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Hairy Ground Truth Enhancement for Semantic Segmentation

Hairy Ground Truth Enhancement for Semantic Segmentation

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Sophie Fischer Irina Voiculescu Department of Computer Science Oxford University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Semantic segmentation is a key task within applications of machine learning for medical imaging, requiring large amounts of medical scans annotated by clinicians. The high cost of data annotation means that models need to make the most of all available ground truth masks; yet many models consider two false positive (or false negative) pixel predictions as ‘equally wrong’ regardless of the individual pixels’ relative position to the ground truth mask. These methods also have no sense of whether a pixel is solitary or belongs to a contiguous *** propose the Hairy transform, a novel method to enhance ground truths using 3D ‘hairs’ to represent each pixel’s position relative to objects in the ground truth. We illustrate its effectiveness using a mainstream model and loss function on a commonly used cardiac MRI dataset, as well as a set of synthetic data constructed to highlight the effect of the method during training. The overall improvement in segmentation results comes at the small cost of a one-off pre-processing step, and can easily be integrated into any standard machine learning model. Rather than looking to make minute improvements for mostly correct ‘standard’ masks we instead show how this method helps improve robustness against catastrophic failures for edge cases.

关键词： Training Costs Three-dimensional displays Semantic segmentation Transforms Predictive models Writing

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CUE-Net: Violence Detection Video Analytics with Spatial Cropping, Enhanced UniformerV2 and Modified Efficient Additive Attention

CUE-Net: Violence Detection Video Analytics with Spatial Cro...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Damith Chamalke Senadeera Xiaoyun Yang Dimitrios Kollias Gregory Slabaugh School of Electronic Engineering and Computer Science Queen Mary University of London UK Queen Mary’s Digital Environment Research Institute (DERI) London UK Remark AI UK Limited London UK

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In this paper we introduce CUE-Net, a novel architecture designed for automated violence detection in video surveillance. As surveillance systems become more prevalent due to technological advances and decreasing costs, the challenge of efficiently monitoring vast amounts of video data has intensified. CUE-Net addresses this challenge by combining spatial Cropping with an enhanced version of the UniformerV2 architecture, integrating convolutional and self-attention mechanisms alongside a novel Modified Efficient Additive Attention mechanism (which reduces the quadratic time complexity of self-attention) to effectively and efficiently identify violent activities. This approach aims to overcome traditional challenges such as capturing distant or partially obscured subjects within video frames. By focusing on both local and global spatiotemporal features, CUE-Net achieves state-of-the-art performance on the RWF-2000 and RLVS datasets, surpassing existing methods. The source code is available at 1 .

关键词： Convolutional codes Additives Attention mechanisms Visual analytics Source coding computer architecture Video surveillance

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T2VBench: Benchmarking Temporal Dynamics for Text-to-Video Generation

T2VBench: Benchmarking Temporal Dynamics for Text-to-Video G...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Pengliang Ji Chuyang Xiao Huilin Tai Mingxiao Huo Carnegie Mellon University ShanghaiTech University McGill University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

While text-to-video (T2V) generative models produce exceptionally realistic videos, they lack a comprehensive evaluation across the temporal dimension, with a limited focus on basic dynamics including camera transitions, movement, and event sequences. In this work, we introduce T2VBench, a comprehensive T2V evaluation benchmark enriched with temporal dynamics lexicons derived from curated temporal words on Wikipedia. T2VBench is a hierarchical evaluation framework comprising over 1,600 temporally rich prompts and 5,000 generated videos with human ratings, spanning 16 critical temporal evaluation dimensions. We assess three leading text-to-video models, including ZeroScope and Pika, to gauge their proficiency in handling temporal dynamics. Our analysis highlights the strengths and limitations of these models across various temporal aspects. Furthermore, we provide insights into future directions for enhancing text-to-video evaluation metrics and offer a detailed analysis of these models’ performance across the temporal dimensions. Overall, T2VBench is the first-of-its-kind comprehensive benchmark fully focused on temporal dynamics for text-to-video evaluation. It aims to facilitate scientific benchmarking of both generative models and automated metrics on text-to-video generation.

关键词： Measurement Analytical models Computational modeling conferences Encyclopedias Benchmark testing pattern recognition

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HSiPu² - A New Human Physical Fitness Action Dataset for recognition and 3D Reconstruction Evaluation

HSiPu<SUP>2</SUP> - A New Human Physical Fitness Action Data...

