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检索条件"任意字段=26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR"

共 1569 条记录，以下是441-450 订阅

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BIND: Binary Integrated Net Descriptors for Texture-less Object recognition 30

BIND: Binary Integrated Net Descriptors for Texture-less Obj...

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

作者： Chan, Jacob Lee, Jimmy Addison Kemao, Qian Nanyang Technol Univ SCE Block N4 Nanyang Ave Singapore 639798 Singapore ASTAR I2R 1 Fusionopolis WayConnexis South Tower Singapore 138632 Singapore

ISBN: (纸本)9781538604571

this paper presents BIND (Binary Integrated Net Descriptor), a texture-less object detector that encodes multi-layered binary-represented nets for high precision edge-based description. Our proposed concept aligns layers of object-sized patches (nets) onto highly fragmented occlusion resistant line-segment midpoints (linelets) to encode regional information into efficient binary strings. these lightweight nets encourage discriminative object description through their high-spatial resolution, enabling highly precise encoding of the object's edges and internal texture-less information. BIND achieved various invariant properties such as rotation, scale and edge-polarity through its unique binary logical-operated encoding and matching techniques, while performing remarkably well in occlusion and clutter. Apart from yielding efficient computational performance, BIND also attained remarkable recognition rates surpassing recent state-of-the-art texture-less object detectors such as BORDER, BOLD and LINE2D.

关键词： object description Nets recognition (Psychology) detector Occlusion Binary object recognition Descriptors Subject Headings Integrate Spatial resolution binary string

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Object-aware Dense Semantic Correspondence 30

Object-aware Dense Semantic Correspondence

引用

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

作者： Yang, Fan Li, Xin Cheng, Hong Li, Jianping Chen, Leiting UESTC Sch Comp Sci & Engn Chengdu Sichuan Peoples R China UESTC Sch Automat Engn Ctr Robot Chengdu Sichuan Peoples R China

ISBN: (纸本)9781538604571

this work aims to build pixel-to-pixel correspondences between images from the same visual class but with different geometries and visual similarities. this task is particularly challenging because (i) their visual content is similar only on the high-level structure, and (ii) background clutters keep bringing in noises. To address these problems, this paper proposes an object-aware method to estimate per-pixel correspondences from semantic to low-level by learning a classifier for each selected discriminative grid cell and guiding the localization of every pixel under the semantic constraint. Specifically, an Object-aware Hierarchical Graph (OHG) model is constructed to regulate matching consistency from one coarse grid cell containing whole object(s), to fine grid cells covering smaller semantic elements, and finally to every pixel. A guidance layer is introduced as the semantic constraint on local structure matching. In addition, we propose to learn the important high-level structure for each grid cell in an "objectness-driven" way as an alternative to hand-crafted descriptors in defining a better visual similarity. the proposed method has been extensively evaluated on various challenging benchmarks and real-world images. the results show that our method significantly outperforms the state-of-the-arts in terms of semantic flow accuracy.

关键词： Semantics Proposals Clutter Visualization computer architecture computer vision

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Switching Convolutional Neural Network for Crowd Counting 30

Switching Convolutional Neural Network for Crowd Counting

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

作者： Sam, Deepak Babu Surya, Shiv Babu, R. Venkatesh Indian Inst Sci Bangalore 560012 Karnataka India

ISBN: (纸本)9781538604571

We propose a novel crowd counting model that maps a given crowd scene to its density. Crowd analysis is compounded by myriad of factors like inter-occlusion between people due to extreme crowding, high similarity of appearance between people and background elements, and large variability of camera view-points. Current state-of-the art approaches tackle these factors by using multi-scale CNN architectures, recurrent networks and late fusion of features from multi-column CNN with different receptive fields. We propose switching convolutional neural network that leverages variation of crowd density within an image to improve the accuracy and localization of the predicted crowd count. Patches from a grid within a crowd scene are relayed to independent CNN regressors based on crowd count prediction quality of the CNN established during training. the independent CNN regressors are designed to have different receptive fields and a switch classifier is trained to relay the crowd scene patch to the best CNN regressor. We perform extensive experiments on all major crowd counting datasets and evidence better performance compared to current state-of-the-art methods. We provide interpretable representations of the multichotomy of space of crowd scene patches inferred from the switch. It is observed that the switch relays an image patch to a particular CNN column based on density of crowd.

关键词： Switches Training computer architecture Relays Head computer vision Neural networks

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Learning Adaptive Receptive Fields for Deep Image Parsing Network 30

Learning Adaptive Receptive Fields for Deep Image Parsing Ne...

