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Distribution-Aware Multi-Label FixMatch for Semi-Supervised Learning on CheXpert

Distribution-Aware Multi-Label FixMatch for Semi-Supervised ...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Sontje Ihler Felix Kuhnke Timo Kuhlgatz Thomas Seel Institute of Mechatronic Systems Leibniz Universität Hannover

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Semi-supervised learning (SSL) has achieved remarkable success for multiclass classification in recent years, yielding a promising solution for medical image classification where labeled data is scarce but unlabeled images are accessible. In the context of multi-label problems however, SSL is still under-explored. In this work we adapt Fix-Match to the multi-label scenario, specifically focusing on CheXpert, a multi-label chest X-ray classification dataset which is imbalanced and only partially labeled. Leveraging distribution alignment, our proposed method, ML-FixMatch+DA, achieves solid performance gains in SSL tasks (AUC: +2.6%) and in a missing label scenario (AUC: +1.9%). In contrast to previous work we achieve a performance gain on CheXpert using FixMatch. We show that in contrast to multiclass FixMatch, where distribution alignment is optional, it is essential for multi-label FixMatch to handle class imbalance and generate reliable (positive and negative) pseudo-labels. Our pseudo-label selection is based on a single threshold for all classes and handles imbalance with no prior knowledge on label distributions. Our adaptation keeps the simplicity of the original multi-class FixMatch with no added hyperparameters (even for imbalanced data) and demonstrates the feasibility of simple SSL for multi-label problems, filling a crucial gap in the literature.

关键词： Semisupervised learning Performance gain Solids Filling Robustness pattern recognition Mirrors

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3D Human Pose Estimation with Occlusions: Introducing BlendMimic3D Dataset and GCN Refinement

3D Human Pose Estimation with Occlusions: Introducing BlendM...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Filipa Lino Carlos Santiago Manuel Marques LARSyS Instituto Superior Técnico Institute for Systems and Robotics Portugal

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In the field of 3D Human Pose Estimation (HPE), accurately estimating human pose, especially in scenarios with occlusions, is a significant challenge. This work identifies and addresses a gap in the current state of the art in 3D HPE concerning the scarcity of data and strategies for handling occlusions. We introduce our novel BlendMimic3D dataset, designed to mimic real-world situations where occlusions occur for seamless integration in 3D HPE algorithms. Additionally, we propose a 3D pose refinement block, employing a Graph Convolutional Network (GCN) to enhance pose representation through a graph model. This GCN block acts as a plug-and-play solution, adaptable to various 3D HPE frameworks without requiring retraining them. By training the GCN with occluded data from Blend-Mimic3D, we demonstrate significant improvements in resolving occluded poses, with comparable results for non-occluded ones. Project web page is available at https://***/BlendMimic3D/.

关键词： Training Solid modeling computer vision Three-dimensional displays Graph convolutional networks conferences Pose estimation

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Multi-Level Feature Fusion Network for Lightweight Stereo Image Super-Resolution

Multi-Level Feature Fusion Network for Lightweight Stereo Im...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Yunxiang Li Wenbin Zou Qiaomu Wei Feng Huang Jing Wu Fuzhou University South China University of Technology Chengdu University of Information Technology

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Stereo image super-resolution utilizes the cross-view complementary information brought by the disparity effect of left and right perspective images to reconstruct higher-quality images. Cascading feature extraction modules and cross-view feature interaction modules to make use of the information from stereo images is the focus of numerous methods. However, this adds a great deal of network parameters and structural redundancy. To facilitate the application of stereo image super-resolution in downstream tasks, we propose an efficient Multi-Level Feature Fusion Network for Lightweight Stereo Image Super-Resolution (MFFSSR). Specifically, MFFSSR utilizes the Hybrid Attention Feature Extraction Block (HAFEB) to extract multi-level intra-view features. Using the channel separation strategy, HAFEB can efficiently interact with the embedded cross-view interaction module. This structural configuration can efficiently mine features inside the view while improving the efficiency of cross-view information sharing. Hence, reconstruct image details and textures more accurately. Abundant experiments demonstrate the effectiveness of MFFSSR. We achieve superior performance with fewer parameters. The source code is available at https://***/KarosLYX/MFFSSR.

