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共 21008 条记录，以下是1411-1420 订阅

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Oriented RepPoints for Aerial Object Detection

Oriented RepPoints for Aerial Object Detection

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Li, Wentong Chen, Yijie Hu, Kaixuan Zhu, Jianke Zhejiang Univ Hangzhou Peoples R China Univ Elect Sci & Technol China Chengdu Peoples R China Alibaba Zhejiang Univ Joint Res Inst Frontier Technol Hangzhou Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

In contrast to the generic object, aerial targets are often non-axis aligned with arbitrary orientations having the cluttered surroundings. Unlike the mainstreamed approaches regressing the bounding box orientations, this paper proposes an effective adaptive points learning approach to aerial object detection by taking advantage of the adaptive points representation, which is able to capture the geometric information of the arbitrary-oriented instances. To this end, three oriented conversion functions are presented to facilitate the classification and localization with accurate orientation. Moreover, we propose an effective quality assessment and sample assignment scheme for adaptive points learning toward choosing the representative oriented reppoints samples during training, which is able to capture the non-axis aligned features from adjacent objects or background noises. A spatial constraint is introduced to penalize the outlier points for roust adaptive learning. Experimental results on four challenging aerial datasets including DOTA, HRSC2016, UCAS-AOD and DIOR-R, demonstrate the efficacy of our proposed approach. The source code is availabel at: https://github com/LiWentomng/OrientedRepPoints.

关键词： Training Location awareness computer vision Codes Object detection Detectors Feature extraction

来源：评论

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Human Hands as Probes for Interactive Object Understanding

Human Hands as Probes for Interactive Object Understanding

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Goyal, Mohit Modi, Sahil Goyal, Rishabh Gupta, Saurabh Univ Illinois Champaign IL 61820 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Interactive object understanding, or what we can do to objects and how is a long-standing goal of computer vision. In this paper, we tackle this problem through observation of human hands in in-the-wild egocentric videos. We demonstrate that observation of what human hands interact with and how can provide both the relevant data and the necessary supervision. Attending to hands, readily localizes and stabilizes active objects for learning and reveals places where interactions with objects occur. Analyzing the hands shows what we can do to objects and how. We apply these basic principles on the EPIC-KITCHENS dataset, and successfully learn state-sensitive features, and object affordances (regions of interaction and afforded grasps), purely by observing hands in egocentric videos.

关键词： computer vision Affordances pattern recognition Probes Unsupervised learning Videos

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AIM: an Auto-Augmenter for Images and Meshes

AIM: an Auto-Augmenter for Images and Meshes

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Singh, Vinit Veerendraveer Kambhamettu, Chandra Univ Delaware Dept Comp & Informat Sci Video Image Modeling & Synth VIMS Lab Newark DE 19716 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Data augmentations are commonly used to increase the robustness of deep neural networks. In most contemporary research, the networks do not decide the augmentations;they are task-agnostic, and grid search determines their magnitudes. Furthermore, augmentations applicable to lower-dimensional data do not easily extend to higher-dimensional data and vice versa. This paper presents an auto-augmenter for images and meshes (AIM) that easily incorporates into neural networks at training and inference times. It jointly optimizes with the network to produce constrained, non-rigid deformations in the data. AIM predicts sample-aware deformations suited for a task, and our experiments confirm its effectiveness with various networks.

关键词： Deep learning Training Visualization computer vision Image recognition Neural networks Robustness

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SCENIC: A JAX Library for computer vision Research and Beyond

SCENIC: A JAX Library for Computer Vision Research and Beyon...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Dehghani, Mostafa Gritsenko, Alexey Arnab, Anurag Minderer, Matthias Tay, Yi Google Brain Mountain View CA 94043 USA Google Res Mountain View CA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

SCENIC is an open-source' JAX library with a focus on transformer-based models for computer vision research and beyond. The goal of this toolkit is to facilitate rapid experimentation, prototyping, and research of new architectures and models. SCENIC supports a diverse range of tasks (e.g., classification, segmentation, detection) and facilitates working on multi-modal problems, along with GPU/TPU support for large-scale, multi-host and multi-device training. SCENIC also offers optimized implementations of stateof-the-art research models spanning a wide range of modalities. SCENIC has been successfully used for numerous projects and published papers and continues serving as the library of choice for rapid prototyping and publication of new research ideas.

关键词： Training computer vision Computational modeling Machine learning Transformers Rapid prototyping Libraries

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Hyperbolic Image Segmentation

Hyperbolic Image Segmentation

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Atigh, Mina Ghadimi Schoep, Julian Acar, Erman Van Noord, Nanne Mettes, Pascal Univ Amsterdam Amsterdam Netherlands Leiden Univ Leiden Netherlands Vrije Univ Amsterdam Amsterdam Netherlands

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

For image segmentation, the current standard is to perform pixel-level optimization and inference in Euclidean output embedding spaces through linear hyperplanes. In this work, we show that hyperbolic manifolds provide a valuable alternative for image segmentation and propose a tractable formulation of hierarchical pixel-level classification in hyperbolic space. Hyperbolic Image Segmentation opens up new possibilities and practical benefits for segmentation, such as uncertainty estimation and boundary information for free, zero-label generalization, and increased performance in low-dimensional output embeddings.

