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Sample-free white-box out-of-distribution detection for deep learning

Sample-free white-box out-of-distribution detection for deep...

引用

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

作者： Begon, Jean-Michel Geurts, Pierre Univ Liege Liege Belgium

ISBN: (纸本)9781665448994

Being able to detect irrelevant test examples with respect to deployed deep learning models is paramount to properly and safely using them. In this paper, we address the problem of rejecting such out-of-distribution (OOD) samples in a fully sample-free way, i.e., without requiring any access to in-distribution or OOD samples. We propose several indicators which can be computed alongside the prediction with little additional cost, assuming white-box access to the network. These indicators prove useful, stable and complementary for OOD detection on frequently-used architectures. We also introduce a surprisingly simple, yet effective summary OOD indicator. This indicator is shown to perform well across several networks and datasets and can furthermore be easily tuned as soon as samples become available. Lastly, we discuss how to exploit this summary in real-world settings.

关键词： Deep learning computer vision Filtering Computational modeling conferences computer architecture Data models

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Deep Image Compression with Latent Optimization and Piece-wise Quantization Approximation

Deep Image Compression with Latent Optimization and Piece-wi...

引用

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

作者： Wu, Yuyang Qi, Zhiyang Zheng, Huiming Tao, Lvfang Gao, Wei Peking Univ Sch Elect & Comp Engn Shenzhen Grad Sch Shenzhen Peoples R China Peng Cheng Lab Shenzhen Peoples R China

ISBN: (纸本)9781665448994

Benefit from its capability of learning high-dimensional compact representation from raw data, the auto-encoders are widely used in various tasks of data compression. In particular, for deep image compression, auto-encoders generally take the responsibility of mapping original images to the latent representation to be coded. In this paper, we propose a new framework for deep image compression by devising a loss function for latent optimization, and adopting the differentiable approximation of quantization. In our experiments, both subjective and objective results can confirm the effectiveness of our contributions.

关键词： computer vision Image coding Quantization (signal) conferences Data compression pattern recognition Task analysis

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Automatic Play Segmentation of Hockey Videos

Automatic Play Segmentation of Hockey Videos

引用

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

作者： Pidaparthy, Hemanth Dowling, Michael H. Elder, James H. York Univ N York ON Canada Queens Univ Kingston ON Canada

ISBN: (纸本)9781665448994

Most team sports such as hockey involve periods of active play interleaved with breaks in play. When watching a game remotely, many fans would prefer an abbreviated game showing only periods of active play. Here we address the problem of identifying these periods in order to produce a time-compressed viewing experience. Our approach is based on a hidden Markov model of play state driven by deep visual and optional auditory cues. We find that our deep visual cues generalize well across different cameras and that auditory cues can improve performance but only if unsupervised methods are used to adapt emission distributions to domain shift across games. Our system achieves temporal compression rates of 20-50% at a recall of 96%.

关键词： Visualization Fans computer vision conferences Hidden Markov models Games Cameras

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Deep Spherical Manifold Gaussian Kernel for Unsupervised Domain Adaptation

Deep Spherical Manifold Gaussian Kernel for Unsupervised Dom...

引用

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

作者： Zhang, Youshan Davison, Brian D. Lehigh Univ Comp Sci & Engn Bethlehem PA 18015 USA

ISBN: (纸本)9781665448994

Unsupervised Domain adaptation is an effective method in addressing the domain shift issue when transferring knowledge from an existing richly labeled domain to a new domain. Existing manifold-based methods either are based on traditional models or largely rely on Grassmannian manifold via minimizing differences of single covariance matrices of two domains. In addition, existing pseudo-labeling algorithms inadequately consider the quality of pseudo labels in aligning the conditional distribution between two domains. In this work, a deep spherical manifold Gaussian kernel (DSGK) framework is proposed to map the source and target subspaces into a spherical manifold and reduce the discrepancy between them by embedding both extracted features and a Gaussian kernel. To align the conditional distributions, we further develop an easy-to-hard pseudo label refinement process to improve the quality of the pseudo labels and then reduce categorical spherical manifold Gaussian kernel geodesic loss. Extensive experimental results show that DSGK outperforms state-of-the-art methods, especially on challenging cross-domain learning tasks.

关键词： Manifolds Adaptation models computer vision conferences Feature extraction pattern recognition Noise measurement

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Dot Distance for Tiny Object Detection in Aerial Images

Dot Distance for Tiny Object Detection in Aerial Images

引用

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

作者： Xu, Chang Wang, Jinwang Yang, Wen Yu, Lei Wuhan Univ Sch Elect Informat Wuhan Peoples R China

ISBN: (纸本)9781665448994

Object detection has achieved great progress with the development of anchor-based and anchor-free detectors. However, the detection of tiny objects is still challenging due to the lack of appearance information. In this paper, we observe that Intersection over Union (IoU), the most widely used metric in object detection, is sensitive to slight offsets between predicted bounding boxes and ground truths when detecting tiny objects. Although some new metrics such as GIoU, DIoU and CIoU are proposed, their performance on tiny object detection is still below the expected level by a large margin. In this paper, we propose a simple but effective new metric called Dot Distance (DotD) for tiny object detection where DotD is defined as normalized Euclidean distance between the center points of two bounding boxes. Extensive experiments on tiny object detection dataset show that anchor-based detectors' performance is highly improved over their baselines with the application of DotD.

关键词： Measurement computer vision conferences Object detection Detectors Euclidean distance pattern recognition

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Symmetric Parallax Attention for Stereo Image Super-Resolution

Symmetric Parallax Attention for Stereo Image Super-Resoluti...