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

作者： Wei, Zhen Sun, Yao Wang, Jinqiao Lai, Hanjiang Liu, Si Chinese Acad Sci Inst Informat Engn State Key Lab Informat Secur SKLOIS Beijing 100093 Peoples R China Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing 100190 Peoples R China Sun Yat Sen Univ Sch Data & Comp Sci Guangzhou 510275 Guangdong Peoples R China Univ Chinese Acad Sci Beijing 101408 Peoples R China

ISBN: (纸本)9781538604571

In this paper, we introduce a novel approach to regulate receptive field in deep image parsing network automatically. Unlike previous works which have stressed much importance on obtaining better receptive fields using manually selected dilated convolutional kernels, our approach uses two affine transformation layers in the network's backbone and operates on feature maps. Feature maps will be inflated/shrinked by the new layer and therefore receptive fields in following layers are changed accordingly. By end-to-end training, the whole framework is data-driven without laborious manual intervention. the proposed method is generic across dataset and different tasks. We conduct extensive experiments on both general image parsing task and face parsing task as concrete examples to demonstrate the method's superior regulation ability over manual designs.

关键词： Kernel Interpolation Training Manuals Face

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CERN: Confidence-Energy Recurrent Network for Group Activity recognition 30

CERN: Confidence-Energy Recurrent Network for Group Activity...

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

作者： Shu, Tianmin Todorovic, Sinisa Zhu, Song-Chun Univ Calif Los Angeles Los Angeles CA 90024 USA Oregon State Univ Corvallis OR 97331 USA

ISBN: (纸本)9781538604571

this work is about recognizing human activities occurring in videos at distinct semantic levels, including individual actions, interactions, and group activities. the recognition is realized using a two-level hierarchy of Long Short-Term Memory (LSTM) networks, forming a feed-forward deep architecture, which can be trained end-to-end. In comparison with existing architectures of LSTMs, we make two key contributions giving the name to our approach as Confidence-Energy Recurrent Network - CERN. First, instead of using the common softmax layer for prediction, we specify a novel energy layer (EL) for estimating the energy of our predictions. Second, rather than finding the common minimum-energy class assignment, which may be numerically unstable under uncertainty, we specify that the EL additionally computes the p-values of the solutions, and in this way estimates the most confident energy minimum. the evaluation on the Collective Activity and Volleyball datasets demonstrates: (i) advantages of our two contributions relative to the common softmax and energy-minimization formulations and (ii) a superior performance relative to the state-of-the-art approaches.

关键词： recurrent network CERN end-to-end Videotapes Human Activities p-Value ENERGY MINIMISATION

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Deep TEN: Texture Encoding Network 30

Deep TEN: Texture Encoding Network

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

作者： Zhang, Hang Xue, Jia Dana, Kristin Rutgers State Univ Dept Elect & Comp Engn New Brunswick NJ 08901 USA

ISBN: (纸本)9781538604571

We propose a Deep Texture Encoding Network (DeepTEN) with a novel Encoding Layer integrated on top of convolutional layers, which ports the entire dictionary learning and encoding pipeline into a single model. Current methods build from distinct components, using standard encoders with separate off-the-shelf features such as SIFT descriptors or pre-trained CNN features for material recognition. Our new approach provides an end-to-end learning framework, where the inherent visual vocabularies are learned directly from the loss function. the features, dictionaries, encoding representation and the classifier are all learned simultaneously. the representation is orderless and therefore is particularly useful for material and texture recognition. the Encoding Layer generalizes robust residual encoders such as VLAD and Fisher Vectors, and has the property of discarding domain specific information which makes the learned convolutional features easier to transfer. Additionally, joint training using multiple datasets of varied sizes and class labels is supported resulting in increased recognition performance. the experimental results show superior performance as compared to state-of-the-art methods using gold-standard databases such as MINC-2500, Flickr Material Database, Kth-TIPS-2b, and two recent databases 4D-Light-Field-Material and GTOS. the source code for the complete system are publicly available1.

关键词： Source code texture ENCODER Dictionaries as Topic Classifiers PRINT textures recognition (Psychology) computer AIDED POLYMER SELECTION coding

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Analyzing computer vision Data - the Good, the Bad and the Ugly 30

Analyzing Computer Vision Data - The Good, the Bad and the U...

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

作者： Zendel, Oliver Honauer, Katrin Murschitz, Markus Humenberger, Martin Dominguez, Gustavo Fernandez Austrian Inst Technol Donau City Str 1 A-1220 Vienna Austria Heidelberg Univ IWR HCI Berliner Str 43 D-69120 Heidelberg Germany

ISBN: (纸本)9781538604571

In recent years, a great number of datasets were published to train and evaluate computer vision (CV) algorithms. these valuable contributions helped to push CV solutions to a level where they can be used for safety-relevant applications, such as autonomous driving. However, major questions concerning quality and usefulness of test data for CV evaluation are still unanswered. Researchers and engineers try to cover all test cases by using as much test data as possible. In this paper, we propose a different solution for this challenge. We introduce a method for dataset analysis which builds upon an improved version of the CV-HAZOP checklist, a list of potential hazards within the CV domain. Picking stereo vision as an example, we provide an extensive survey of 28 datasets covering the last two decades. We create a tailored checklist and apply it to the datasets Middlebury, KITTI, Sintel, Freiburg, and HCI to present a thorough characterization and quantitative comparison. We confirm the usability of our checklist for identification of challenging stereo situations by applying nine state-of-the-art stereo matching algorithms on the analyzed datasets, showing that hazard frames correlate with difficult frames. We show that challenging datasets still allow a meaningful algorithm evaluation even for small subsets. Finally, we provide a list of missing test cases that are still not covered by current datasets as inspiration for researchers who want to participate in future dataset creation.