关键词： computer vision Source coding conferences Computational modeling Superresolution Redundancy Information sharing

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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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T2FNorm: Train-time Feature Normalization for OOD Detection in Image Classification

T2FNorm: Train-time Feature Normalization for OOD Detection ...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Sudarshan Regmi Bibek Panthi Sakar Dotel Prashnna K Gyawali Danail Stoyanov Binod Bhattarai University College London UK NepAl Applied Mathematics and Informatics Institute for Research Nepal Tribhuvan University Nepal West Virginia University USA University of Aberdeen UK

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Neural networks are notorious for being overconfident predictors, posing a significant challenge to their safe deployment in real-world applications. While feature normalization has garnered considerable attention within the deep learning literature, current train-time regularization methods for Out-of-Distribution(OOD) detection are yet to fully exploit this potential. Indeed, the naive incorporation of feature normalization within neural networks does not guarantee substantial improvement in OOD detection performance. In this work, we introduce T2FNorm, a novel approach to transforming features to hyperspherical space during training, while employing non-transformed space for OOD-scoring purposes. This method yields a surprising enhancement in OOD detection capabilities without compromising model accuracy in in-distribution(ID). Our investigation demonstrates that the proposed technique substantially diminishes the norm of the features of all samples, more so in the case of out-of-distribution samples, thereby addressing the prevalent concern of overconfidence in neural networks. The proposed method also significantly improves various post-hoc OOD detection methods.

关键词： Training Measurement Deep learning computer vision Accuracy conferences Neural networks

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Dr-SAM: An End-to-End Framework for Vascular Segmentation, Diameter Estimation, and Anomaly Detection on Angiography Images.

Dr-SAM: An End-to-End Framework for Vascular Segmentation, D...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Vazgen Zohranyan Vagner Navasardyan Hayk Navasardyan Jan Borggrefe Shant Navasardyan Yerevan State University (YSU) ServiceTitan Inc. Ruhr-Universität Bochum (RUB) Johannes Wesling University Hospital Department of Radiology Neuroradiology and Nuclear Medicine Synopsys Armenia CJSC Picsart AI Research (PAIR)

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Recent advancements in AI have significantly transformed medical imaging, particularly in angiography, by enhancing diagnostic precision and patient care. However existing works are limited in analyzing the aorta and iliac arteries, above all for vascular anomaly detection and characterization. To close this gap, we propose Dr-SAM, a comprehensive multi-stage framework for vessel segmentation, diameter estimation, and anomaly analysis aiming to examine the peripheral vessels through angiography images. For segmentation we introduce a customized positive/negative point selection mechanism applied on top of the Segment Anything Model (SAM), specifically for medical (Angiography) images. Then we propose a morphological approach to determine the vessel diameters followed by our histogram-driven anomaly detection approach. Moreover, we introduce a new benchmark dataset for the comprehensive analysis of peripheral vessel angiography images which we hope can boost the upcoming research in this direction leading to enhanced diagnostic precision and ultimately better health outcomes for individuals facing vascular issues.

关键词： Image segmentation Angiography conferences Estimation Benchmark testing pattern recognition Anomaly detection

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DECNet: A Non-Contacting Dual-Modality Emotion Classification Network for Driver Health Monitoring

DECNet: A Non-Contacting Dual-Modality Emotion Classificatio...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Zhekang Dong Chenhao Hu Shiqi Zhou Liyan Zhu Junfan Wang Yi Chen Xudong Lv Xiaoyue Ji Hangzhou Dianzi University Zhejiang Provincial Key Laboratory of Equipment Electronics Zhejiang University Tsinghua University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Negative emotions have been identified as significant factors influencing driver behavior, easily leading to extremely serious traffic accidents. Hence, there is a pressing need to develop an automatic emotion classification method for driver health monitoring and road safety improvement. Most of the existing methods predominantly focus on single modalities, resulting in suboptimal classification performance due to the underutilization of heterogeneous information. In this work, we propose a novel non-contacting dual-modality driver emotion classification network (DECNet) to address these limitations. DECNet consists of three key modules: 1) facial video modality processing module; 2) driving behavior modality processing module; 3) fusion decision module. Meanwhile, we introduce a combined multi-task learning strategy within DECNet to improve the efficacy in the driver emotion classification task. To evaluate the effectiveness of the proposed DECNet, we conducted experiments on the PPB-Emo dataset, the experimental results showcase the superiority in terms of accuracy (⩾ 6.12% Acc-7) and F1-score (⩾ 7.25% F1-7) compared to existing state-of-the art methods. The model and code will be available at https://***/fqfqngxhs/***

关键词： Smart cities Pressing Feature extraction Multitasking Road safety Safety pattern recognition

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LSDIR: A Large Scale Dataset for Image Restoration

