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检索条件"任意字段=2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023"

共 11753 条记录，以下是4341-4350 订阅

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Audio-Visual Instance Discrimination with Cross-Modal Agreement

Audio-Visual Instance Discrimination with Cross-Modal Agreem...

引用

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

作者： Morgado, Pedro Vasconcelos, Nuno Misra, Ishan Univ Calif San Diego La Jolla CA 92093 USA Facebook AI Res New York NY USA

ISBN: (纸本)9781665445092

We present a self-supervised learning approach to learn audio-visual representations from video and audio. Our method uses contrastive learning for cross-modal discrimination of video from audio and vice-versa. We show that optimizing for cross-modal discrimination, rather than withinmodal discrimination, is important to learn good representations from video and audio. With this simple but powerful insight, our method achieves highly competitive performance when finetuned on action recognition tasks. Furthermore, while recent work in contrastive learning defines positive and negative samples as individual instances, we generalize this definition by exploring cross-modal agreement. We group together multiple instances as positives by measuring their similarity in both the video and audio feature spaces. Cross-modal agreement creates better positive and negative sets, which allows us to calibrate visual similarities by seeking within-modal discrimination of positive instances, and achieve significant gains on downstream tasks.

关键词： Visualization computer vision Extraterrestrial measurements pattern recognition Task analysis

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Deep Fair Clustering via Maximizing and Minimizing Mutual Information: Theory, Algorithm and Metric

Deep Fair Clustering via Maximizing and Minimizing Mutual In...

引用

conference on computer vision and pattern recognition (cvpr)

作者： Pengxin Zeng Yunfan Li Peng Hu Dezhong Peng Jiancheng Lv Xi Peng College of Computer Science Sichuan University China

Fair clustering aims to divide data into distinct clusters while preventing sensitive attributes (e.g., gender, race, RNA sequencing technique) from dominating the clustering. Although a number of works have been conducted and achieved huge success recently, most of them are heuristical, and there lacks a unified theory for algorithm design. In this work, we fill this blank by developing a mutual information theory for deep fair clustering and accordingly designing a novel algorithm, dubbed FCMI. In brief, through maximizing and minimizing mutual information, FCMI is designed to achieve four characteristics highly expected by deep fair clustering, i.e., compact, balanced, and fair clusters, as well as informative features. Besides the contributions to theory and algorithm, another contribution of this work is proposing a novel fair clustering metric built upon information theory as well. Unlike existing evaluation metrics, our metric measures the clustering quality and fairness as a whole instead of separate manner. To verify the effectiveness of the proposed FCMI, we conduct experiments on six benchmarks including a single-cell RNA-seq atlas compared with 11 state-of-the-art methods in terms of five metrics. The code could be accessed from https://***.

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Mirror3D: Depth Refinement for Mirror Surfaces

Mirror3D: Depth Refinement for Mirror Surfaces

引用

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

作者： Tan, Jiaqi Lin, Weijie Chang, Angel X. Savva, Manolis Simon Fraser Univ Burnaby BC Canada

ISBN: (纸本)9781665445092

Despite recent progress in depth sensing and 3D reconstruction, mirror surfaces are a significant source of errors. To address this problem, we create the Mirror3D dataset: a 3D mirror plane dataset based on three RGBD datasets (Matterpot3D, NYUv2 and ScanNet) containing 7,011 mirror instance masks and 3D planes. We then develop Mirror3DNet: a module that refines raw sensor depth or estimated depth to correct errors on mirror surfaces. Our key idea is to estimate the 3D mirror plane based on RGB input and surrounding depth context, and use this estimate to directly regress mirror surface depth. Our experiments show that Mirror3DNet significantly mitigates errors from a variety of input depth data, including raw sensor depth and depth estimation or completion methods.

