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Conditional Similarity Networks 30

Conditional Similarity Networks

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

作者： Veit, Andreas Belongie, Serge Karaletsos, theofanis Cornell Univ Dept Comp Sci Ithaca NY 14853 USA Cornell Univ Cornell Tech Ithaca NY 14853 USA Uber AI Labs Ithaca NY USA Sloan Kettering Inst Computat Biol New York NY USA

ISBN: (纸本)9781538604571

What makes images similar? To measure the similarity between images, they are typically embedded in a feature-vector space, in which their distance preserve the relative dissimilarity. However, when learning such similarity embeddings the simplifying assumption is commonly made that images are only compared to one unique measure of similarity. A main reason for this is that contradicting notions of similarities cannot be captured in a single space. To address this shortcoming, we propose Conditional Similarity Networks (CSNs) that learn embeddings differentiated into semantically distinct subspaces that capture the different notions of similarities. CSNs jointly learn a disentangled embedding where features for different similarities are encoded in separate dimensions as well as masks that select and reweight relevant dimensions to induce a subspace that encodes a specific similarity notion. We show that our approach learns interpretable image representations with visually relevant semantic subspaces. Further, when evaluating on triplet questions from multiple similarity notions our model even outperforms the accuracy obtained by training individual specialized networks for each notion separately.

关键词： Visualization Semantics Neural networks computer vision Kernel Extraterrestrial measurements Extraterrestrial measurements images Semantics computer vision Neural network Visualization Subspace Kernel image representation similar measure semiotics Conditional Analogy Seeds

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A New Representation of Skeleton Sequences for 3D Action recognition 30

A New Representation of Skeleton Sequences for 3D Action Rec...

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

作者： Ke, Qiuhong Bennamoun, Mohammed An, Senjian Sohel, Ferdous Boussaid, Farid Univ Western Australia Nedlands WA Australia Murdoch Univ Murdoch WA Australia

ISBN: (纸本)9781538604571

this paper presents a new method for 3D action recognition with skeleton sequences (i.e., 3D trajectories of human skeleton joints). the proposed method first transforms each skeleton sequence into three clips each consisting of several frames for spatial temporal feature learning using deep neural networks. Each clip is generated from one channel of the cylindrical coordinates of the skeleton sequence. Each frame of the generated clips represents the temporal information of the entire skeleton sequence, and incorporates one particular spatial relationship between the joints. the entire clips include multiple frames with different spatial relationships, which provide useful spatial structural information of the human skeleton. We propose to use deep convolutional neural networks to learn long-term temporal information of the skeleton sequence from the frames of the generated clips, and then use a Multi-Task Learning Network (MTLN) to jointly process all frames of the generated clips in parallel to incorporate spatial structural information for action recognition. Experimental results clearly show the effectiveness of the proposed new representation and feature learning method for 3D action recognition.

关键词： Skeletons bone structure clipping Clip Human Remains FRAMES time information Frame CYLINDRICAL COORDINATES recognition (Psychology) Information recognition

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Hard Mixtures of Experts for Large ScaleWeakly Supervised vision 30

Hard Mixtures of Experts for Large ScaleWeakly Supervised Vi...

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

作者： Gross, Sam Ranzato, Marc'Aurelio Szlam, Arthur Facebook AI Res Menlo Pk CA USA

ISBN: (纸本)9781538604571

Training convolutional networks (CNN's) that fit on a single GPU with minibatch stochastic gradient descent has become effective in practice. However, there is still no effective method for training large CNN's that do not fit in the memory of a few GPU cards, or for parallelizing CNN training. In this work we show that a simple hard mixture of experts model can be efficiently trained to good effect on large scale hashtag (multilabel) prediction tasks. Mixture of experts models are not new [7, 3], but in the past, researchers have had to devise sophisticated methods to deal with data fragmentation. We show empirically that modern weakly supervised data sets are large enough to support naive partitioning schemes where each data point is assigned to a single expert. Because the experts are independent, training them in parallel is easy, and evaluation is cheap for the size of the model. Furthermore, we show that we can use a single decoding layer for all the experts, allowing a unified feature embedding space. We demonstrate that it is feasible (and in fact relatively painless) to train far larger models than could be practically trained with standard CNN architectures, and that the extra capacity can be well used on current datasets.

关键词： Professional personnel model size Dataset Functional training GRAPPER PICK UP Graphics Processing Unit expert model

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Unambiguous Text Localization and Retrieval for Cluttered Scenes 30

Unambiguous Text Localization and Retrieval for Cluttered Sc...

