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检索条件"任意字段=31st Meeting of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2018"

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Mobile Video Object Detection with Temporally-Aware Feature Maps 31

Mobile Video Object Detection with Temporally-Aware Feature ...

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

作者： Liu, Mason Zhu, Menglong Georgia Tech Atlanta GA 30332 USA Google Mountain View CA 94043 USA

ISBN: (纸本)9781538664209

This paper introduces an online model for object detection in videos designed to run in real-time on low-powered mobile and embedded devices. Our approach combines fast single-image object detection with convolutional long short term memory (LstM) layers to create an inter-weaved recurrent-convolutional architecture. Additionally, we propose an efficient Bottleneck-LstM layer that significantly reduces computational cost compared to regular LstMs. Our network achieves temporal awareness by using Bottleneck-LstMs to refine and propagate feature maps across frames. This approach is substantially faster than existing detection methods in video, outperforming the fastest single-frame models in model size and computational cost while attaining accuracy comparable to much more expensive single-frame models on the Imagenet VID 2015 dataset. Our model reaches a real-time inference speed of up to 15 FPS on a mobile CPU.

关键词： Feature extraction Computational modeling Object detection Detectors computer architecture streaming media Neural networks

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Feedback-prop: Convolutional Neural Network Inference under Partial Evidence 31

Feedback-prop: Convolutional Neural Network Inference under ...

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

作者： Wang, Tianlu Yamaguchi, Kota Ordonez, Vicente Univ Virginia Charlottesville VA 22903 USA CyberAgent Inc Tokyo Japan

ISBN: (纸本)9781538664209

We propose an inference procedure for deep convolutional neural networks (CNNs) when partial evidence is available. Our method consists of a general feedback-based propagation approach (feedback-prop) that boosts the prediction accuracy for an arbitrary set of unknown target labels when the values for a non-overlapping arbitrary set of target labels are known. We show that existing models trained in a multi-label or multi-task setting can readily take advantage of feedback-prop without any retraining or fine-tuning. Our feedback-prop inference procedure is general, simple, reliable, and works on different challenging visual recognition tasks. We present two variants of feedback-prop based on layer-wise and residual iterative updates. We experiment using several multi-task models and show that feedback-prop is effective in all of them. Our results unveil a previously unreported but interesting dynamic property of deep CNNs. We also present an associated technical approach that takes advantage of this property for inference under partial evidence in general visual recognition tasks.

关键词： Task analysis Visualization computer vision Neural networks Radio frequency Graphical models Training

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Zero-shot recognition via Semantic Embeddings and Knowledge Graphs 31

Zero-shot Recognition via Semantic Embeddings and Knowledge ...

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

作者： Wang, Xiaolong Ye, Yufei Gupta, Abhinav Carnegie Mellon Univ Inst Robot Pittsburgh PA 15213 USA

ISBN: (纸本)9781538664209

We consider the problem of zero-shot recognition: learning a visual classifier for a category with zero training examples, just using the word embedding of the category and its relationship to other categories, which visual data are provided. The key to dealing with the unfamiliar or novel category is to transfer knowledge obtained from familiar classes to describe the unfamiliar class. In this paper we build upon the recently introduced Graph Convolutional Network (GCN) and propose an approach that uses both semantic embeddings and the categorical relationships to predict the classifiers. Given a learned knowledge graph (KG), our approach takes as input semantic embeddings for each node (representing visual category). After a series of graph convolutions, we predict the visual classifier for each category. During training, the visual classifiers for a few categories are given to learn the GCN parameters. At test time, these filters are used to predict the visual classifiers of unseen categories. We show that our approach is robust to noise in the KG. More importantly, our approach provides significant improvement in performance compared to the current state-of-the-art results (from 2 similar to 3% on some metrics to whopping 20% on a few).

关键词： Visualization Semantics Training Animals Task analysis Symmetric matrices pattern recognition

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Multi-view Harmonized Bilinear Network for 3D Object recognition 31

Multi-view Harmonized Bilinear Network for 3D Object Recogni...

