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检索条件"任意字段=32nd IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2019"

共 858 条记录，以下是51-60 订阅

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It's not about the Journey;It's about the Destination: Following Soft Paths under Question-Guidance for Visual Reasoning 32

It's not about the Journey;It's about the Destination: Follo...

引用

32nd ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Haurilet, Monica Roitberg, Alina Stiefelhagen, Rainer Karlsruhe Inst Technol D-76131 Karlsruhe Germany

ISBN: (纸本)9781728132938

Visual Reasoning remains a challenging task, as it has to deal with long-range and multi-step object relationships in the scene. We present a new model for Visual Reasoning, aimed at capturing the interplay among individual objects in the image represented as a scene graph. As not all graph components are relevant for the query, we introduce the concept of a question-based visual guide, which constrains the potential solution space by learning an optimal traversal scheme, where the final destination nodes alone are used to produce the answer. We show, that finding relevant semantic structures facilitates generalization to new tasks by introducing a novel problem of knowledge transfer: training on one question type and answering questions from a different domain without any training data. Furthermore, we report state-of-the-art results for Visual Reasoning on multiple query types and diverse image and video datasets.

关键词： vision + Language Visual Reasoning

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Neural Sequential Phrase Grounding (SeqGROUnd) 32

Neural Sequential Phrase Grounding (SeqGROUND)

引用

32nd ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Dogan, Pelin Sigal, Leonid Gross, Markus Swiss Fed Inst Technol Zurich Switzerland Univ British Columbia Vancouver BC Canada Vector Inst Toronto ON Canada Disney Res Zurich Switzerland

ISBN: (纸本)9781728132938

We propose an end-to-end approach for phrase grounding in images. Unlike prior methods that typically attempt to ground each phrase independently by building an imagetext embedding, our architecture formulates grounding of multiple phrases as a sequential and contextual process. Specifically, we encode region proposals and all phrases into two stacks of LSTM cells, along with so far grounded phrase-region pairs. These LSTM stacks collectively capture context for grounding of the next phrase. The resulting architecture, which we call SeqGROUnd, supports many-to-many matching by allowing an image region to be matched to multiple phrases and vice versa. We show competitive performance on the Flickr30K benchmark dataset and, through ablation studies, validate the efficacy of sequential grounding as well as individual design choices in our model architecture.

关键词： Categorization recognition: Detection Retrieval vision + Language Visual Reasoning

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Multi-Task Multi-Sensor Fusion for 3D Object Detection 32

Multi-Task Multi-Sensor Fusion for 3D Object Detection

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32nd ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Liang, Ming Yang, Bin Chen, Yun Hu, Rui Urtasun, Raquel Uber Adv Technol Grp Pittsburgh PA 15201 USA Univ Toronto Toronto ON Canada Uber AI Residency Program San Francisco CA USA

ISBN: (纸本)9781728132938

In this paper we propose to exploit multiple related tasks for accurate multi-sensor 3D object detection. Towards this goal we present an end-to-end learnable architecture that reasons about 2D and 3D object detection as well as ground estimation and depth completion. Our experiments show that all these tasks are complementary and help the network learn better representations by fusing information at various levels. Importantly, our approach leads the KITTI benchmark on 2D, 3D and bird's eye view object detection, while being real-time.

关键词： 3D from Multiview and Sensors Categorization Deep Learning recognition: Detection Representation Learning Retrieval Robotics + Driving

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VizWiz-Priv: A Dataset for Recognizing the Presence and Purpose of Private Visual Information in Images Taken by Blind People 32

VizWiz-Priv: A Dataset for Recognizing the Presence and Purp...

引用

32nd ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Gurari, Danna Li, Qing Lin, Chi Zhao, Yinan Guo, Anhong Stangl, Abigale Bigham, Jeffrey P. Univ Texas Austin Austin TX 78712 USA Univ Calif Los Angeles Los Angeles CA 90024 USA Carnegie Mellon Univ Pittsburgh PA 15213 USA Univ Colorado Boulder CO 80309 USA

ISBN: (纸本)9781728132938

We introduce the first visual privacy dataset originating from people who are blind in order to better understand their privacy disclosures and to encourage the development of algorithms that can assist in preventing their unintended disclosures. It includes 8,862 regions showing private content across 5,537 images taken by blind people. Of these, 1,403 are paired with questions and 62% of those directly ask about the private content. Experiments demonstrate the utility of this data for predicting whether an image shows private information and whether a question asks about the private content in an image. The dataset is publicly-shared at http://***/data/.

关键词： Datasets and Evaluation vision + Language vision Applications and Systems

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Attention Branch Network: Learning of Attention Mechanism for Visual Explanation 32

Attention Branch Network: Learning of Attention Mechanism fo...

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32nd ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Fukui, Hiroshi Hirakawa, Tsubasa Yamashita, Takayoshi Fujiyoshi, Hironobu Chubu Univ 1200 Matsumotocho Kasugai Aichi Japan

ISBN: (纸本)9781728132938

Visual explanation enables humans to understand the decision making of deep convolutional neural network (CNN), but it is insufficient to contribute to improving CNN performance. In this paper, we focus on the attention map for visual explanation, which represents a high response value as the attention location in image recognition. This attention region significantly improves the performance of CNN by introducing an attention mechanism that focuses on a specific region in an image. In this work, we propose Attention Branch Network (ABN), which extends a response-based visual explanation model by introducing a branch structure with an attention mechanism. ABN can be applicable to several image recognition tasks by introducing a branch for the attention mechanism and is trainable for visual explanation and image recognition in an end-to-end manner. We evaluate ABN on several image recognition tasks such as image classification, fine-grained recognition, and multiple facial attribute recognition. Experimental results indicate that ABN outperforms the baseline models on these image recognition tasks while generating an attention map for visual explanation. Our code is available(1).