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

作者： Rong, Xuejian Yi, Chucai Tian, Yingli CUNY City Coll New York NY 10031 USA HERE North Amer LLC Chicago IL USA

ISBN: (纸本)9781538604571

Text instance as one category of self-described objects provides valuable information for understanding and describing cluttered scenes. In this paper, we explore the task of unambiguous text localization and retrieval, to accurately localize a specific targeted text instance in a cluttered image given a natural language description that refers to it. To address this issue, first a novel recurrent Dense Text Localization Network (DTLN) is proposed to sequentially decode the intermediate convolutional representations of a cluttered scene image into a set of distinct text instance detections. Our approach avoids repeated detections at multiple scales of the same text instance by recurrently memorizing previous detections, and effectively tackles crowded text instances in close proximity. Second, we propose a Context Reasoning Text Retrieval (CRTR) model, which jointly encodes text instances and their context information through a recurrent network, and ranks localized text bounding boxes by a scoring function of context compatibility. Quantitative evaluations on standard scene text localization benchmarks and a newly collected scene text retrieval dataset demonstrate the effectiveness and advantages of our models for both scene text localization and retrieval.

关键词： Decoding Natural languages Convolutional codes Data mining Context modeling Text recognition

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From Zero-shot Learning to Conventional Supervised Classification: Unseen Visual Data Synthesis 30

From Zero-shot Learning to Conventional Supervised Classific...

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

作者： Long, Yang Liu, Li Shao, Ling Shen, Fumin Ding, Guiguang Han, Jungong Univ Sheffield Dept Elect & Elect Engn Sheffield S Yorkshire England Univ East Anglia Sch Comp Sci Norwich Norfolk England Univ Elect Sci & Technol China Ctr Future Media Chengdu Sichuan Peoples R China Tsinghua Univ Sch Software Beijing Peoples R China Northumbria Univ Dept Comp Sci & Digital Technol Newcastle Upon Tyne Tyne & Wear England

ISBN: (纸本)9781538604571

Robust object recognition systems usually rely on powerful feature extraction mechanisms from a large number of real images. However, in many realistic applications, collecting sufficient images for ever-growing new classes is unattainable. In this paper, we propose a new Zero-shot learning (ZSL) framework that can synthesise visual features for unseen classes without acquiring real images. Using the proposed Unseen Visual Data Synthesis (UVDS) algorithm, semantic attributes are effectively utilised as an intermediate clue to synthesise unseen visual features at the training stage. Hereafter, ZSL recognition is converted into the conventional supervised problem, i.e. the synthesised visual features can be straightforwardly fed to typical classifiers such as SVM. On four benchmark datasets, we demonstrate the benefit of using synthesised unseen data. Extensive experimental results suggest that our proposed approach significantly improve the state-of-the-art results.

关键词： Visualization Semantics Training Predictive models Feature extraction computer vision Visualization images Feature extraction Semantics Prediction models computer vision Functional training semiotics Training New classes

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Convex Global 3D Registration with Lagrangian Duality 30

Convex Global 3D Registration with Lagrangian Duality

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

作者： Briales, Jesus Gonzalez-Jimenez, Javier Univ Malaga MAPIR UMA Grp Malaga Spain

ISBN: (纸本)9781538604571

the registration of 3D models by a Euclidean transformation is a fundamental task at the core of many application in computer vision. this problem is non- convex due to the presence of rotational constraints, making traditional local optimization methods prone to getting stuck in local minima. this paper addresses finding the globally optimal transformation in various 3D registration problems by a unified formulation that integrates common geometric registration modalities (namely point-to-point, point-to-line and point-to-plane). this formulation renders the optimization problem independent of both the number and nature of the correspondences. the main novelty of our proposal is the introduction of a strengthened Lagrangian dual relaxation for this problem, which surpasses previous similar approaches [32] in effectiveness. In fact, even though with no theoretical guarantees, exhaustive empirical evaluation in both synthetic and real experiments always resulted on a tight relaxation that allowed to recover a guaranteed globally optimal solution by exploiting duality theory. thus, our approach allows for effectively solving the 3D registration with global optimality guarantees while running at a fraction of the time for the state-of-the-art alternative [34], based on a more computationally intensive Branch and Bound method.

关键词： Registration images computer vision duality theory Branch and bound methods Lagrangian Non-Convex three Dimensions point to point communication

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UntrimmedNets for Weakly Supervised Action recognition and Detection 30

UntrimmedNets for Weakly Supervised Action Recognition and D...

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

作者： Wang, Limin Xiong, Yuanjun Lin, Dahua Van Gool, Luc Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland Chinese Univ Hong Kong Dept Informat Engn Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781538604571

Current action recognition methods heavily rely on trimmed videos for model training. However, it is expensive and time-consuming to acquire a large-scale trimmed video dataset. this paper presents a new weakly supervised architecture, called UntrimmedNet, which is able to directly learn action recognition models from untrimmed videos without the requirement of temporal annotations of action instances. Our UntrimmedNet couples two important components, the classification module and the selection module, to learn the action models and reason about the temporal duration of action instances, respectively. these two components are implemented with feed-forward networks, and UntrimmedNet is therefore an end-to-end trainable architecture. We exploit the learned models for action recognition (WSR) and detection (WSD) on the untrimmed video datasets of thUMOS14 and ActivityNet. Although our UntrimmedNet only employs weak supervision, our method achieves performance superior or comparable to that of those strongly supervised approaches on these two datasets.

