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Weakly supervised object localization with progressive domain adaptation

Weakly supervised object localization with progressive domai...

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Li, Dong Huang, Jia-Bin Li, Yali Wang, Shengjin Yang, Ming-Hsuan Tsinghua University China University of Illinois Urbana-Champaign United States University of California Merced United States

ISBN: (纸本)9781467388511

We address the problem of weakly supervised object localization where only image-level annotations are available for training. Many existing approaches tackle this problem through object proposal mining. However, a substantial amount of noise in object proposals causes ambiguities for learning discriminative object models. Such approaches are sensitive to model initialization and often converge to an undesirable local minimum. In this paper, we address this problem by progressive domain adaptation with two main steps: classification adaptation and detection adaptation. In classification adaptation, we transfer a pre-trained network to our multi-label classification task for recognizing the presence of a certain object in an image. In detection adaptation, we first use a mask-out strategy to collect class-specific object proposals and apply multiple instance learning to mine confident candidates. We then use these selected object proposals to fine-tune all the layers, resulting in a fully adapted detection network. We extensively evaluate the localization performance on the PASCAL VOC and ILSVRC datasets and demonstrate significant performance improvement over the state-of-the-art methods.

关键词： pattern recognition

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CRAFT objects from images

CRAFT objects from images

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Yang, Bin Yan, Junjie Lei, Zhen Li, Stan Z. National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences China Tsinghua University China

ISBN: (纸本)9781467388511

Object detection is a fundamental problem in image understanding. One popular solution is the R-CNN framework [15] and its fast versions [14, 27]. They decompose the object detection problem into two cascaded easier tasks: 1) generating object proposals from images, 2) classifying proposals into various object categories. Despite that we are handling with two relatively easier tasks, they are not solved perfectly and there's still room for improvement. In this paper, we push the "divide and conquer" solution even further by dividing each task into two sub-tasks. We call the proposed method "CRAFT" (Cascade Regionproposal-network And FasT-rcnn), which tackles each task with a carefully designed network cascade. We show that the cascade structure helps in both tasks: in proposal generation, it provides more compact and better localized object proposals; in object classification, it reduces false positives (mainly between ambiguous categories) by capturing both inter- and intra-category variances. CRAFT achieves consistent and considerable improvement over the state-ofthe-art on object detection benchmarks like PASCAL VOC 07/12 and ILSVRC.

关键词： Object detection

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Joint training of cascaded CNN for face detection

Joint training of cascaded CNN for face detection

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Qin, Hongwei Yan, Junjie Li, Xiu Hu, Xiaolin Grad. School at Shenzhen Tsinghua University China Dept. of Automation Tsinghua University China Dept. of Computer Science and Technology Tsinghua University China SenseTime United States

ISBN: (纸本)9781467388511

Cascade has been widely used in face detection, where classifier with low computation cost can be firstly used to shrink most of the background while keeping the recall. The cascade in detection is popularized by seminal Viola-Jones framework and then widely used in other pipelines, such as DPM and CNN. However, to our best knowledge, most of the previous detection methods use cascade in a greedy manner, where previous stages in cascade are fixed when training a new stage. So optimizations of different CNNs are isolated. In this paper, we propose joint training to achieve end-to-end optimization for CNN cascade. We show that the back propagation algorithm used in training CNN can be naturally used in training CNN cascade. We present how jointly training can be conducted on naive CNN cascade and more sophisticated region proposal network (RPN) and fast R-CNN. Experiments on face detection benchmarks verify the advantages of the joint training.

关键词： Face recognition

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Temporal action localization in untrimmed videos via multi-stage CNNs

Temporal action localization in untrimmed videos via multi-s...

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Shou, Zheng Wang, Dongang Chang, Shih-Fu Columbia University New YorkNY United States

ISBN: (纸本)9781467388511

We address temporal action localization in untrimmed long videos. This is important because videos in real applications are usually unconstrained and contain multiple action instances plus video content of background scenes or other activities. To address this challenging issue, we exploit the effectiveness of deep networks in temporal action localization via three segment-based 3D ConvNets: (1) a proposal network identifies candidate segments in a long video that may contain actions; (2) a classification network learns one-vs-all action classification model to serve as initialization for the localization network; and (3) a localization network fine-tunes the learned classification network to localize each action instance. We propose a novel loss function for the localization network to explicitly consider temporal overlap and achieve high temporal localization accuracy. In the end, only the proposal network and the localization network are used during prediction. On two largescale benchmarks, our approach achieves significantly superior performances compared with other state-of-the-art systems: mAP increases from 1.7% to 7.4% on MEXaction2 and increases from 15.0% to 19.0% on THUMOS 2014.

关键词： computer vision

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Action recognition in video using sparse coding and relative features

Action recognition in video using sparse coding and relative...