关键词： Surface reconstruction computer vision Three-dimensional displays Estimation computer architecture Prediction methods Sensors

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Repetitive Activity Counting by Sight and Sound

Repetitive Activity Counting by Sight and Sound

引用

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

作者： Zhang, Yunhua Shao, Ling Snoek, Cees G. M. Univ Amsterdam Amsterdam Netherlands Incept Inst Artificial Intelligence Abu Dhabi U Arab Emirates

ISBN: (纸本)9781665445092

This paper strives for repetitive activity counting in videos. Different from existing works, which all analyze the visual video content only, we incorporate for the first time the corresponding sound into the repetition counting process. This benefits accuracy in challenging vision conditions such as occlusion, dramatic camera view changes, low resolution, etc. We propose a model that starts with analyzing the sight and sound streams separately. Then an audiovisual temporal stride decision module and a reliability estimation module are introduced to exploit cross-modal temporal interaction. For learning and evaluation, an existing dataset is repurposed and reorganized to allow for repetition counting with sight and sound. We also introduce a variant of this dataset for repetition counting under challenging vision conditions. Experiments demonstrate the benefit of sound, as well as the other introduced modules, for repetition counting. Our sight-only model already outperforms the state-of-the-art by itself, when we add sound, results improve notably, especially under harsh vision conditions. The code and datasets are available at https://***/xiaobai1217/RepetitionCounting.

关键词： Visualization computer vision Analytical models Codes Estimation Cameras pattern recognition

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Harmonious Semantic Line Detection via Maximal Weight Clique Selection

Harmonious Semantic Line Detection via Maximal Weight Clique...

引用

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

作者： Jin, Dongkwon Park, Wonhui Jeong, Seong-Gyun Kim, Chang-Su Korea Univ Seoul South Korea 42Dot Ai Seoul South Korea

ISBN: (纸本)9781665445092

A novel algorithm to detect an optimal set of semantic lines is proposed in this work. We develop two networks: selection network (S-Net) and harmonization network (H-Net). First, S-Net computes the probabilities and offsets of line candidates. Second, we filter out irrelevant lines through a selection-and-removal process. Third, we construct a complete graph, whose edge weights are computed by H-Net. Finally, we determine a maximal weight clique representing an optimal set of semantic lines. Moreover, to assess the overall harmony of detected lines, we propose a novel metric, called HIoU. Experimental results demonstrate that the proposed algorithm can detect harmonious semantic lines effectively and efficiently. Our codes are available at https://***/dongkwonjin/Semantic-Line-MWCS.

关键词： Measurement computer vision Codes Image edge detection Semantics Detectors Filtering algorithms

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Controlling the Rain: from Removal to Rendering

Controlling the Rain: from Removal to Rendering

引用

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

作者： Ni, Siqi Cao, Xueyun Yue, Tao Hu, Xuemei Nanjing Univ Sch Elect Sci & Engn Nanjing Peoples R China

ISBN: (纸本)9781665445092

Existing rain image editing methods focus on either removing rain from rain images or rendering rain on rain-free images. This paper proposes to realize continuous control of rain intensity bidirectionally, from clear rain-free to downpour image with a single rain image as input, without changing the scene-specific characteristics, e.g. the direction, appearance and distribution of rain. Specifically, we introduce a Rain Intensity Controlling Network (RICNet) that contains three sub-networks of background extraction network, high-frequency rain-streak elimination network and main controlling network, which allows to control rain image of different intensities continuously by interpolation in the deep feature space. The HOG loss and autocorrelation loss are proposed to enhance consistency in orientation and suppress repetitive rain streaks. Furthermore, a decremental learning strategy that trains the network from downpour to drizzle images sequentially is proposed to further improve the performance and speedup the convergence. Extensive experiments on both rain dataset and real rain images demonstrate the effectiveness of the proposed method.

关键词： Interpolation computer vision Rain Aerospace electronics Rendering (computer graphics) Feature extraction pattern recognition

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CLCC: Contrastive Learning for Color Constancy

CLCC: Contrastive Learning for Color Constancy

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

作者： Yi-Chen Lo Chia-Che Chang Hsuan-Chao Chiu Yu-Hao Huang Chia-Ping Chen Yu-Lin Chang Jou, Kevin MediaTek Inc Hsinchu Taiwan

ISBN: (纸本)9781665445092

In this paper;we present CLCC, a novel contrastive learning framework for color constancy. Contrastive learning has been applied for learning high-quality visual representations for image classification. One key aspect to yield useful representations for image classification is to design illuminant invariant augmentations. However, the illuminant invariant assumption conflicts with the nature of the color constancy task, which aims to estimate the illuminant given a raw image. Therefore, we construct effective contrastive pairs for learning better illuminant-dependent features via a novel raw-domain color augmentation. On the NUS-8 dataset, our method provides 17.5% relative improvements over a strong baseline, reaching state-of-the-art performance without increasing model complexity. Furthermore, our method achieves competitive performance on the Gehler dataset with 3x fewer parameters compared to top-ranking deep learning methods. More importantly, we show that our model is more robust to different scenes under close proximity of illuminants, significantly reducing 28.7% worstcase error in data-sparse regions. Our code is available at https://github. coin/ howardyclo/cicc-cvpr21.

