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检索条件"任意字段=29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016"

共 90 条记录，以下是21-30 订阅

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ER3: A Unified Framework for Event Retrieval, recognition and Recounting 30

ER3: A Unified Framework for Event Retrieval, Recognition an...

引用

30th ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Gao, Zhanning Hua, Gang Zhang, Dongqing Jojic, Nebojsa Wang, Le Xue, Jianru Zheng, Nanning Xi An Jiao Tong Univ Inst Artificial Intelligence & Robot Xian Shaanxi Peoples R China Microsoft Res Redmond WA USA

ISBN: (纸本)9781538604571

We develop a unified framework for complex event retrieval, recognition and recounting. the framework is based on a compact video representation that exploits the temporal correlations in image features. Our feature alignment procedure identifies and removes the feature redundancies across frames and outputs an intermediate tensor representation we call video imprint. the video imprint is then fed into a reasoning network, whose attention mechanism parallels that of memory networks used in language modeling. the reasoning network simultaneously recognizes the event category and locates the key pieces of evidence for event recounting. In event retrieval tasks, we show that the compact video representation aggregated from the video imprint achieves significantly better retrieval accuracy compared with existing methods. We also set new state of the art results in event recognition tasks with an additional benefit: the latent structure in our reasoning network highlights the areas of the video imprint and can be directly used for event recounting. As video imprint maps back to locations in the video frames, the network allows not only the identification of key frames but also specific areas inside each frame which are most influential to the decision process.

关键词： Feature extraction Cognition pattern recognition Neural networks Layout computer vision

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A Gift from Knowledge Distillation: Fast Optimization, Network Minimization and Transfer Learning 30

A Gift from Knowledge Distillation: Fast Optimization, Netwo...

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30th ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Yim, Junho Joo, Donggyu Bae, Jihoon Kim, Junmo Korea Adv Inst Sci & Technol Sch Elect Engn Daejeon South Korea Elect & Telecommun Res Inst Daejeon South Korea

ISBN: (纸本)9781538604571

We introduce a novel technique for knowledge transfer, where knowledge from a pretrained deep neural network (DNN) is distilled and transferred to another DNN. As the DNN maps from the input space to the output space through many layers sequentially, we define the distilled knowledge to be transferred in terms of flow between layers, which is calculated by computing the inner product between features from two layers. When we compare the student DNN and the original network with the same size as the student DNN but trained without a teacher network, the proposed method of transferring the distilled knowledge as the flow between two layers exhibits three important phenomena: (1) the student DNN that learns the distilled knowledge is optimized much faster than the original model;(2) the student DNN outperforms the original DNN;and (3) the student DNN can learn the distilled knowledge from a teacher DNN that is trained at a different task, and the student DNN outperforms the original DNN that is trained from scratch.

关键词： Training Optimization Knowledge engineering Feature extraction Knowledge transfer computer vision Transfer (Psychology) Knowledge based engineering images computer vision Feature extraction Students TWO-LAYER Functional training Epistemology Training

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A Low Power, Fully Event-Based Gesture recognition System 30

A Low Power, Fully Event-Based Gesture Recognition System

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30th ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Amir, Arnon Taba, Brian Berg, David Melano, Timothy McKinstry, Jeffrey Di Nolfo, Carmelo Nayak, Tapan Andreopoulos, Alexander Garreau, Guillaume Mendoza, Marcela Kusnitz, Jeff Debole, Michael Esser, Steve Delbruck, Tobi Flickner, Myron Modha, Dharmendra IBM Res Yorktown Hts NY 10598 USA Univ Calif San Diego San Diego CA USA UZH ETH Zurich Zurich Switzerland IniLabs GmbH Zurich Switzerland