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

作者： Yu, Tan Meng, Jingjing Yuan, Junsong Nanyang Technol Univ Interdisciplinary Grad Sch Singapore Singapore SUNY Buffalo Dept CSE Buffalo NY USA

ISBN: (纸本)9781538664209

View-based methods have achieved considerable success in 3D object recognition tasks. Different from existing view based methods pooling the view-wise features, we tackle this problem from the perspective of patches-to-patches similarity measurement. By exploiting the relationship between polynomial kernel and bilinear pooling, we obtain an effective 3D object representation by aggregating local convolutional features through bilinear pooling. Meanwhile, we harmonize different components inherited in the bilinear feature to obtain a more discriminative representation. To achieve an end-to-end trainable framework, we incorporate the harmonized bilinear pooling as a layer of a network, constituting the proposed Multi-view Harmonized Bilinear Network (MHBN). Systematic experiments conducted on two public benchmark datasets demonstrate the efficacy of the proposed methods in 3D object recognition.

关键词： Three-dimensional displays Object recognition Two dimensional displays Feature extraction Kernel Convolutional neural networks Shape

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OATM: Occlusion Aware Template Matching by Consensus Set Maximization 31

OATM: Occlusion Aware Template Matching by Consensus Set Max...

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

作者： Korman, Simon Milam, Mark Soatto, stefano Weizmann Inst Sci Rehovot Israel Northrop Grumman Falls Church VA USA Univ Calif Los Angeles Vis Lab Los Angeles CA 90095 USA

ISBN: (纸本)9781538664209

We present a novel approach to template matching that is efficient, can handle partial occlusions, and comes with provable performance guarantees. A key component of the method is a reduction that transforms the problem of searching a nearest neighbor among N high-dimensional vv ectors, to searching neighbors among two sets of order root N vectors, which can be found efficiently using range search techniques. This allows for a quadratic improvement in search complexity, and makes the method scalable in handling large search spaces. The second contribution is a hashing scheme based on consensus set maximization, which allows us to handle occlusions. The resulting scheme can be seen as a randomized hypothesize-and-test algorithm, which is equipped with guarantees regarding the number of iterations required for obtaining an optimal solution with high probability. The predicted matching rates are validated empirically and the algorithm shows a significant improvement over the state-of-the-art in both speed and robustness to occlusions.

关键词： strain Complexity theory Search problems Task analysis Mathematical model computer vision pattern matching

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Hallucinated-IQA: No-Reference Image Quality Assessment via Adversarial Learning 31

Hallucinated-IQA: No-Reference Image Quality Assessment via ...

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

作者： Lin, Kwan-Yee Wang, Guanxiang Peking Univ Sch Math Sci Dept Informat Sci Beijing Peoples R China Peking Univ Sch Math Sci Dept Math Beijing Peoples R China

ISBN: (纸本)9781538664209

No-reference image quality assessment (NR-IQA) is a fundamental yet challenging task in low-level computer vision community. The difficulty is particularly pronounced for the limited information, for which the corresponding reference for comparison is typically absent. Although various feature extraction mechanisms have been leveraged from natural scene statistics to deep neural networks in previous methods, the performance bottleneck still exists. In this work, we propose a hallucination-guided quality regression network to address the issue. We firstly generate a hallucinated reference constrained on the distorted image, to compensate the absence of the true reference. Then, we pair the information of hallucinated reference with the distorted image, and forward them to the regressor to learn the perceptual discrepancy with the guidance of an implicit ranking relationship within the generator, and therefore produce the precise quality prediction. To demonstrate the effectiveness of our approach, comprehensive experiments are evaluated on four popular image quality assessment benchmarks. Our method significantly outperforms all the previous state-of-the-art methods by large margins. The code and model are publicly available on the project page https: //***. io/projects/HIQA/***.

关键词： Task analysis Distortion Image quality Feature extraction Semantics computer vision Predictive models

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Look, Imagine and Match: Improving Textual-Visual Cross-Modal Retrieval with Generative Models 31

Look, Imagine and Match: Improving Textual-Visual Cross-Moda...