关键词： Deep learning Visualization computer vision Image color analysis Computational modeling Data models pattern recognition

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CASTing Your Model: Learning to Localize Improves Self-Supervised Representations

CASTing Your Model: Learning to Localize Improves Self-Super...

引用

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

作者： Selvaraju, Ramprasaath R. Desai, Karan Johnson, Justin Naik, Nikhil Salesforce Res Palo Alto CA 94301 USA Univ Michigan Ann Arbor MI 48109 USA

ISBN: (纸本)9781665445092

Recent advances in self-supervised learning (SSL) have largely closed the gap with supervised ImageNet pretraining. Despite their success these methods have been primarily applied to unlabeled ImageNet images, and show marginal gains when trained on larger sets of uncurated images. We hypothesize that current SSL methods perform best on iconic images, and struggle on complex scene images with many objects. Analyzing contrastive SSL methods shows that they have poor visual grounding and receive poor supervisory signal when trained on scene images. We propose Contrastive Attention-Supervised Tuning (CAST) to overcome these limitations. CAST uses unsupervised saliency maps to intelligently sample crops, and to provide grounding supervision via a Grad-CAM attention loss. Experiments on COCO show that CAST significantly improves the features learned by SSL methods on scene images, and further experiments show that CAST-trained models are more robust to changes in backgrounds. Our code is available at https://***/salesforce/CAST/.

关键词： Visualization computer vision Correlation Codes Grounding Crops Robustness

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Post-hoc Uncertainty Calibration for Domain Drift Scenarios

Post-hoc Uncertainty Calibration for Domain Drift Scenarios

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

作者： Tomani, Christian Gruber, Sebastian Erdem, Muhammed Ebrar Cremers, Daniel Buettner, Florian Tech Univ Munich Munich Germany Siemens AG Munich Germany German Canc Consortium Heidelberg Germany German Canc Res Ctr Heidelberg Heidelberg Germany Frankfurt Univ Frankfurt Germany

ISBN: (纸本)9781665445092

We address the problem of uncertainty calibration. While standard deep neural networks typically yield uncalibrated predictions, calibrated confidence scores that are representative of the true likelihood of a prediction can be achieved using post-hoc calibration methods. However, to date, the focus of these approaches has been on in-domain calibration. Our contribution is two-fold. First, we show that existing post-hoc calibration methods yield highly overconfident predictions under domain shift. Second, we introduce a simple strategy where perturbations are applied to samples in the validation set before performing the post-hoc calibration step. In extensive experiments, we demonstrate that this perturbation step results in substantially better calibration under domain shift on a wide range of architectures and modelling tasks.

关键词： Deep learning computer vision Uncertainty Perturbation methods Computational modeling computer architecture Calibration

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Objectron: A Large Scale Dataset of Object-Centric Videos in the Wild with Pose Annotations

Objectron: A Large Scale Dataset of Object-Centric Videos in...

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

作者： Ahmadyan, Adel Zhang, Liangkai Ablavatski, Artsiom Wei, Jianing Grundmann, Matthias Google Res Mountain View CA 94043 USA

ISBN: (纸本)9781665445092

3D object detection has recently become popular due to many applications in robotics, augmented reality, autonomy, and image retrieval. We introduce the Objectron dataset to advance the state of the art in 3D object detection and foster new research and applications, such as 3D object tracking, view synthesis, and improved 3D shape representation. The dataset contains object-centric short videos with pose annotations for nine categories and includes 4 million annotated images in 14, 819 annotated videos. We also propose a new evaluation metric, 3D Intersection over Union, for 3D object detection. We demonstrate the usefulness of our dataset in 3D object detection and novel view synthesis tasks by providing baseline models trained on this dataset. Our dataset and evaluation source code are available online at ***/google-research-datasets/Objectron.

关键词： Measurement Solid modeling Three-dimensional displays Annotations Shape Object detection pattern recognition

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