关键词： Videos Proposals Feature extraction Training Motion pictures Adaptation models Visualization Videotapes Cinema Feature extraction Visualization Functional training Films Adaptation models sort module recognition (Psychology) Training Dataset Action

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A Matrix Splitting Method for Composite Function Minimization 30

A Matrix Splitting Method for Composite Function Minimizatio...

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

作者： Yuan, Ganzhao Zheng, Wei-Shi Ghanem, Bernard KAUST Thuwal Saudi Arabia SYSU Sch Data & Comp Sci Guangzhou Guangdong Peoples R China Sun Yat Sen Univ Minist Educ Key Lab Machine Intelligence & Adv Comp Guangzhou Guangdong Peoples R China

ISBN: (纸本)9781538604571

Composite function minimization captures a wide spectrum of applications in both computer vision and machine learning. It includes bound constrained optimization and cardinality regularized optimization as special cases. this paper proposes and analyzes a new Matrix Splitting Method (MSM) for minimizing composite functions. It can be viewed as a generalization of the classical GaussSeidel method and the Successive Over-Relaxation method for solving linear systems in the literature. Incorporating a new Gaussian elimination procedure, the matrix splitting method achieves state-of-the-art performance. For convex problems, we establish the global convergence, convergence rate, and iteration complexity of MSM, while for non-convex problems, we prove its global convergence. Finally, we validate the performance of our matrix splitting method on two particular applications: nonnegative matrix factorization and cardinality regularized sparse coding. Extensive experiments show that our method outperforms existing composite function minimization techniques in term of both efficiency and efficacy.

关键词： composite functions FUNCTION MINIMISATION Spectra applications base number MSM matrix decomposition matrices Non-Convex Linear system global convergence images computer vision Gaussian elimination

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AMVH: Asymmetric Multi-Valued Hashing 30

AMVH: Asymmetric Multi-Valued Hashing

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

作者： Da, Cheng Xu, Shibiao Ding, Kun Meng, Gaofeng Xiang, Shiming Pan, Chunhong Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing 100864 Peoples R China Univ Chinese Acad Sci Sch Comp & Control Engn Beijing Peoples R China

ISBN: (纸本)9781538604571

Most existing hashing methods resort to binary codes for similarity search, owing to the high efficiency of computation and storage. However, binary codes lack enough capability in similarity preservation, resulting in less desirable performance. To address this issue, we propose an asymmetric multi-valued hashing method supported by two different non-binary embeddings. (1) A real-valued embedding is used for representing the newly-coming query. (2) A multi-integer-embedding is employed for compressing the whole database, which is modeled by binary sparse representation with fixed sparsity. With these two non-binary embeddings, the similarities between data points can be preserved precisely. To perform meaningful asymmetric similarity computation for efficient semantic search, these embeddings are jointly learnt by preserving the label-based similarity. Technically, this results in a mixed integer programming problem, which is efficiently solved by alternative optimization. Extensive experiments on three multilabel datasets demonstrate that our approach not only outperforms the existing binary hashing methods in search accuracy, but also retains their query and storage efficiency.

关键词： hashing binary code hashing method

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Quad-networks: unsupervised learning to rank for interest point detection 30

Quad-networks: unsupervised learning to rank for interest po...

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

作者： Savinov, Nikolay Seki, Akihito Ladicky, L'ubor Sattler, Torsten Pollefeys, Marc Swiss Fed Inst Technol Dept Comp Sci Zurich Switzerland Toshiba Co Ltd Tokyo Japan Microsoft Redmond WA USA

ISBN: (纸本)9781538604571

Several machine learning tasks require to represent the data using only a sparse set of interest points. An ideal detector is able to find the corresponding interest points even if the data undergo a transformation typical for a given domain. Since the task is of high practical interest in computer vision, many hand-crafted solutions were proposed. In this paper, we ask a fundamental question: can we learn such detectors from scratch? Since it is often unclear what points are "interesting", human labelling cannot be used to find a truly unbiased solution. therefore, the task requires an unsupervised formulation. We are the first to propose such a formulation: training a neural network to rank points in a transformation-invariant manner. Interest points are then extracted from the top/bottom quantiles of this ranking. We validate our approach on two tasks: standard RGB image interest point detection and challenging cross-modal interest point detection between RGB and depth images. We quantitatively show that our unsupervised method performs better or on-par with baselines.

关键词： Detectors Dogs three-dimensional displays Unsupervised learning Training Unsupervised learning three-dimensional displays Corner detection images Functional training computer vision Canis familiaris detector red-green-blue colour model Training BASELINE

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