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Bidirectional Retrieval Made Simple 31

Bidirectional Retrieval Made Simple

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31st ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Wehrmann, Jonatas Barros, Rodrigo C. Pontiffcia Univ Catolica Rio Grande do Sul Sch Technol Porto Alegre RS Brazil

ISBN: (纸本)9781538664209

This paper provides a very simple yet effective character-level architecture for learning bidirectional retrieval models. Aligning multimodal content is particularly challenging considering the difficulty in finding semantic correspondence between images and descriptions. We introduce an efficient character-level inception module, designed to learn textual semantic embeddings by convolving raw characters in distinct granularity levels. Our approach is capable of explicitly encoding hierarchical information from distinct base-level representations (e.g., characters, words, and sentences) into a shared multimodal space, where it maps the semantic correspondence between images and descriptions via a contrastive pairwise loss function that minimizes order-violations. Models generated by our approach are far more robust to input noise than state-of-the-art strategies based on word-embeddings. Despite being conceptually much simpler and requiring fewer parameters, our models outperform the state-of-the-art approaches by 4.8% in the task of description retrieval and 2.7% (absolute R@ 1 values) in the task of image retrieval in the popular MS COCO retrieval dataset. We also show that our models present solid performance for text classification, specially in multilingual and noisy domains.

关键词： computer architecture Semantics Training Feature extraction Task analysis Complexity theory Image coding

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Wide Compression: Tensor Ring Nets 31

Wide Compression: Tensor Ring Nets

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31st ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Wang, Wenqi Sun, Yifan Eriksson, Brian Wang, Wenlin Aggarwal, Vaneet Purdue Univ W Lafayette IN 47907 USA Technicolor Res Los Altos CA USA Adobe Los Altos CA USA Duke Univ Durham NC 27706 USA

ISBN: (纸本)9781538664209

Deep neural networks have demonstrated state-of-the-art performance in a variety of real-world applications. In order to obtain performance gains, these networks have grown larger and deeper, containing millions or even billions of parameters and over a thousand layers. The tradeoff is that these large architectures require an enormous amount of memory, storage, and computation, thus limiting their usability. Inspired by the recent tensor ring factorization, we introduce Tensor Ring Networks (TR-Nets), which significantly compress both the fully connected layers and the convolutional layers of deep neural networks. Our results show that our TR-Nets approach is able to compress LeNet-5 by 11x without losing accuracy, and can compress the state-of-the-art Wide ResNet by 243x with only 2.3% degradation in Cifar10 image classification. Overall, this compression scheme shows promise in scientific computing and deep learning, especially for emerging resource-constrained devices such as smartphones, wearables, and IoT devices.

关键词： Neural networks Image coding Shape Merging computer architecture

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Sketch-a-Classifier: Sketch-based Photo Classifier Generation 31

Sketch-a-Classifier: Sketch-based Photo Classifier Generatio...

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31st ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Hu, Conghui Li, Da Song, Yi-Zhe Xiang, Tao Hospedales, Timothy M. Queen Mary Univ London SketchX London England Univ Edinburgh Edinburgh Midlothian Scotland

ISBN: (纸本)9781538664209

Contemporary deep learning techniques have made image recognition a reasonably reliable technology. However training effective photo classifiers typically takes numerous examples which limits image recognition's scalability and applicability to scenarios where images may not be available. This has motivated investigation into zero-shot learning, which addresses the issue via knowledge transfer from other modalities such as text. In this paper we investigate an alternative approach of synthesizing image classifiers: almost directly from a user's imagination, via freehand sketch. This approach doesn't require the category to be nameable or describable via attributes as per zero-shot learning. We achieve this via training a model regression network to map from free-hand sketch space to the space of photo classifiers. It turns out that this mapping can be learned in a category-agnostic way, allowing photo classifiers for new categories to be synthesized by user with no need for annotated training photos. We also demonstrate that this modality of classifier generation can also be used to enhance the granularity of an existing photo classifier, or as a complement to name-based zero-shot learning.

