute of Technology Karnataka Mangaluru India

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Alzheimer’s Disease (AD) poses a substantial healthcare challenge marked by cognitive decline and a lack of definitive treatments. As the global population ages, the prevalence of AD escalates, underscoring the need for more advanced diagnostic techniques. Current single-modality methods have limitations, emphasizing the critical need for early detection and precise diagnosis to facilitate timely interventions and the development of effective therapies. We propose a novel multimodal medical diagnostic framework for AD employing a hybrid deep learning model. This framework integrates a 3D Convolutional Neural Network (CNN) to extract detailed intra-slice features from MRI volumes and a Long Short-Term Memory (LSTM) network to capture intricate inter-sequence patterns indicative of AD progression. By leveraging longitudinal MRI data alongside spatial, temporal, biomarkers, and cognitive scores, our framework significantly enhances diagnostic accuracy, particularly in the early stages of the disease. We incorporate Grad-CAM to enhance the interpretability of MRI-based diagnoses by highlighting relevant brain regions. This multimodal approach achieves a promising accuracy of 92.65%, outperforming state-of-the-art works by a margin of 6%.

关键词： Accuracy Three-dimensional displays Recurrent neural networks Magnetic resonance imaging Feature extraction Spatial databases pattern recognition

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