引用

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

作者： Zhang, Chuanlei Liu, Lixin Yao, Minda Chen, Wei Chen, Dufeng Wu, Yuliang Tianjin Univ Sci & Technol Tianjin Peoples R China China Univ Min & Technol Beijing Beijing Peoples R China China Univ Min & Technol Xuzhou Jiangsu Peoples R China Beijing Geotech & Invest Engn Inst Beijing Peoples R China Sichuan Staff Univ Sci & Technol Chengdu Peoples R China

ISBN: (纸本)9781665448994

In this paper a human physical fitness action feature dataset named HSiPu2 is introduced, which contains 8,044 action data sequences and 80,440 images. The dataset is built for three physical fitness actions, which are situp, push-up and pull-up, and each action data has two categories corresponding to standard and non-standard actions. Two cameras work as sensors to capture features from different views. HSiPu2 data set facilitates the evaluation of the performance of machine learning algorithms used in recognition and evaluation problems related to human behaviours recognition. The dataset is freely and publicly available online. A new recognition method based on deep learning techniques, including the two-branch multi-stage convolutional neural networks (CNNs) and long short-term memory (LSTM) networks with attention, are employed to learn the long-term dependencies from videos for human physical fitness action recognition on the HSiPu2. For comparison purpose, traditional machine learning models, including Decision Tree, Random Forest, SVM, Bagging, GBDT, AdaBoost, XGBoost and Voting, are utilized for the action recolonization. Furhtermore, HSiPu2 also can serve as a dataset to evaluating a human action 3D model.

关键词： Support vector machines Solid modeling Three-dimensional displays Sensor phenomena and characterization pattern recognition Convolutional neural networks Proposals

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Enhancing the Transferability of Adversarial Attacks with Stealth Preservation

Enhancing the Transferability of Adversarial Attacks with St...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Xinwei Zhang Tianyuan Zhang Yitong Zhang Shuangcheng Liu School of Computer Science and Engineering Beihang University Beijing China State Key Lab of Software Development Environment Beihang University Beijing China Shen Yuan Honors College Beihang University Beijing China

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Deep neural networks are susceptible to attacks from adversarial examples in recent years. Especially, the black-box attacks cause a more serious threat to practical applications. However, while most existing black-box attacks have achieved a high success rate in deceiving models, they have not focused on the stealthiness of adversarial examples, often exhibiting suspicious visual appearances. To address this issue, this paper proposes the Mask Momentum Iterative Attack (MMIA), which introduces a masking mechanism and adopts an optimal perturbation strategy to identify regions of an image most vulnerable to attacks. This approach effectively ensures the transferability and stealthiness of adversarial examples. Simultaneously, by integrating image enhancement techniques and temporal and spatial momentum terms into the iterative process of the attack, we prevent the attack from getting stuck in local optima, further improving the transferability of adversarial examples. To enhance the success rate of black-box attacks, we apply MMIA to a model ensemble using a joint optimization strategy. We demonstrate that adversarially trained models with a strong defense ability are also susceptible to our black-box attacks. We conduct extensive experiments on classification tasks using common vision models, and our results significantly demonstrate the superiority of our method over state-of-the-art approaches when considering both transferability and stealthiness.

关键词： Visualization Fuses Computational modeling Perturbation methods Closed box pattern recognition Iterative methods

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Dataset condensation with latent quantile matching

Dataset condensation with latent quantile matching

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Wei Wei Tom De Schepper Kevin Mets Department of Computer Science IDLab University of Antwerp - imec Antwerp Belgium IDLab Faculty of Applied Engineering University of Antwerp - imec Antwerp Belgium

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Dataset condensation (DC) methods aim to learn a smaller, synthesized dataset with informative data records to accelerate the training of machine learning models. Current distribution matching (DM) based DC methods learn a synthesized dataset by matching the mean of the latent embeddings between the synthetic and the real dataset. However, two distributions with the same mean can still be vastly different. In this work, we demonstrate the shortcomings of using Maximum Mean Discrepancy to match latent distributions, i.e., the weak matching power and lack of outlier regularization. To alleviate these shortcomings, we propose our new method: Latent Quantile Matching (LQM), which matches the quantiles of the latent embeddings to minimize the goodness of fit test statistic between two distributions. Empirical experiments on both image and graph-structured datasets show that LQM matches or outperforms previous state of the art in distribution matching based DC. Moreover, we show that LQM improves the performance in continual graph learning (CGL) setting, where memory efficiency and privacy can be important. Our work sheds light on the application of DM based DC for CGL.

关键词： Training Measurement Data privacy conferences Memory management Machine learning Current distribution

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Zero-Shot Monocular Motion Segmentation in the Wild by Combining Deep Learning with Geometric Motion Model Fusion

Zero-Shot Monocular Motion Segmentation in the Wild by Combi...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Yuxiang Huang Yuhao Chen John Zelek Vision and Image Processing Lab University of Waterloo Waterloo Canada

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Detecting and segmenting moving objects from a moving monocular camera is challenging in the presence of unknown camera motion, diverse object motions and complex scene structures. Most existing methods rely on a single motion cue to perform motion segmentation, which is usually insufficient when facing different complex environments. While a few recent deep learning based methods are able to combine multiple motion cues to achieve improved accuracy, they depend heavily on vast datasets and extensive annotations, making them less adaptable to new scenarios. To address these limitations, we propose a novel monocular dense segmentation method that achieves state-of-the-art motion segmentation results in a zero-shot manner. The proposed method synergestically combines the strengths of deep learning and geometric model fusion methods by performing geometric model fusion on object proposals. Experiments show that our method achieves competitive results on several motion segmentation datasets and even surpasses some state-of-the-art supervised methods on certain benchmarks, while not being trained on any data. We also present an ablation study to show the effectiveness of combining different geometric models together for motion segmentation, highlighting the value of our geometric model fusion strategy.