ISBN: (纸本)9781538604571

We present the first gesture recognition system implemented end-to-end on event-based hardware, using a TrueNorth neurosynaptic processor to recognize hand gestures in real-time at low power from events streamed live by a Dynamic vision Sensor (DVS). the biologically inspired DVS transmits data only when a pixel detects a change, unlike traditional frame-based cameras which sample every pixel at a fixed frame rate. this sparse, asynchronous data representation lets event-based cameras operate at much lower power than frame-based cameras. However, much of the energy efficiency is lost if, as in previous work, the event stream is interpreted by conventional synchronous processors. Here, for the first time, we process a live DVS event stream using TrueNorth, a natively event-based processor with 1 million spiking neurons. Configured here as a convolutional neural network (CNN), the TrueNorth chip identifies the onset of a gesture with a latency of 105 ms while consuming less than 200mW. the CNN achieves 96.5% out-of-sample accuracy on a newly collected DVS dataset (DvsGesture) comprising 11 hand gesture categories from 29 subjects under 3 illumination conditions.

关键词： Event-based Gestures LOW POWER Daphne virus S Gesture recognition Energy efficiency pixel end-to-end Cameras PROCESSOR recognition system Latency

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Instance-aware Image and Sentence Matching with Selective Multimodal LSTM 30

Instance-aware Image and Sentence Matching with Selective Mu...

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30th ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Huang, Yan Wang, Wei Wang, Liang Natl Lab Pattern Recognit Ctr Res Intelligent Percept & Comp CRIPAC Beijing Peoples R China Chinese Acad Sci CASIA Ctr Excellence Brain Sci & Intelligence Tech Inst Automat Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

ISBN: (纸本)9781538604571

Effective image and sentence matching depends on how to well measure their global visual-semantic similarity. Based on the observation that such a global similarity arises from a complex aggregation of multiple local similarities between pairwise instances of image (objects) and sentence (words), we propose a selective multimodal Long Short-Term Memory network (sm-LSTM) for instance-aware image and sentence matching. the sm-LSTM includes a multimodal context-modulated attention scheme at each timestep that can selectively attend to a pair of instances of image and sentence, by predicting pairwise instance-aware saliency maps for image and sentence. For selected pairwise instances, their representations are obtained based on the predicted saliency maps, and then compared to measure their local similarity. By similarly measuring multiple local similarities within a few timesteps, the sm-LSTM sequentially aggregates them with hidden states to obtain a final matching score as the desired global similarity. Extensive experiments show that our model can well match image and sentence with complex content, and achieve the state-of-the-art results on two public benchmark datasets.

关键词： Dogs Roads Image color analysis Aggregates Detectors pattern recognition

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A Novel Tensor-based Video Rain Streaks Removal Approach via Utilizing Discriminatively Intrinsic Priors 30

A Novel Tensor-based Video Rain Streaks Removal Approach via...

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30th ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Jiang, Tai-Xiang Huang, Ting-Zhu Zhao, Xi-Le Deng, Liang-Jian Wang, Yao Univ Elect Sci & Technol China Sch Math Sci Chengdu Sichuan Peoples R China Xi An Jiao Tong Univ Sch Math & Stat Xian Shaanxi Peoples R China

ISBN: (纸本)9781538604571

Rain streaks removal is an important issue of the outdoor vision system and has been recently investigated extensively. In this paper, we propose a novel tensor based video rain streaks removal approach by fully considering the discriminatively intrinsic characteristics of rain streaks and clean videos, which needs neither rain detection nor time-consuming dictionary learning stage. In specific, on the one hand, rain streaks are sparse and smooth along the raindrops' direction, and on the other hand, the clean videos possess smoothness along the rain-perpendicular direction and global and local correlation along time direction. We use the l(1) norm to enhance the sparsity of the underlying rain streaks, two unidirectional Total Variation (TV) regularizers to guarantee the different discriminative smoothness, and a tensor nuclear norm and a time directional difference operator to characterize the exclusive correlation of the clean video along time. Alternation direction method of multipliers (ADMM) is employed to solve the proposed concise tensor based convex model. Experiments implemented on synthetic and real data substantiate the effectiveness and efficiency of the proposed method. Under comprehensive quantitative performance measures, our approach outperforms other state-of-the-art methods.