关键词： Training Support vector machines Task analysis Feature extraction Birds Image recognition pattern recognition

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FOTS: Fast Oriented Text Spotting with a Unified Network 31

FOTS: Fast Oriented Text Spotting with a Unified Network

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31st ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Liu, Xuebo Liang, Ding Yan, Shi Chen, Dagui Qiao, Yu Yan, Junjie SenseTime Grp Ltd Hong Kong Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Beijing Peoples R China

ISBN: (纸本)9781538664209

Incidental scene text spotting is considered one of the most difficult and valuable challenges in the document analysis community. Most existing methods treat text detection and recognition as separate tasks. In this work, we propose a unified end-to-end trainable Fast Oriented Text Spotting (FOTS) network for simultaneous detection and recognition, sharing computation and visual information among the two complementary tasks. Specifically, RoIRotate is introduced to share convolutional features between detection and recognition. Benefiting from convolution sharing strategy, our FOTS has little computation overhead compared to baseline text detection network, and the joint training method makes our method perform better than these two-stage methods. Experiments on ICDAR 2015, ICDAR 2017 MLT, and ICDAR 2013 datasets demonstrate that the proposed method outperforms state-of-the-art methods significantly, which further allows us to develop the first real-time oriented text spotting system which surpasses all previous state-of-the-art results by more than 5% on ICDAR 2015 text spotting task while keeping 22.6 fps.

关键词： Text recognition Feature extraction Convolution Proposals Task analysis Decoding computer vision

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Generalized Zero-Shot Learning via Synthesized Examples 31

Generalized Zero-Shot Learning via Synthesized Examples

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31st ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Verma, Vinay Kumar Arora, Gundeep Mishra, Ashish Rai, Piyush Indian Inst Technol Kanpur Kanpur Uttar Pradesh India Indian Inst Technol Madras Chennai India

ISBN: (纸本)9781538664209

We present a generative framework for generalized zero shot learning where the training and test classes are not necessarily disjoint. Built upon a variational autoencoder based architecture, consisting of a probabilistic encoder and a probabilistic conditional decoder, our model can generate novel exemplars from seen/unseen classes, given their respective class attributes. These exemplars can subsequently be used to train any off-the-shelf classification model. One of the key aspects of our encoder-decoder architecture is a feedback-driven mechanism in which a discriminator (a multivariate regressor) learns to map the generated exemplars to the corresponding class attribute vectors, leading to an improved generator. Our model's ability to generate and leverage examples from unseen classes to train the classification model naturally helps to mitigate the bias towards predicting seen classes in generalized zero shot learning settings. Through a comprehensive set of experiments, we show that our model outperforms several state-of-the-art methods, on several benchmark datasets, for both standard as well as generalized zero-shot learning.

关键词： Generators Training Predictive models computer architecture Probabilistic logic Decoding Image reconstruction

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R-FCN-3000 at 30fps: Decoupling Detection and Classification 31

R-FCN-3000 at 30fps: Decoupling Detection and Classification

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31st ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Singh, Bharat Li, Hengduo Sharma, Abhishek Davis, Larry S. Univ Maryland College Pk MD 20742 USA Fudan Univ Shanghai Peoples R China Gobasco AI Labs New Delhi India

ISBN: (纸本)9781538664209

We propose a modular approach towards large-scale real-time object detection by decoupling objectness detection and classification. We exploit the fact that many object classes are visually similar and share parts. Thus, a universal objectness detector can be learned for class-agnostic object detection followed by fine-grained classification using a (non)linear classifier. Our approach is a modification of the R-FCN architecture to learn shared filters for performing localization across different object classes. We trained a detector for 3000 object classes, called R-FCN-3000, that obtains an mAP of 34.9% on the ImageNet detection dataset. It outperforms YOLO-9000 by 18% while processing 30 images per second. We also show that the objectness learned by R-FCN-3000 generalizes to novel classes and the performance increases with the number of training object classes - supporting the hypothesis that it is possible to learn a universal objectness detector. Code will be made available.

关键词： Detectors Object detection Training Proposals Convolution computer architecture Dogs

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Deep Texture Manifold for Ground Terrain recognition 31

Deep Texture Manifold for Ground Terrain Recognition

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31st ieee/cvf conference on computer vision and pattern recognition (cvpr)

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

ISBN: (纸本)9781538664209

We present a texture network called Deep Encoding Pooling Network (DEP) for the task of ground terrain recognition. recognition of ground terrain is an important task in establishing robot or vehicular control parameters, as well as for localization within an outdoor environment. The architecture of DEP integrates orderless texture details and local spatial information and the performance of DEP surpasses state-of-the-art methods for this task. The GTOS database (comprised of over 30,000 images of 40 classes of ground terrain in outdoor scenes) enables supervised recognition. For evaluation under realistic conditions, we use test images that are not from the existing GTOS dataset, but are instead from hand-held mobile phone videos of similar terrain. This new evaluation dataset, GTOS-mobile, consists of 81 videos of 31 classes of ground terrain such as grass, gravel, asphalt and sand. The resultant network shows excellent performance not only for GTOS-mobile, but also for more general databases (MINC and DTD). Leveraging the discriminant features learned from this network, we build a new texture manifold called DEP-manifold. We learn a parametric distribution in feature space in a fully supervised manner, which gives the distance relationship among classes and provides a means to implicitly represent ambiguous class boundaries. The source code and database are publicly available(1).

