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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411-420 订阅

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Attend to You: Personalized Image Captioning with Context Sequence Memory Networks 30

<i>Attend to You</i>: Personalized Image Captioning with Con...

引用

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

作者： Park, Cesc Chunseong Kim, Byeongchang Kim, Gunhee Lunit Inc Seoul South Korea Seoul Natl Univ Seoul South Korea

ISBN: (纸本)9781538604571

We address personalization issues of image captioning, which have not been discussed yet in previous research. For a query image, we aim to generate a descriptive sentence, accounting for prior knowledge such as the users active vocabularies in previous documents. As applications of personalized image captioning, we tackle two post automation tasks: hashtag prediction and post generation, on our newly collected Instagram dataset, consisting of 1.1 M posts from 6.3 K users. We propose a novel captioning model named Context Sequence Memory Network (CSMN). Its unique updates over previous memory network models include (i) exploiting memory as a repository for multiple types of context information, (ii) appending previously generated words into memory to capture long-term information without suffering from the vanishing gradient problem, and (iii) adopting CNN memory structure to jointly represent nearby ordered memory slots for better context understanding. With quantitative evaluation and user studies via Amazon Mechanical Turk, we show the effectiveness of the three novel features of CSMN and its performance enhancement for personalized image captioning over state-of-the-art captioning models.

关键词： conferences computer vision pattern recognition

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Modeling Relationships in Referential Expressions with Compositional Modular Networks 30

Modeling Relationships in Referential Expressions with Compo...

引用

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

作者： Hu, Ronghang Rohrbach, Marcus Andreas, Jacob Darrell, Trevor Saenko, Kate Univ Calif Berkeley Berkeley CA 94720 USA Boston Univ Boston MA 02215 USA

ISBN: (纸本)9781538604571

People often refer to entities in an image in terms of their relationships with other entities. For example, the black cat sitting under the table refers to both a black cat entity and its relationship with another table entity. Understanding these relationships is essential for interpreting and grounding such natural language expressions. Most prior work focuses on either grounding entire referential expressions holistically to one region, or localizing relationships based on a fixed set of categories. In this paper we instead present a modular deep architecture capable of analyzing referential expressions into their component parts, identifying entities and relationships mentioned in the input expression and grounding them all in the scene. We call this approach Compositional Modular Networks (CMNs): a novel architecture that learns linguistic analysis and visual inference end-to-end. Our approach is built around two types of neural modules that inspect local regions and pairwise interactions between regions. We evaluate CMNs on multiple referential expression datasets, outperforming state-of-the-art approaches on all tasks.

关键词： Visualization Grounding Feature extraction Natural languages computer vision Cats

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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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All You Need is Beyond a Good Init: Exploring Better Solution for Training Extremely Deep Convolutional Neural Networks with Orthonormality and Modulation 30

All You Need is Beyond a Good Init: Exploring Better Solutio...

引用

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

作者： Xie, Di Xiong, Jiang Pu, Shiliang Hikvis Res Inst Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9781538604571

Deep neural network is difficult to train and this predicament becomes worse as the depth increases. the essence of this problem exists in the magnitude of backpropagated errors that will result in gradient vanishing or exploding phenomenon. We show that a variant of regularizer which utilizes orthonormality among different filter banks can alleviate this problem. Moreover, we design a backward error modulation mechanism based on the quasi-isometry assumption between two consecutive parametric layers. Equipped with these two ingredients, we propose several novel optimization solutions that can be utilized for training a specific-structured (repetitively triple modules of Conv-BNReLU) extremely deep convolutional neural network (CNN) WIthOUT any shortcuts/identity mappings from scratch. Experiments show that our proposed solutions can achieve distinct improvements for a 44-layer and a 110-layer plain networks on both the CIFAR-10 and ImageNet datasets. Moreover, we can successfully train plain CNNs to match the performance of the residual counterparts. Besides, we propose new principles for designing network structure from the insights evoked by orthonormality. Combined with residual structure, we achieve comparative performance on the ImageNet dataset.

关键词： Modulation Neural networks Training Jacobian matrices Convolution computer vision Network architecture

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Unsupervised part learning for visual recognition 30

Unsupervised part learning for visual recognition

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

作者： Sicre, Ronan Avrithis, Yannis Kijak, Ewa Jurie, Frederic INRIA IRISA Rennes France Normandie Univ UNICAEN ENSICAEN CNRS UMR GREYC Caen France

ISBN: (纸本)9781538604571

Part-based image classification aims at representing categories by small sets of learned discriminative parts, upon which an image representation is built. Considered as a promising avenue a decade ago, this direction has been neglected since the advent of deep neural networks. In this context, this paper brings two contributions: first, this work proceeds one step further compared to recent part-based models (PBM), focusing on how to learn parts without using any labeled data. Instead of learning a set of parts per class, as generally performed in the PBM literature, the proposed approach both constructs a partition of a given set of images into visually similar groups, and subsequently learns a set of discriminative parts per group in a fully unsupervised fashion. this strategy opens the door to the use of PBM in new applications where labeled data are typically not available, such as instance-based image retrieval. Second, this paper shows that despite the recent success of end-to-end models, explicit part learning can still boost classification performance. We experimentally show that our learned parts can help building efficient image representations, which outperform state-of-the art Deep Convolutional Neural Networks (DCNN) on both classification and retrieval tasks.

关键词： image representation phytobacteriomycin Image classification image retrieval end-to-end DOOR PART Neural network

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Multiple Instance Detection Network with Online Instance Classifier Refinement 30

Multiple Instance Detection Network with Online Instance Cla...