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

作者： Wang, Yingqian Ying, Xinyi Wang, Longguang Yang, Jungang An, Wei Guo, Yulan Natl Univ Def Technol Zunyi Guizhou Peoples R China

ISBN: (纸本)9781665448994

Although recent years have witnessed the great advances in stereo image super-resolution (SR), the beneficial information provided by binocular systems has not been fully used. Since stereo images are highly symmetric under epipolar constraint, in this paper, we improve the performance of stereo image SR by exploiting symmetry cues in stereo image pairs. Specifically, we propose a symmetric bi-directional parallax attention module (biPAM) and an inline occlusion handling scheme to effectively interact cross-view information. Then, we design a Siamese network equipped with a biPAM to super-resolve both sides of views in a highly symmetric manner. Finally, we design several illuminance-robust losses to enhance stereo consistency. Experiments on four public datasets demonstrate the superior performance of our method.

关键词： Training computer vision conferences Superresolution Estimation Bidirectional control Robustness

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Edge Guided Progressively Generative Image Outpainting

Edge Guided Progressively Generative Image Outpainting

引用

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

作者： Lin, Han Pagnucco, Maurice Song, Yang Univ New South Wales Sch Comp Sci & Engn Sydney NSW Australia

ISBN: (纸本)9781665448994

Deep-learning based generative models are proven to be capable for achieving excellent results in numerous image processing tasks with a wide range of applications. One significant improvement of deep-learning approaches compared to traditional approaches is their ability to regenerate semantically coherent images by only relying on an input with limited information. This advantage becomes even more crucial when the input size is only a very minor proportion of the output size. Such image expansion tasks can be more challenging as the missing area may originally contain many semantic features that are critical in judging the quality of an image. In this paper we propose an edge-guided generative network model for producing semantically consistent output from a small image input. Our experiments show the proposed network is able to regenerate high quality images even when some structural features are missing in the input.

关键词： Measurement computer vision Image edge detection Computational modeling Image processing conferences Semantics

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Pano3D: A Holistic Benchmark and a Solid Baseline for 360° Depth Estimation

Pano3D: A Holistic Benchmark and a Solid Baseline for 360° ...

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

作者： Albanis, Georgios Zioulis, Nikolaos Drakoulis, Petros Gkitsas, Vasileios Sterzentsenko, Vladimiros Alvarez, Federico Zarpalas, Dimitrios Daras, Petros Ctr Res & Technol Hellas Thessaloniki Greece Univ Politecn Madrid Madrid Spain

ISBN: (纸本)9781665448994

Pano3D is a new benchmark for depth estimation from spherical panoramas. It aims to assess performance across all depth estimation traits, the primary direct depth estimation performance targeting precision and accuracy, and also the secondary traits, boundary preservation and smoothness. Moreover, Pano3D moves beyond typical intra-dataset evaluation to inter-dataset performance assessment. By disentangling the capacity to generalize in unseen data into different test splits, Pano3D represents a holistic benchmark for 360 degrees depth estimation. We use it as a basis for an extended analysis seeking to offer insights into classical choices for depth estimation. This results into a solid baseline for panoramic depth that followup works can built upon to steer future progress.

关键词： computer vision conferences Estimation Benchmark testing Solids pattern recognition

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CompConv: A Compact Convolution Module for Efficient Feature Learning

CompConv: A Compact Convolution Module for Efficient Feature...

引用

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

作者： Zhang, Chen Xu, Yinghao Shen, Yujun Zhejiang Univ Hangzhou Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781665448994

Convolutional Neural Networks (CNNs) have achieved remarkable success in various computer vision tasks but rely on tremendous computational cost. To solve this problem, existing approaches either compress well-trained large-scale models or learn lightweight models with carefully designed network structures. In this work, we make a close study of the convolution operator, which is the basic unit used in CNNs, to reduce its computing load. In particular, we propose a compact convolution module, called CompConv, to facilitate efficient feature learning. With the divide-and-conquer strategy, CompConv is able to save a great many computations as well as parameters to produce a certain dimensional feature map. Furthermore, CompConv discreetly integrates the input features into the outputs to efficiently inherit the input information. More importantly, the novel CompConv is a plug-and-play module that can be directly applied to modern CNN structures to replace the vanilla convolution layers without further effort. Extensive experimental results suggest that CompConv can adequately compress the benchmark CNN structures yet barely sacrifice the performance, surpassing other competitors.

关键词： computer vision Convolution Computational modeling conferences Benchmark testing pattern recognition Computational efficiency

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AnimalFormer: Multimodal vision Framework for Behavior-based Precision Livestock Farming

AnimalFormer: Multimodal Vision Framework for Behavior-based...

引用

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

作者： Ahmed Qazi Taha Razzaq Asim Iqbal Tibbling Technologies

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

We introduce a multimodal vision framework for precision livestock farming, harnessing the power of GroundingDINO, HQSAM, and ViTPose models. This integrated suite enables comprehensive behavioral analytics from video data without invasive animal tagging. GroundingDINO generates accurate bounding boxes around livestock, while HQSAM segments individual animals within these boxes. ViTPose estimates key body points, facilitating posture and movement analysis. Demonstrated on a sheep dataset with grazing, running, sitting, standing, and walking activities, our framework extracts invaluable insights: activity and grazing patterns, interaction dynamics, and detailed postural evaluations. Applicable across species and video resolutions, this framework revolutionizes noninvasive livestock monitoring for activity detection, counting, health assessments, and posture analyses. It empowers data-driven farm management, optimizing animal welfare and productivity through AI-powered behavioral understanding.

关键词： Productivity Legged locomotion Limiting Tagging Livestock Hardware pattern recognition

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