关键词： Encoding Manifolds Databases Videos Image recognition Feature extraction Training

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PlaneNet: Piece-wise Planar Reconstruction from a Single RGB Image 31

PlaneNet: Piece-wise Planar Reconstruction from a Single RGB...

引用

31st ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Liu, Chen Yang, Jimei Ceylan, Duygu Yumer, Ersin Furukawa, Yasutaka Washington Univ St Louis MO 63130 USA Adobe Res San Jose CA USA Argo AI Pittsburgh PA USA Simon Fraser Univ Burnaby BC Canada

ISBN: (纸本)9781538664209

This paper proposes a deep neural network (DNN) for piece-wise planar depthmap reconstruction from a single RGB image. While DNNs have brought remarkable progress to single-image depth prediction, piece-wise planar depthmap reconstruction requires a structured geometry representation, and has been a difficult task to master even for DNNs. The proposed end-to-end DNN learns to directly infer a set of plane parameters and corresponding plane segmentation masks from a single RGB image. We have generated more than 50,000 piece-wise planar depthmaps for training and testing from ScanNet, a large-scale RGBD video database. Our qualitative and quantitative evaluations demonstrate that the proposed approach outperforms baseline methods in terms of both plane segmentation and depth estimation accuracy. To the best of our knowledge, this paper presents the first end-to-end neural architecture for piece-wise planar reconstruction from a single RGB image. Code and data are available at https: //***/art-programmer/PlaneNet.

关键词： Image segmentation Image reconstruction Three-dimensional displays Task analysis Neural networks Training Probabilistic logic

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Compassionately Conservative Balanced Cuts for Image Segmentation 31

Compassionately Conservative Balanced Cuts for Image Segment...

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31st ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Cahill, Nathan D. Hayes, Tyler L. Meinhold, Renee T. Hamilton, John F. Rochester Inst Technol Rochester NY 14623 USA

ISBN: (纸本)9781538664209

The Normalized Cut (NCut) objective,function, widely used in data clustering and image segmentation, quantifies the cost of graph partitioning in a way that biases clusters or segments that are balanced towards having lower values than unbalanced partitionings. However, this bias is so strong that it avoids any singleton partitions, even when vertices are very weakly connected to the rest of the graph. Motivated by the Biihler-Heinfamily of balanced cut costs, we propose the family of Compassionately Conservative Balanced (CCB) Cut costs, which are indexed by a parameter that can he used to strike a compromise between the desire to avoid too many singleton partitions and the notion that all partitions should be balanced. fre show that CCBCut minimization can be relaxed into cm orthogonally constrained tr-minimization problem that coincides with the problem of computing Piecewise Flat Embeddings (PFE) Throne particular index value, and we present an algorithm Jr solving the relaxed problem by iteratively minimizing a sequence of reweighted Rayleigh quotients (IRRQ). Using images from the BSDS500 database, we show that image segmentation based on CCB-Cut minimization provides better accuracy with respect to ground truth and greater variability in region size than NCut-based image segmentation.

关键词： Image segmentation Minimization computer vision pattern recognition Linear programming Indexes Partitioning algorithms

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Image Super-Resolution via Dual-state Recurrent Networks 31

Image Super-Resolution via Dual-State Recurrent Networks

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31st ieee/cvf conference on computer vision and pattern recognition (cvpr)

作者： Han, Wei Chang, Shiyu Liu, Ding Yu, Mo Witbrock, Michael Huang, Thomas S. Univ Illinois Champaign IL 61820 USA IBM Res Yorktown Hts NY USA

ISBN: (纸本)9781538664209

Advances in image super-resolution (SR) have recently benefited significantly from rapid developments in deep neural networks. Inspired by these recent discoveries, we note that many state-of-the-art deep SR architectures can be reformulated as a single-state recurrent neural network (RNN) with finite unfoldings. In this paper, we explore new structures for SR based on this compact RNN view, leading us to a dual-state design, the Dual-state Recurrent Network (DSRN). Compared to its single-state counterparts that operate at a fixed spatial resolution, DSRN exploits both low-resolution (LR) and high-resolution (HR) signals jointly. Recurrent signals are exchanged between these states in both directions (both LR to HR and HR to LR) via delayed feedback. Extensive quantitative and qualitative evaluations on benchmark datasets and on a recent challenge demonstrate that the proposed DSRN performs favorably against state-of-the-art algorithms in terms of both memory consumption and predictive accuracy. The code for our method is publicly available.

关键词： Spatial resolution Signal resolution Recurrent neural networks Computational modeling computer architecture

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