LSDIR: A Large Scale Dataset for Image Restoration

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Yawei Li Kai Zhang Jingyun Liang Jiezhang Cao Ce Liu Rui Gong Yulun Zhang Hao Tang Yun Liu Denis Demandolx Rakesh Ranjan Radu Timofte Luc Van Gool Computer Vision Lab ETH Zürich Meta Reality Labs University of Würzburg KU Leuven

The aim of this paper is to propose a large scale dataset for image restoration (LSDIR). Recent work in image restoration has been focused on the design of deep neural networks. The datasets used to train these networks ‘only’ contain some thousands of images, which is still incomparable with the large scale datasets for other vision tasks such as visual recognition and object detection. The small training set limits the performance of image restoration networks. To solve this problem, we collect high-resolution (HR) images from Flickr for image restoration. To ensure the pixel-level quality of the collected dataset, annotators were invited to manually inspect each of the collected image and remove the low-quality ones. The final dataset contains 84,991 high-quality training images, 1,000 validation images, and 1,000 test images. In addition, we showed that the model capacity of large networks could be fully exploited by training on the large scale dataset with significantly increased patch size and prolonged training iterations. The experimental results on image super-resolution (SR), denoising, JPEG deblocking, deblurring, and demosaicking, and real-world SR show that image restoration networks benefit a lot from the large scale dataset.

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Rip Current Segmentation: A Novel Benchmark and YOLOv8 Baseline Results

Rip Current Segmentation: A Novel Benchmark and YOLOv8 Basel...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Andrei Dumitriu Florin Tatui Florin Miron Radu Tudor Ionescu Radu Timofte Computer Vision Lab CAIDAS & IFI University of Würzburg Germany University of Bucharest Romania

Rip currents are the leading cause of fatal accidents and injuries on many beaches worldwide, emphasizing the importance of automatically detecting these hazardous surface water currents. In this paper, we address a novel task: rip current instance segmentation. We introduce a comprehensive dataset containing 2,466 images with newly created polygonal annotations for instance segmentation, used for training and validation. Additionally, we present a novel dataset comprising 17 drone videos (comprising about 24K frames) captured at 30 FPS, annotated with both polygons for instance segmentation and bounding boxes for object detection, employed for testing purposes. We train various versions of YOLOv8 for instance segmentation on static images and assess their performance on the test dataset (videos). The best results were achieved by the YOLOv8-nano model (runnable on a portable device), with an mAP50 of 88.94% on the validation dataset and 81.21% macro average on the test dataset. The results provide a baseline for future research in rip current segmentation. Our work contributes to the existing literature by introducing a detailed, annotated dataset, and training a deep learning model for instance segmentation of rip currents. The code, training details and the annotated dataset are made publicly available at https://***/Irikos/rip_currents.

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The Third Monocular Depth Estimation Challenge

The Third Monocular Depth Estimation Challenge

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Jaime Spencer Fabio Tosi Matteo Poggi Ripudaman Singh Arora Chris Russell Simon Hadfield Richard Bowden GuangYuan Zhou ZhengXin Li Qiang Rao YiPing Bao Xiao Liu Dohyeong Kim Jinseong Kim Myunghyun Kim Mykola Lavreniuk Rui Li Qing Mao Jiang Wu Yu Zhu Jinqiu Sun Yanning Zhang Suraj Patni Aradhye Agarwal Chetan Arora Pihai Sun Kui Jiang Gang Wu Jian Liu Xianming Liu Junjun Jiang Xidan Zhang Jianing Wei Fangjun Wang Zhiming Tan Jiabao Wang Albert Luginov Muhammad Shahzad Seyed Hosseini Aleksander Trajcevski James H. Elder Independent University of Bologna Blue River Technology Oxford Internet Institute University of Surrey ByteDance University of Chinese Academy of Science RGA Inc. Space Research Institute NASU-SSAU Kyiv Ukraine Northwestern Polytechnical University Indian Institute of Technology Delhi Harbin Institute of Technology Fujitsu GuangXi University University of Reading York University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper discusses the results of the third edition of the Monocular Depth Estimation Challenge (MDEC). The challenge focuses on zero-shot generalization to the challenging SYNS-Patches dataset, featuring complex scenes in natural and indoor settings. As with the previous edition, methods can use any form of supervision, i.e. supervised or self-supervised. The challenge received a total of 19 submissions outperforming the baseline on the test set: 10 among them submitted a report describing their approach, highlighting a diffused use of foundational models such as Depth Anything at the core of their method. The challenge winners drastically improved 3D F-Score performance, from 17.51% to 23.72%.

关键词： computer vision Three-dimensional displays conferences Computational modeling Estimation pattern recognition

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