关键词： rain Streak Videotapes computer vision difference operator Smoothness

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A Deep Regression Architecture with Two-Stage Re-initialization for High Performance Facial Landmark Detection 30

A Deep Regression Architecture with Two-Stage Re-initializat...

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30th ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Lv, Jiangjing Shao, Xiaohu Xing, Junliang Cheng, Cheng Zhou, Xi Chinese Acad Sci Chongqing Inst Green & Intelligent Technol Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Chinese Acad Sci Inst Automat Beijing Peoples R China

ISBN: (纸本)9781538604571

Regression based facial landmark detection methods usually learns a series of regression functions to update the landmark positions from an initial estimation. Most of existing approaches focus on learning effective mapping functions with robust image features to improve performance. the approach to dealing with the initialization issue, however, receives relatively fewer attentions. In this paper, we present a deep regression architecture with two-stage re-initialization to explicitly deal with the initialization problem. At the global stage, given an image with a rough face detection result, the full face region is firstly re-initialized by a supervised spatial transformer network to a canonical shape state and then trained to regress a coarse landmark estimation. At the local stage, different face parts are further separately re-initialized to their own canonical shape states, followed by another regression subnetwork to get the final estimation. Our proposed deep architecture is trained from end to end and obtains promising results using different kinds of unstable initialization. It also achieves superior performances over many competing algorithms.

关键词： Face Shape computer architecture Robustness Detectors Machine learning

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Spatiotemporal Pyramid Network for Video Action recognition 30

Spatiotemporal Pyramid Network for Video Action Recognition

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30th ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Wang, Yunbo Long, Mingsheng Wang, Jianmin Yu, Philip S. Tsinghua Univ MOE KLiss Beijing Peoples R China Tsinghua Univ TNList Beijing Peoples R China Tsinghua Univ NEL BDSS Beijing Peoples R China Tsinghua Univ Sch Software Beijing Peoples R China Univ Illinois Chicago IL 60680 USA

ISBN: (纸本)9781538604571

Two-stream convolutional networks have shown strong performance in video action recognition tasks. the key idea is to learn spatiotemporal features by fusing convolutional networks spatially and temporally. However, it remains unclear how to model the correlations between the spatial and temporal structures at multiple abstraction levels. First, the spatial stream tends to fail if two videos share similar backgrounds. Second, the temporal stream may be fooled if two actions resemble in short snippets, though appear to be distinct in the long term. We propose a novel spatiotemporal pyramid network to fuse the spatial and temporal features in a pyramid structure such that they can reinforce each other. From the architecture perspective, our network constitutes hierarchical fusion strategies which can be trained as a whole using a unified spatiotemporal loss. A series of ablation experiments support the importance of each fusion strategy. From the technical perspective, we introduce the spatiotemporal compact bilinear operator into video analysis tasks. this operator enables efficient training of bilinear fusion operations which can capture full interactions between the spatial and temporal features. Our final network achieves state-of-the-art results on standard video datasets.

关键词： Videotapes Pyramid Network bilinearity Fusion reactions FUSION fusion (melting) Fusion, biological recognition (Psychology)

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Binarized Mode Seeking for Scalable Visual pattern Discovery 30

Binarized Mode Seeking for Scalable Visual Pattern Discovery

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30th ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Zhang, Wei Cao, Xiaochun Wang, Rui Guo, Yuanfang Chen, Zhineng Chinese Acad Sci Inst Informat Engn SKLOIS Beijing Peoples R China Univ Chinese Acad Sci Sch Cyber Secur Beijing Peoples R China Chinese Acad Sci Inst Automat Beijing Peoples R China

ISBN: (纸本)9781538604571

this paper studies visual pattern discovery in large-scale image collections via binarized mode seeking, where images can only be represented as binary codes for efficient storage and computation. We address this problem from the perspective of binary space mode seeking. First, a binary mean shift (bMS) is proposed to discover frequent patterns via mode seeking directly in binary space. the binomial-based kernel and binary constraint are introduced for binarized analysis. Second, we further extend bMS to a more general form, namely contrastive binary mean shift (cbMS), which maximizes the contrastive density in binary space, for finding informative patterns that are both frequent and discriminative for the dataset. With the binarized algorithm and optimization, our methods demonstrate significant computation (50x) and storage (32x) improvement compared to standard techniques operating in Euclidean space, while the performance does not largely degenerate. Furthermore, cbMS discovers more informative patterns by suppressing low discriminative modes. We evaluate our methods on both annotated ILSVRC (1M images) and un-annotated blind Flickr (10M images) datasets with million scale images, which demonstrates both the scalability and effectiveness of our algorithms for discovering frequent and informative patterns in large scale collection.