关键词： And Body Pose Big Data computer vision Theory Deep Learning Face Gesture Large Scale Methods recognition:

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Adaptive NMS: Refining Pedestrian Detection in a Crowd 32

Adaptive NMS: Refining Pedestrian Detection in a Crowd

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32nd ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Liu, Songtao Huang, Di Wang, Yunhong Beihang Univ Beijing Adv Innovat Ctr Big Data & Brain Comp Beijing Peoples R China Beihang Univ State Key Lab Software Dev Environm Beijing Peoples R China Beihang Univ Sch Comp Sci & Engn Beijing 100191 Peoples R China

ISBN: (纸本)9781728132938

Pedestrian detection in a crowd is a very challenging issue. This paper addresses this problem by a novel Non-Maximum Suppression (NMS) algorithm to better refine the bounding boxes given by detectors. The contributions are threefold: (1) we propose adaptive-NMS, which applies a dynamic suppression threshold to an instance, according to the target density;(2) we design an efficient subnetwork to learn density scores, which can be conveniently embedded into both the single-stage and two-stage detectors;and (3) we achieve state of the art results on the CityPersons and CrowdHuman benchmarks.

关键词： And Body Pose Categorization Face Gesture recognition: Detection Retrieval

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MBS: Macroblock Scaling for CNN Model Reduction 32

MBS: Macroblock Scaling for CNN Model Reduction

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32nd ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Lin, Yu-Hsun Chou, Chun-Nan Chang, Edward Y. HTC Res & Healthcare DeepQ New Taipei Taiwan

ISBN: (纸本)9781728132938

In this paper we propose the macroblock scaling (MBS) algorithm, which can be applied to various CNN architectures to reduce their model size. MBS adaptively reduces each CNN macroblock depending on its information redundancy measured by our proposed effective flops. Empirical studies conducted with ImageNet and CIFAR-10 attest that MBS can reduce the model size of some already compact CNN models, e.g., MobileNetV2 (25.03% further reduction) and ShuffleNet (20.74%), and even ultra-deep ones such as ResNet-101 (51.67%) and ResNet-1202 (72.71%) with negligible accuracy degradation. MBS also performs better reduction at a much lower cost than the state-of-the-art optimization-based methods do. MBS's simplicity and efficiency, its flexibility to work with any CNN model, and its scalability to work with models of any depth make it an attractive choice for CNN model size reduction.

关键词： Categorization Deep Learning recognition: Detection Retrieval

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Recurrent Back-Projection Network for Video Super-Resolution 32

Recurrent Back-Projection Network for Video Super-Resolution

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32nd ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Haris, Muhammad Shakhnarovich, Greg Ukita, Norimichi Toyota Technol Inst Nagoya Aichi Japan Toyota Technol Inst Chicago IL USA

ISBN: (纸本)9781728132938

We proposed a novel architecture for the problem of video super-resolution. We integrate spatial and temporal contexts from continuous video frames using a recurrent encoder-decoder module, that fuses multi frame information with the more traditional, single frame super-resolution path for the target frame. In contrast to most prior work where frames are pooled together by stacking or warping, our model, the Recurrent Back-Projection Network (RBPN) treats each context frame as a separate source of information. These sources are combined in an iterative refinement framework inspired by the idea of back projectionin multiple-image super-resolution. This is aided by explicitly representing estimated inter-frame motion with respect to the target, rather than explicitly aligning frames. We propose a new video super-resolution benchmark, allowing evaluation at a larger scale and considering videos in different motion regimes. Experimental results demonstrate that our RBPN is superior to existing methods on several datasets.

关键词： Deep Learning Low-level vision

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Long-Term Feature Banks for Detailed Video Understanding 32

Long-Term Feature Banks for Detailed Video Understanding

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32nd ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Wu, Chao-Yuan Feichtenhofer, Christoph Fan, Haoqi He, Kaiming Krahenbuhl, Philipp Girshick, Ross Univ Texas Austin Austin TX 78712 USA Facebook AI Res FAIR Menlo Pk CA 94025 USA

ISBN: (纸本)9781728132938

To understand the world, we humans constantly need to relate the present to the past, and put events in context. In this paper, we enable existing video models to do the same. We propose a long-term feature bank-supportive information extracted over the entire span of a video-to augment state-of-the-art video models that otherwise would only view short clips of 2-5 seconds. Our experiments demonstrate that augmenting 3D convolutional networks with a long-term feature bank yields state-of-the-art results on three challenging video datasets: AVA, EPIC-Kitchens, and Charades. Code is available online.

关键词： Action recognition Categorization Deep Learning recognition: Detection Retrieval Scene Analysis and Understanding

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Learning Video Representations from Correspondence Proposals 32

Learning Video Representations from Correspondence Proposals

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32nd ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Liu, Xingyu Lee, Joon-Young Jin, Hailin Stanford Univ Stanford CA 94305 USA Adobe Res San Jose CA USA

ISBN: (纸本)9781728132938

Correspondences between frames encode rich information about dynamic content in videos. However, it is challenging to effectively capture and learn those due to their irregular structure and complex dynamics. In this paper, we propose a novel neural network that learns video representations by aggregating information from potential correspondences. This network, named CPNet, can learn evolving 2D fields with temporal consistency. In particular, it can effectively learn representations for videos by mixing appearance and long-range motion with an RGB-only input. We provide extensive ablation experiments to validate our model. CPNet shows stronger performance than existing methods on Kinetics and achieves the state-of-the-art performance on Something-Something and Jester. We provide analysis towards the behavior of our model and show its robustness to errors in proposals.

关键词： Action recognition Deep Learning Representation Learning

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