关键词： Analyzing computer vision CV CV-HAZOP

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Noisy Softmax: Improving the Generalization Ability of DCNN via Postponing the Early Softmax Saturation 30

Noisy Softmax: Improving the Generalization Ability of DCNN ...

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

作者： Chen, Binghui Deng, Weihong Du, Junping Beijing Univ Posts & Telecommun Sch Informat & Commun Engn Beijing Peoples R China Beijing Univ Posts & Telecommun Sch Comp Sci Beijing Peoples R China

ISBN: (纸本)9781538604571

Over the past few years, softmax and SGD have become a commonly used component and the default training strategy in CNN frameworks, respectively. However, when optimizing CNNs with SGD, the saturation behavior behind softmax always gives us an illusion of training well and then is omitted. In this paper, we first emphasize that the early saturation behavior of softmax will impede the exploration of SGD, which sometimes is a reason for model converging at a bad local-minima, then propose Noisy Softmax to mitigating this early saturation issue by injecting annealed noise in softmax during each iteration. this operation based on noise injection aims at postponing the early saturation and further bringing continuous gradients propagation so as to significantly encourage SGD solver to be more exploratory and help to find a better local-minima. this paper empirically verifies the superiority of the early softmax desaturation, and our method indeed improves the generalization ability of CNN model by regularization. We experimentally find that this early desaturation helps optimization in many tasks, yielding state-of-the-art or competitive results on several popular benchmark datasets.

关键词： Training Noise measurement Annealing Standards Robustness computer vision Telecommunications

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Video Captioning with Transferred Semantic Attributes 30

Video Captioning with Transferred Semantic Attributes

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

作者： Pan, Yingwei Yao, Ting Li, Houqiang Mei, Tao Univ Sci & Technol China Hefei Anhui Peoples R China Microsoft Res Beijing Peoples R China

ISBN: (纸本)9781538604571

Automatically generating natural language descriptions of videos plays a fundamental challenge for computer vision community. Most recent progress in this problem has been achieved through employing 2-D and/or 3-D Convolutional Neural Networks (CNNs) to encode video content and Recurrent Neural Networks (RNNs) to decode a sentence. In this paper, we present Long Short-Term Memory with Transferred Semantic Attributes (LSTM-TSA)-a novel deep architecture that incorporates the transferred semantic attributes learnt from images and videos into the CNN plus RNN framework, by training them in an end-to-end manner. the design of LSTM-TSA is highly inspired by the facts that 1) semantic attributes play a significant contribution to captioning, and 2) images and videos carry complementary semantics and thus can reinforce each other for captioning. To boost video captioning, we propose a novel transfer unit to model the mutually correlated attributes learnt from images and videos. Extensive experiments are conducted on three public datasets, i.e., MSVD, M-VAD and MPIIMD. Our proposed LSTM-TSA achieves to-date the best published performance in sentence generation on MSVD: 52.8% and 74.0% in terms of BLEU@4 and CIDEr-D. Superior results are also reported on M-VAD and MPII-MD when compared to state-of-the-art methods.

关键词： Semantics Visualization computer architecture computer vision Natural languages Recurrent neural networks Probability distribution

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Fine-grained Image Classification via Combining vision and Language 30

Fine-grained Image Classification via Combining Vision and L...

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

作者： He, Xiangteng Peng, Yuxin Peking Univ Inst Comp Sci & Technol Beijing Peoples R China

ISBN: (纸本)9781538604571

Fine-grained image classification is a challenging task due to the large intra-class variance and small inter-class variance, aiming at recognizing hundreds of sub-categories belonging to the same basic-level category. Most existing fine-grained image classification methods generally learn part detection models to obtain the semantic parts for better classification accuracy. Despite achieving promising results, these methods mainly have two limitations: (1) not all the parts which obtained through the part detection models are beneficial and indispensable for classification, and (2) fine-grained image classification requires more detailed visual descriptions which could not be provided by the part locations or attribute annotations. For addressing the above two limitations, this paper proposes the two-stream model combining vision and language (CVL) for learning latent semantic representations. the vision stream learns deep representations from the original visual information via deep convolutional neural network. the language stream utilizes the natural language descriptions which could point out the discriminative parts or characteristics for each image, and provides a flexible and compact way of encoding the salient visual aspects for distinguishing sub-categories. Since the two streams are complementary, combining the two streams can further achieves better classification accuracy. Comparing with 12 state-of-the-art methods on the widely used CUB-200-2011 dataset for fine-grained image classification, the experimental results demonstrate our CVL approach achieves the best performance.

关键词： Visualization Streaming media Natural languages Semantics Feature extraction Image recognition

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