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

作者： Gu, Jiuxiang Cai, Jianfei Joty, Shafiq Niu, Li Wang, Gang Nanyang Technol Univ Interdisciplinary Grad Sch ROSE Lab Singapore Singapore Nanyang Technol Univ Sch Comp Sci & Engn Singapore Singapore Rice Univ Houston TX 77251 USA Alibaba AI Labs Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9781538664209

Textual-visual cross-modal retrieval has been a hot research topic in both computer vision and natural language processing communities. Learning appropriate representations for multi-modal data is crucial for the cross-modal retrieval performance. Unlike existing image-text retrieval approaches that embed image-text pairs as single feature vectors in a common representational space, we propose to incorporate generative processes into the cross-modal feature embedding, through which we are able to learn not only the global abstract features but also the local grounded features. Extensive experiments show that our framework can well match images and sentences with complex content, and achieve the state-of-the-art cross-modal retrieval results on MSCOCO dataset.

关键词： Visualization Training Decoding Semantics Measurement Xenon computer vision

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Fooling vision and Language Models Despite Localization and Attention Mechanism 31

Fooling Vision and Language Models Despite Localization and ...

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

作者： Xu, Xiaojun Chen, Xinyun Liu, Chang Rohrbach, Anna Darrell, Trevor Song, Dawn Shanghai Jiao Tong Univ Shanghai Peoples R China Univ Calif Berkeley EECS Berkeley CA 94720 USA MPI Informat Saarbrucken Germany

ISBN: (纸本)9781538664209

Adversarial attacks are known to succeed on classifiers, but it has been an open question whether more complex vision systems are vulnerable. In this paper, we study adversarial examples for vision and language models, which incorporate natural language understanding and complex structures such as attention, localization, and modular architectures. In particular, we investigate attacks on a dense captioning model and on two visual question answering (VQA) models. Our evaluation shows that we can generate adversarial examples with a high success rate (i.e., > 90%) for these models. Our work sheds new light on understanding adversarial attacks on vision systems which have a language component and shows that attention, bounding box localization, and compositional internal structures are vulnerable to adversarial attacks. These observations will inform future work towards building effective defenses.

关键词： Prediction algorithms Computational modeling Neural networks Knowledge discovery Visualization Predictive models Natural languages

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Partially Shared Multi-Task Convolutional Neural Network with Local Constraint for Face Attribute Learning 31

Partially Shared Multi-Task Convolutional Neural Network wit...

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

作者： Cao, Jiajiong Li, Yingming Zhang, Zhongfei Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9781538664209

In this paper, we study the face attribute learning problem by considering the identity information and attribute relationships simultaneously. In particular, we first introduce a Partially Shared Multi-task Convolutional Neural Network (PS-MCNN), in which four Task Specific Networks (TSNets) and one Shared Network (SNet) are connected by Partially Shared (PS) structures to learn better shared and task specific representations. To utilize identity information to further boost the performance, we introduce a local learning constraint which minimizes the difference between the representations of each sample and its local geometric neighbours with the same identity. Consequently, we present a local constraint regularized multitask network, called Partially Shared Multi-task Convolutional Neural Network with Local Constraint (PS-MCNN-LC), where PS structure and local constraint are integrated together to help the framework learn better attribute representations. The experimental results on CelebA and LFWA demonstrate the promise of the proposed methods.

关键词： Task analysis Face Hair Convolutional neural networks computer architecture Feature extraction Bellows

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Classifier Learning with Prior Probabilities for Facial Action Unit recognition 31

Classifier Learning with Prior Probabilities for Facial Acti...

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

作者： Zhang, Yong Dong, Weiming Hu, Bao-Gang Ji, Qiang CASIA Natl Lab Pattern Recognit Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Rensselaer Polytech Inst Troy NY 12181 USA

ISBN: (纸本)9781538664209

Facial action units (AUs) play an important role in human emotion understanding. One big challenge for data-driven AU recognition approaches is the lack of enough AU annotations, since AU annotation requires strong domain expertise. To alleviate this issue, we propose a knowledge-driven method for jointly learning multiple AU classifiers without any AU annotation by leveraging prior probabilities on AUs, including expression-independent and expression-dependent AU probabilities. These prior probabilities are drawn from facial anatomy and emotion studies, and are independent of datasets. We incorporate the prior probabilities on AUs as the constraints into the objective function of multiple AU classifiers, and develop an efficient learning algorithm to solve the formulated problem. Experimental results on five benchmark expression databases demonstrate the effectiveness of the proposed method, especially its generalization ability, and the power of the prior probabilities.

关键词： Gold Training Correlation Muscles Face recognition Linear programming

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