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检索条件"任意字段=1994 IEEE Computer-Society Conference on Computer Vision and Pattern Recognition"

共 22906 条记录，以下是4731-4740 订阅

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Predicting Goal-directed Human Attention Using Inverse Reinforcement Learning

Predicting Goal-directed Human Attention Using Inverse Reinf...

引用

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

作者： Yang, Zhibo Huang, Lihan Chen, Yupei Wei, Zijun Ahn, Seoyoung Zelinsky, Gregory Samaras, Dimitris Hoai, Minh SUNY Stony Brook Stony Brook NY 11794 USA Adobe Inc San Jose CA USA

ISBN: (纸本)9781728171685

Human gaze behavior prediction is important for behavioral vision and for computer vision applications. Most models mainly focus on predicting free-viewing behavior using saliency maps, but do not generalize to goal-directed behavior, such as when a person searches for a visual target object. We propose the first inverse reinforcement learning (IRL) model to learn the internal reward function and policy used by humans during visual search. We modeled the viewer's internal belief states as dynamic contextual belief maps of object locations. These maps were learned and then used to predict behavioral scanpaths for multiple target categories. To train and evaluate our IRL model we created COCO-Search18, which is now the largest dataset of high-quality search fixations in existence. COCO-Search18 has 10 participants searching for each of 18 target-object categories in 6202 images, making about 300,000 goal-directed fixations. When trained and evaluated on COCO-Search18, the IRL model outperformed baseline models in predicting search fixation scanpaths, both in terms of similarity to human search behavior and search efficiency. Finally, reward maps recovered by the IRL model reveal distinctive target-dependent patterns of object prioritization, which we interpret as a learned object context.

关键词： computer vision

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NVTC: Nonlinear Vector Transform Coding

NVTC: Nonlinear Vector Transform Coding

引用

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

作者： Feng, Runsen Guo, Zongyu Li, Weiping Chen, Zhibo Univ Sci & Technol China Hefei Peoples R China

ISBN: (纸本)9798350301298

In theory, vector quantization (VQ) is always better than scalar quantization (SQ) in terms of rate-distortion (RD) performance [33]. Recent state-of-the-art methods for neural image compression are mainly based on nonlinear transform coding (NTC) with uniform scalar quantization, overlooking the benefits of VQ due to its exponentially increased complexity. In this paper, we first investigate on some toy sources, demonstrating that even if modern neural networks considerably enhance the compression performance of SQ with nonlinear transform, there is still an insurmountable chasm between SQ and VQ. Therefore, revolving around VQ, we propose a novel framework for neural image compression named Nonlinear Vector Transform Coding (NVTC). NVTC solves the critical complexity issue of VQ through (1) a multi-stage quantization strategy and (2) nonlinear vector transforms. In addition, we apply entropy-constrained VQ in latent space to adaptively determine the quantization boundaries for joint rate-distortion optimization, which improves the performance both theoretically and experimentally. Compared to previous NTC approaches, NVTC demonstrates superior rate-distortion performance, faster decoding speed, and smaller model size. Our code is available at https://***/USTC-IMCL/NVTC.

关键词： Low-level vision

来源：评论

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Data-driven Feature Tracking for Event Cameras

Data-driven Feature Tracking for Event Cameras

引用

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

作者： Messikommer, Nico Fang, Carter Gehrig, Mathias Scaramuzza, Davide Univ Zurich Robot & Percept Grp Zurich Switzerland

ISBN: (纸本)9798350301298

Because of their high temporal resolution, increased resilience to motion blur, and very sparse output, event cameras have been shown to be ideal for low-latency and low-bandwidth feature tracking, even in challenging scenarios. Existing feature tracking methods for event cameras are either handcrafted or derived from first principles but require extensive parameter tuning, are sensitive to noise, and do not generalize to different scenarios due to unmodeled effects. To tackle these deficiencies, we introduce the first data-driven feature tracker for event cameras, which leverages low-latency events to track features detected in a grayscale frame. We achieve robust performance via a novel frame attention module, which shares information across feature tracks. By directly transferring zero-shot from synthetic to real data, our data-driven tracker outperforms existing approaches in relative feature age by up to 120% while also achieving the lowest latency. This performance gap is further increased to 130% by adapting our tracker to real data with a novel self-supervision strategy.

关键词： Low-level vision

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Contrast Prior and Fluid Pyramid Integration for RGBD Salient Object Detection 32

Contrast Prior and Fluid Pyramid Integration for RGBD Salien...