关键词： Binary Scalability images binary code Dataset Euclidean space spatial mode image samples Visualization

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See the Forest for the Trees: Joint Spatial and Temporal Recurrent Neural Networks for Video-based Person Re-identification 30

See the Forest for the Trees: Joint Spatial and Temporal Rec...

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30th ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Zhou, Zhen Huang, Yan Wang, Wei Wang, Liang Tan, Tieniu Natl Lab Pattern Recognit NLPR Ctr Res Intelligent Percept & Comp CRIPAC Beijing Peoples R China Chinese Acad Sci CASIA Inst Automat Ctr Excellence Brain Sci & Intelligence Technol C Beijing Peoples R China Univ Chinese Acad Sci UCAS Beijing Peoples R China

ISBN: (纸本)9781538604571

Surveillance cameras have been widely used in different scenes. Accordingly, a demanding need is to recognize a person under different cameras, which is called person re-identification. this topic has gained increasing interests in computer vision recently. However, less attention has been paid to video-based approaches, compared with image-based ones. Two steps are usually involved in previous approaches, namely feature learning and metric learning. But most of the existing approaches only focus on either feature learning or metric learning. Meanwhile, many of them do not take full use of the temporal and spatial information. In this paper, we concentrate on video-based person re-identification and build an end-to-end deep neural network architecture to jointly learn features and metrics. the proposed method can automatically pick out the most discriminative frames in a given video by a temporal attention model. Moreover, it integrates the surrounding information at each location by a spatial recurrent model when measuring the similarity with another pedestrian video. that is, our method handles spatial and temporal information simultaneously in a unified manner. the carefully designed experiments on three public datasets show the effectiveness of each component of the proposed deep network, performing better in comparison with the state-of-the-art methods.

关键词： Measurement Image sequences Feature extraction computer architecture computer vision Recurrent neural networks

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Weighted-Entropy-based Quantization for Deep Neural Networks 30

Weighted-Entropy-based Quantization for Deep Neural Networks

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30th ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Park, Eunhyeok Ahn, Junwhan Yoo, Sungjoo Seoul Natl Univ Comp & Memory Architecture Lab Seoul South Korea Seoul Natl Univ Design Automat Lab Seoul South Korea

ISBN: (纸本)9781538604571

Quantization is considered as one of the most effective methods to optimize the inference cost of neural network models for their deployment to mobile and embedded systems, which have tight resource constraints. In such approaches, it is critical to provide low-cost quantization under a tight accuracy loss constraint (e.g., 1%). In this paper, we propose a novel method for quantizing weights and activations based on the concept of weighted entropy. Unlike recent work on binary-weight neural networks, our approach is multi-bit quantization, in which weights and activations can be quantized by any number of bits depending on the target accuracy. this facilitates much more flexible exploitation of accuracy-performance trade-off provided by different levels of quantization. Moreover, our scheme provides an automated quantization flow based on conventional training algorithms, which greatly reduces the design-time effort to quantize the network. According to our extensive evaluations based on practical neural network models for image classification (AlexNet, GoogLeNet and ResNet-50/101), object detection (R-FCN with ResNet-50), and language modeling (an LSTM network), our method achieves significant reductions in both the model size and the amount of computation with minimal accuracy loss. Also, compared to existing quantization schemes, ours provides higher accuracy with a similar resource constraint and requires much lower design effort.

关键词： Quantization model size Neural network design effort Activation Embedded systems Neural Networks (computer) Drill bits

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