关键词： Deep learning computer vision Motion segmentation conferences Computational modeling Geometric modeling Benchmark testing

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H3Net: Irregular Posture Detection by Understanding Human Character and Core Structures

H3Net: Irregular Posture Detection by Understanding Human Ch...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Seungha Noh Kangmin Bae Yuseok Bae Byong-Dai Lee Kyonggi University Suwon South Korea ETRI Daejeon South Korea

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper proposes H 3 Net that considers detecting people in irregular postures by utilizing human structures and characters. To handle both features, we introduce two attention modules: 1) Human Structure Attention Module (HSAM), which is introduced to focus on the spatial aspects of a person, and 2) Human Character Attention Module (HCAM), which is designed to address the issue of repetitive appearance. HSAM effectively handles both foreground and background information about a human instance and utilizes keypoints to provide additional guidance to predict irregular postures. Meanwhile, HCAM employs ID information obtained from the tracking head, enriching the posture prediction with high-level semantic information. Furthermore, gathering images of people in irregular postures is a challenging task. Therefore, many conventional datasets consist of images with the same actors simulating varying postures in distinct images. To address this problem, we propose a Human ID Dependent Posture (HID 2 ) loss that handles repeated instances. The HID 2 loss generates a regularization term by considering duplicated instances to reduce bias. Our experiments demonstrate the effectiveness of H 3 Net compared to existing algorithms on irregular posture datasets. Furthermore, we show the qualitative results using color-coded masks and bounding boxes. We also provide ablation studies to highlight the significance of our proposed methods.

关键词： computer vision Head conferences Semantics pattern recognition

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TAB: Text-Align Anomaly Backbone Model for Industrial Inspection Tasks

TAB: Text-Align Anomaly Backbone Model for Industrial Inspec...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Ho-Weng Lee Shang-Hong Lai Department of Computer Science National Tsing Hua University Taiwan

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In recent years, the focus on anomaly detection and localization in industrial inspection tasks has intensified. While existing studies have demonstrated impressive outcomes, they often rely heavily on extensive training datasets or robust features extracted from pre-trained models trained on diverse datasets like ImageNet. In this work, we propose a novel framework leveraging the visual-linguistic CLIP model to adeptly train a backbone model tailored to the manufacturing domain. Our approach concurrently considers visual and text-aligned embedding spaces for normal and abnormal conditions. The resulting pre-trained backbone markedly enhances performance in industrial downstream tasks, particularly in anomaly detection and localization. This improvement is substantiated through experiments conducted on multiple datasets such as MVTecAD, BTAD, and KSDD2. Furthermore, using our pre-trained backbone weights allows previous works to achieve superior performance in few-shot scenarios with less training data. The proposed anomaly backbone provides a foundation model for more precise anomaly detection and localization. The source code and pre-trained weights are available at https://***/Howeng98/TAB.

关键词： Location awareness Training Visualization Source coding Training data Inspection Feature extraction

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Medium Scale Benchmark for Cricket Excited Actions Understanding

Medium Scale Benchmark for Cricket Excited Actions Understan...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Altaf Hussain Noman Khan Muhammad Munsif Min Je Kim Sung Wook Baik Sejong University Republic of Korea

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The Sports Action recognition (SAR) domain is of significant importance in research, with diverse applications, ranging from aiding coaches in strategic decision-making to empowering athletes and contributing to real-time commercial entertainment. Despite the existence of extensive large-scale and small-scale datasets, the direct application of these datasets to specific sports domains, such as cricket, poses challenges. Existing datasets predominantly center around daily life actions, lacking the necessary granularity for in-depth sports analyses. Current Cricket Action Analysis (CAA) datasets have limitations, including their small scale, modality constraints, and their narrow focus on specific aspects, such as cricket batting. Recognizing the need for a more comprehensive benchmark, this article introduces the Cricket Excited Actions (CEA) dataset. Developed in collaboration with professional cricket players, the CEA dataset encompasses challenging multi-person actions within realistic cricket scenarios. The selected activity classes, such as Clean Bowled, Six, Four, and Catches, adhere to official standards and represent pivotal moments in cricket matches. Through precise annotation and empirical studies, utilizing state-of-the-art action recognition model architectures, this study provides a valuable resource for further research and makes significant contributions by offering support essential to advancing CAA within the cricket sports community. The data and code are available at https://***/Altaf-hucn/Cricket-Excited-Actions-Benchmark.

关键词： Computational modeling Refining Collaboration computer architecture Benchmark testing Real-time systems pattern recognition

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