引用

32nd ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Zhao, Jia-Xing Cao, Yang Fan, Deng-Ping Cheng, Ming-Ming Li, Xuan-Yi Zhang, Le Nankai Univ CS TKLNDST Tianjin Peoples R China ASTAR Singapore Singapore

ISBN: (纸本)9781728132938

The large availability of depth sensors provides valuable complementary information for salient object detection (SOD) in RGBD images. However, due to the inherent difference between RGB and depth information, extracting features from the depth channel using ImageNet pre-trained backbone models and fusing them with RGB features directly are sub-optimal. In this paper, we utilize contrast prior;which used to be a dominant cue in none deep learning based SOD approaches, into CNNs-based architecture to enhance the depth information. The enhanced depth cues are further integrated with RGB features for SOD, using a novel fluid pyramid integration, which can make better use of multi-scale cross-modal features. Comprehensive experiments on 5 challenging benchmark datasets demonstrate the superiority of the architecture CPFP over 9 state-of-the-art alternative methods.

关键词： Low-level vision RGBD sensors and analytics

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Cross-view and Cross-pose Completion for 3D Human Understanding

Cross-view and Cross-pose Completion for 3D Human Understand...

引用

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

作者： Armando, Matthieu Galaaoui, Salma Baradel, Fabien Lucas, Thomas Leroy, Vincent Bregier, Romain Weinzaepfel, Philippe Rogez, Gregory NAVER LABS Europe Meylan France

ISBN: (纸本)9798350353013;9798350353006

Human perception and understanding is a major domain of computer vision which, like many other vision subdomains recently, stands to gain from the use of large models pre-trained on large datasets. We hypothesize that the most common pre-training strategy of relying on general purpose, object-centric image datasets such as ImageNet, is limited by an important domain shift. On the other hand, collecting domain-specific ground truth such as 2D or 3D labels does not scale well. Therefore, we propose a pre-training approach based on self-supervised learning that works on human-centric data using only images. Our method uses pairs of images of humans: the first is partially masked and the model is trained to reconstruct the masked parts given the visible ones and a second image. It relies on both stereoscopic (cross-view) pairs, and temporal (cross-pose) pairs taken from videos, in order to learn priors about 3D as well as human motion. We pre-train a model for body-centric tasks and one for hand-centric tasks. With a generic transformer architecture, these models outperform existing self-supervised pre-training methods on a wide set of human-centric downstream tasks, and obtain state-of-the-art performance for instance when fine-tuning for model-based and model-free human mesh recovery.

关键词： hand-centric human-centric pretraining representation learning vision tranformer

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Natural and Realistic Single Image Super-Resolution with Explicit Natural Manifold Discrimination 32

Natural and Realistic Single Image Super-Resolution with Exp...

引用

32nd ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Soh, Jae Woong Park, Gu Yong Jo, Junho Cho, Nam Ik Seoul Natl Univ Dept ECE INMC Seoul South Korea

ISBN: (纸本)9781728132938

Recently, many convolutional neural networksfor single image super-resolution (SISR) have been proposed, which focus on reconstructingthe high-resolutionimages in terms of objective distortion measures. However, the networks trainedwith objective lossfunctions generallyfail to reconstructthe realisticfine textures and details that are essential for better perceptual quality. Recovering the realistic details remains a challengingproblem, and only a few works have been proposed which aim at increasing the perceptual quality by generating enhanced textures. However the generatedfake details often make undesirableartifacts and the overall image looks somewhat unnatural. Therefore, in thispaper,we present a new approachto reconstructingrealistic super-resolved images with high perceptualquality, while maintainingthe naturalnessof the result. In particular we focus on the domain priorpropertiesof SISR problem. Specifically, we define the naturalnesspriorin the lowlevel domain and constrain the output image in the natural manifold, which eventually generates more natural and realistic images. Our results show better naturalness compared to the recent super-resolution algorithms including perception-orientedones.

关键词： Deep Learning Low-level vision

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Multi-Agent Tensor Fusion for Contextual Trajectory Prediction 32

Multi-Agent Tensor Fusion for Contextual Trajectory Predicti...

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32nd ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Zhao, Tianyang Xu, Yifei Monfort, Mathew Choi, Wongun Baker, Chris Zhao, Yibiao Wang, Yizhou Wu, Ying Nian ISEE AI Cambridge MA USA Peking Univ Beijing Peoples R China Univ Calif Los Angeles Los Angeles CA 90024 USA MIT CSAIL Cambridge MA USA

ISBN: (纸本)9781728132938

Accurate prediction of others' trajectories is essential for autonomous driving. Trajectory prediction is challenging because it requires reasoning about agents' past movements, social interactions among varying numbers and kinds of agents, constraints from the scene context, and the stochasticity of human behavior. Our approach models these interactions and constraints jointly within a novel Multi-Agent Tensor Fusion (MATF) network. Specifically, the model encodes multiple agents' past trajectories and the scene context into a Multi-Agent Tensor, then applies convolutional fusion to capture multiagent interactions while retaining the spatial structure of agents and the scene context. The model decodes recurrently to multiple agents' future trajectories, using adversarial loss to learn stochastic predictions. Experiments on both highway driving and pedestrian crowd datasets show that the model achieves state-of-the-art prediction accuracy.