关键词： Manifolds Image segmentation Uncertainty Semantics Machine learning pattern recognition Task analysis

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ZZ-Net: A Universal Rotation Equivariant Architecture for 2D Point Clouds

ZZ-Net: A Universal Rotation Equivariant Architecture for 2D...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Bokman, Georg Kahla, Fredrik Flinth, Axel Chalmers Univ Technol Dept Elect Engn Gothenburg Sweden Umea Univ Dept Math & Math Stat Umea Sweden

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

In this paper, we are concerned with rotation equivariance on 2D point cloud data. We describe a particular set of functions able to approximate any continuous rotation equivariant and permutation invariant function. Based on this result, we propose a novel neural network architecture for processing 2D point clouds and we prove its universality for approximating functions exhibiting these symmetries. We also show how to extend the architecture to accept a set of 2D-2D correspondences as indata, while maintaining similar equivariance properties. Experiments are presented on the estimation of essential matrices in stereo vision.

关键词： Point cloud compression Deep learning computer vision Neural networks Memory management Estimation computer architecture

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OSKDet: Orientation-sensitive Keypoint Localization for Rotated Object Detection

OSKDet: Orientation-sensitive Keypoint Localization for Rota...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Lu, Dongchen Li, Dongmei Li, Yali Wang, Shengjin Tsinghua Univ Dept Elect Engn Beijing Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Rotated object detection is a challenging issue in computer vision field. Inadequate rotated representation and the confusion of parametric regression have been the bottleneck for high performance rotated detection. In this paper, we propose an orientation-sensitive keypoint based rotated detector OSKDet. First, we adopt a set of keypoints to represent the target and predict the keypoint heatmap on ROI to get the rotated box. By proposing the orientation-sensitive heatmap, OSKDet could learn the shape and direction of rotated target implicitly and has stronger modeling capabilities for rotated representation, which improves the localization accuracy and acquires high quality detection results. Second, we explore a new unordered keypoint representation paradigm, which could avoid the confusion of keypoint regression caused by rule based ordering. Furthermore, we propose a localization quality uncertainty module to better predict the classification score by the distribution uncertainty of keypoints heatmap. Experimental results on several public benchmarks show the state-of-the-art performance of OSKDet. Specifically, we achieve an AP of 80.91% on DOTA, 89.98% on HRSC2016, 97.27% on UCAS-AOD, and a F-measure of 92.18% on ICDAR2015, 81.43% on ICDAR2017, respectively.

关键词： Location awareness Heating systems computer vision Uncertainty Shape Object detection Detectors

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Prompt Distribution Learning

Prompt Distribution Learning

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Lu, Yuning Liu, Jianzhuang Zhang, Yonggang Liu, Yajing Tian, Xinmei Univ Sci & Technol China Hefei Peoples R China Huawei Noahs Ark Lab Shenzhen Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We present prompt distribution learning for effectively adapting a pre-trained vision-language model to address downstream recognition tasks. Our method not only learns low-bias prompts from a few samples but also captures the distribution of diverse prompts to handle the varying visual representations. In this way, we provide high-quality task-related content for facilitating recognition. This prompt distribution learning is realized by an efficient approach that learns the output embeddings of prompts instead of the input embeddings. Thus, we can employ a Gaussian distribution to model them effectively and derive a surrogate loss for efficient training. Extensive experiments on 12 datasets demonstrate that our method consistently and significantly outperforms existing methods. For example, with 1 sample per category, it relatively improves the average result by 9.1% compared to human-crafted prompts.

关键词： Training Visualization computer vision Adaptation models Gaussian distribution pattern recognition Task analysis

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Convolutions for Spatial Interaction Modeling

Convolutions for Spatial Interaction Modeling

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Su, Zhaoen Wang, Chao Bradley, David Vallespi-Gonzalez, Carlos Wellington, Carl Djuric, Nemanja

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

In many different fields interactions between objects play a critical role in determining their behavior. Graph neural networks (GNNs) have emerged as a powerful tool for modeling interactions, although often at the cost of adding considerable complexity and latency. In this paper, we consider the problem of spatial interaction modeling in the context of predicting the motion of actors around autonomous vehicles, and investigate alternatives to GNNs. We revisit 2D convolutions and show that they can demonstrate comparable performance to graph networks in modeling spatial interactions with lower latency, thus providing an effective and efficient alternative in time-critical systems. Moreover, we propose a novel interaction loss to further improve the interaction modeling of the considered methods.

关键词： Tracking Navigation Computational modeling Predictive models Graph neural networks Complexity theory pattern recognition

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Certified Patch Robustness via Smoothed vision Transformers

Certified Patch Robustness via Smoothed Vision Transformers

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Salman, Hadi Jain, Saachi Wong, Eric Madry, Aleksander MIT 77 Massachusetts Ave Cambridge MA 02139 USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Certified patch defenses can guarantee robustness of an image classifier to arbitrary changes within a bounded contiguous region. But, currently, this robustness comes at a cost of degraded standard accuracies and slower inference times. We demonstrate how using vision transformers enables significantly better certified patch robustness that is also more computationally efficient and does not incur a substantial drop in standard accuracy. These improvements stem from the inherent ability of the vision transformer to gracefully handle largely masked images.(1)

关键词： Visualization computer vision Smoothing methods Costs Computational modeling computer architecture Transformers

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