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

作者： Tang, Peng Wang, Xinggang Bai, Xiang Liu, Wenyu Huazhong Univ Sci & Technol Sch EIC Wuhan Hubei Peoples R China

ISBN: (纸本)9781538604571

Of late, weakly supervised object detection is with great importance in object recognition. Based on deep learning, weakly supervised detectors have achieved many promising results. However, compared with fully supervised detection, it is more challenging to train deep network based detectors in a weakly supervised manner. Here we formulate weakly supervised detection as a Multiple Instance Learning (MIL) problem, where instance classifiers (object detectors) are put into the network as hidden nodes. We propose a novel online instance classifier refinement algorithm to integrate MIL and the instance classifier refinement procedure into a single deep network, and train the network end-to-end with only image-level supervision, i.e., without object location information. More precisely, instance labels inferred from weak supervision are propagated to their spatially overlapped instances to refine instance classifier online. the iterative instance classifier refinement procedure is implemented using multiple streams in deep network, where each stream supervises its latter stream. Weakly supervised object detection experiments are carried out on the challenging PASCAL VOC 2007 and 2012 benchmarks. We obtain 47% mAP on VOC 2007 that significantly outperforms the previous state-of-the-art.

关键词： Classifiers Object detection streams Refining object recognition detector Network

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End-to-end Learning of Driving Models from Large-scale Video Datasets 30

End-to-end Learning of Driving Models from Large-scale Video...

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

作者： Xu, Huazhe Gao, Yang Yu, Fisher Darrell, Trevor Univ Calif Berkeley Berkeley CA 94720 USA

ISBN: (纸本)9781538604571

Robust perception-action models should be learned from training data with diverse visual appearances and realistic behaviors, yet current approaches to deep visuomotor policy learning have been generally limited to in-situ models learned from a single vehicle or simulation environment. We advocate learning a generic vehicle motion model from large scale crowd-sourced video data, and develop an end-to-end trainable architecture for learning to predict a distribution over future vehicle egomotion from instantaneous monocular camera observations and previous vehicle state. Our model incorporates a novel FCN-LSTM architecture, which can be learned from large-scale crowd-sourced vehicle action data, and leverages available scene segmentation side tasks to improve performance under a privileged learning paradigm. We provide a novel large-scale dataset of crowd-sourced driving behavior suitable for training our model, and report results predicting the driver action on held out sequences across diverse conditions.

关键词： Predictive models Visualization Data models computer architecture Motion segmentation Training

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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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the VQA-Machine: Learning How to Use Existing vision Algorithms to Answer New Questions 30

The VQA-Machine: Learning How to Use Existing Vision Algorit...

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

作者： Wang, Peng Wu, Qi Shen, Chunhua van den Hengel, Anton Northwestern Polytech Univ Xian Shaanxi Peoples R China Univ Adelaide Adelaide SA Australia Australian Ctr Robot Vis Brisbane Qld Australia

ISBN: (纸本)9781538604571

One of the most intriguing features of the Visual Question Answering (VQA) challenge is the unpredictability of the questions. Extracting the information required to answer them demands a variety of image operations from detection and counting, to segmentation and reconstruction. To train a method to perform even one of these operations accurately from {image, question, answer} tuples would be challenging, but to aim to achieve them all with a limited set of such training data seems ambitious at best. We propose here instead a more general and scalable approach which exploits the fact that very good methods to achieve these operations already exist, and thus do not need to be trained. Our method thus learns how to exploit a set of external off-the-shelf algorithms to achieve its goal, an approach that has something in common with the Neural Turing Machine [10]. the core of our proposed method is a new co-attention model. In addition, the proposed approach generates human-readable reasons for its decision, and can still be trained end-to-end without ground truth reasons being given. We demonstrate the effectiveness on two publicly available datasets, Visual Genome and VQA, and show that it produces the state-of-the-art results in both cases.

关键词： Visualization Cognition Prediction algorithms Neural networks Data mining Telescopes

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Incremental Kernel Null Space Discriminant Analysis for Novelty Detection 30

Incremental Kernel Null Space Discriminant Analysis for Nove...

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

作者： Liu, Juncheng Lian, Zhouhui Wang, Yi Xiao, Jianguo Peking Univ Inst Comp Sci & Technol Beijing Peoples R China Dalian Univ Technol Sch Software Dalian Peoples R China Key Lab Ubiquitous Network & Serv Software Liaoni Dalian Peoples R China

ISBN: (纸本)9781538604571

Novelty detection, which aims to determine whether a given data belongs to any category of training data or not, is considered to be an important and challenging problem in areas of pattern recognition, Machine Learning, etc. Recently, kernel null space method (KNDA) was reported to have state-of-the-art performance in novelty detection. However, KNDA is hard to scale up because of its high computational cost. With the ever-increasing size of data, accelerating the implementing speed of KNDA is desired and critical. Moreover, it becomes incapable when there exist successively injected data. To address these issues, we propose the Incremental Kernel Null Space based Discriminant Analysis (IKNDA) algorithm. the key idea is to extract new information brought by newly-added samples and integrate it with the existing model by an efficient updating scheme. Experiments conducted on two publicly-available datasets demonstrate that the proposed IKNDA yields comparable performance as the batch KNDA yet significantly reduces the computational complexity, and our IKNDA based novelty detection methods markedly outperform approaches using deep neural network (DNN) classifiers. this validates the superiority of our IKNDA against the state of the art in novelty detection for large-scale data.

关键词： Null space Algorithm design and analysis pattern recognition Training data Training computer vision

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