关键词： Deep Learning Motion and Tracking Robotics + Driving Scene Analysis and Understanding vision Applications and System

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Collaborative and Adversarial Network for Unsupervised domain adaptation 31

Collaborative and Adversarial Network for Unsupervised domai...

引用

31st ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Zhang, Weichen Ouyang, Wanli Li, Wen Xu, Dong Univ Sydney Sch Elect & Informat Engn Sydney NSW Australia Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland

ISBN: (纸本)9781538664209

In this paper, we propose a new unsupervised domain adaptation approach called Collaborative and Adversarial Network (CAN) through domain-collaborative and domain adversarial training of neural networks. We add several domain classifiers on multiple CNN feature extraction blocks1, in which each domain classifier is connected to the hidden representations from one block and one loss function is defined based on the hidden presentation and the domain labels (e.g., source and target). We design a new loss function by integrating the losses from all blocks in order to learn domain informative representations from lower blocks through collaborative learning and learn domain uninformative representations from higher blocks through adversarial learning. We further extend our CAN method as Incremental CAN (iCAN), in which we iteratively select a set of pseudo labelled target samples based on the image classifier and the last domain classifier from the previous training epoch and re-train our CAN model by using the enlarged training set. Comprehensive experiments on two benchmark datasets Office and ImageCLEF-DA clearly demonstrate the effectiveness of our newly proposed approaches CAN and iCAN for unsupervised domain adaptation.

关键词： Training Collaboration Feature extraction Adaptation models Visualization Task analysis computer vision

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Background-Aware Pooling and Noise-Aware Loss for Weakly-Supervised Semantic Segmentation

Background-Aware Pooling and Noise-Aware Loss for Weakly-Sup...

引用

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

作者： Oh, Youngmin Kim, Beomjun Ham, Bumsub Yonsei Univ Sch Elect & Elect Engn Seoul South Korea

ISBN: (纸本)9781665445092

We address the problem of weakly-supervised semantic segmentation (WSSS) using bounding box annotations. Although object bounding boxes are good indicators to segment corresponding objects, they do not specify object boundaries, making it hard to train convolutional neural networks (CNNs) for semantic segmentation. We find that background regions are perceptually consistent in part within an image, and this can be leveraged to discriminate foreground and background regions inside object bounding boxes. To implement this idea, we propose a novel pooling method, dubbed background-aware pooling (BAP), that focuses more on aggregating foreground features inside the bounding boxes using attention maps. This allows to extract high-quality pseudo segmentation labels to train CNNs for semantic segmentation, but the labels still contain noise especially at object boundaries. To address this problem, we also introduce a noise-aware loss (NAL) that makes the networks less susceptible to incorrect labels. Experimental results demonstrate that learning with our pseudo labels already outperforms state-of-the-art weakly- and semi-supervised methods on the PASCAL VOC 2012 dataset, and the NAL further boosts the performance.

关键词： Training Image segmentation computer vision Annotations Computational modeling Semantics Feature extraction

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Joint Distribution Matters: Deep Brownian Distance Covariance for Few-Shot Classification

Joint Distribution Matters: Deep Brownian Distance Covarianc...

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

作者： Xie, Jiangtao Long, Fei Lv, Jiaming Wang, Qilong Li, Peihua Dalian Univ Technol Dalian Peoples R China Tianjin Univ Tianjin Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Few-shot classification is a challenging problem as only very few training examples are given for each new task. One of the effective research lines to address this challenge focuses on learning deep representations driven by a similarity measure between a query image and few support images of some class. Statistically, this amounts to measure the dependency of image features, viewed as random vectors in a high-dimensional embedding space. Previous methods either only use marginal distributions without considering joint distributions, suffering from limited representation capability, or are computationally expensive though harnessing joint distributions. In this paper, we propose a deep Brownian Distance Covariance (DeepBDC) method for few-shot classification. The central idea of DeepBDC is to learn image representations by measuring the discrepancy between joint characteristic functions of embedded features and product of the marginals. As the BDC metric is decoupled, we formulate it as a highly modular and efficient layer. Furthermore, we instantiate DeepBDC in two different few-shot classification frameworks. We make experiments on six standard few-shot image benchmarks, covering general object recognition, fine-grained categorization and cross-domain classification. Extensive evaluations show our DeepBDC significantly outperforms the counterparts, while establishing new state-of-the-art results. The source code is available at http://***/DeepBDC.

关键词： Training Deep learning computer vision Transfer learning Image representation Extraterrestrial measurements pattern recognition

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