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Alfaro, Analí Mery, Domingo Soto, Alvaro P. Universidad Catolica de Chile Santiago Chile

ISBN: (纸本)9781467388511

This work presents an approach to category-based action recognition in video using sparse coding techniques. The proposed approach includes two main contributions: i) A new method to handle intra-class variations by decomposing each video into a reduced set of representative atomic action acts or key-sequences, and ii) A new video descriptor, ITRA: Inter-Temporal Relational Act Descriptor, that exploits the power of comparative reasoning to capture relative similarity relations among key-sequences. In terms of the method to obtain key-sequences, we introduce a loss function that, for each video, leads to the identification of a sparse set of representative key-frames capturing both, relevant particularities arising in the input video, as well as relevant generalities arising in the complete class collection. In terms of the method to obtain the ITRA descriptor, we introduce a novel scheme to quantify relative intra and inter-class similarities among local temporal patterns arising in the videos. The resulting ITRA descriptor demonstrates to be highly effective to discriminate among action categories. As a result, the proposed approach reaches remarkable action recognition performance on several popular benchmark datasets, outperforming alternative state-ofthe-art techniques by a large margin.

关键词： Video signal processing

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Synthesized classifiers for zero-shot learning

Synthesized classifiers for zero-shot learning

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2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Changpinyo, Soravit Chao, Wei-Lun Gong, Boqing Sha, Fei U. of Southern California Los AngelesCA United States U. of Central Florida OrlandoFL United States U. of California Los AngelesCA United States

ISBN: (纸本)9781467388511

Given semantic descriptions of object classes, zeroshot learning aims to accurately recognize objects of the unseen classes, from which no examples are available at the training stage, by associating them to the seen classes, from which labeled examples are provided. We propose to tackle this problem from the perspective of manifold learning. Our main idea is to align the semantic space that is derived from external information to the model space that concerns itself with recognizing visual features. To this end, we introduce a set of "phantom" object classes whose coordinates live in both the semantic space and the model space. Serving as bases in a dictionary, they can be optimized from labeled data such that the synthesized real object classifiers achieve optimal discriminative performance. We demonstrate superior accuracy of our approach over the state of the art on four benchmark datasets for zero-shot learning, including the full ImageNet Fall 2011 dataset with more than 20,000 unseen classes.

关键词： computer vision

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Robust, real-time 3D tracking of multiple objects with similar appearances

Robust, real-time 3D tracking of multiple objects with simil...

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2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Sekii, Taiki Panasonic System Networks R and D Lab. Co. Ltd. Japan

ISBN: (纸本)9781467388511

This paper proposes a novel method for tracking multiple moving objects and recovering their three-dimensional (3D) models separately using multiple calibrated cameras. For robustly tracking objects with similar appearances, the proposed method uses geometric information regarding 3D scene structure rather than appearance. A major limitation of previous techniques is foreground confusion, in which the shapes of objects and/or ghosting artifacts are ignored and are hence not appropriately specified in foreground regions. To overcome this limitation, our method classifies foreground voxels into targets (objects and artifacts) in each frame using a novel, probabilistic two-stage framework. This is accomplished by step-wise application of a track graph describing how targets interact and the maximum a posteriori expectation-maximization algorithm for the estimation of target parameters. We introduce mixture models with semiparametric component distributions regarding 3D target shapes. In order to not confuse artifacts with objects of interest, we automatically detect and track artifacts based on a closed-world assumption. Experimental results show that our method outperforms state-of-the-art trackers on seven public sequences while achieving real-time performance.

关键词： computer vision

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Learning to select pre-trained deep representations with Bayesian evidence framework

Learning to select pre-trained deep representations with Bay...

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Kim, Yong-Deok Jang, Taewoong Han, Bohyung Choi, Seungjin Software R and D Center Device Solutions Samsung Electronics Korea Republic of Stradvision Inc. Korea Republic of Department of Computer Science and Engineering POSTECH Korea Republic of

ISBN: (纸本)9781467388511

We propose a Bayesian evidence framework to facilitate transfer learning from pre-trained deep convolutional neural networks (CNNs). Our framework is formulated on top of a least squares SVM (LS-SVM) classifier, which is simple and fast in both training and testing, and achieves competitive performance in practice. The regularization parameters in LS-SVM is estimated automatically without grid search and cross-validation by maximizing evidence, which is a useful measure to select the best performing CNN out of multiple candidates for transfer learning; the evidence is optimized efficiently by employing Aitken's delta-squared process, which accelerates convergence of fixed point update. The proposed Bayesian evidence framework also provides a good solution to identify the best ensemble of heterogeneous CNNs through a greedy algorithm. Our Bayesian evidence framework for transfer learning is tested on 12 visual recognition datasets and illustrates the state-of-the-art performance consistently in terms of prediction accuracy and modeling efficiency.

关键词： Bayesian networks

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Cataloging public objects using aerial and street-level images - Urban Trees

Cataloging public objects using aerial and street-level imag...

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Wegner, Jan D. Branson, Steve Hall, David Schindler, Konrad Perona, Pietro ETH Zürich Switzerland California Institute of Technology United States

ISBN: (纸本)9781467388511

Each corner of the inhabited world is imaged from multiple viewpoints with increasing frequency. Online map services like Google Maps or Here Maps provide direct access to huge amounts of densely sampled, georeferenced images from street view and aerial perspective. There is an opportunity to design computer vision systems that will help us search, catalog and monitor public infrastructure, buildings and artifacts. We explore the architecture and feasibility of such a system. The main technical challenge is combining test time information from multiple views of each geographic location (e.g., aerial and street views). We implement two modules: det2geo, which detects the set of locations of objects belonging to a given category, and geo2cat, which computes the fine-grained category of the object at a given location. We introduce a solution that adapts state-oftheart CNN-based object detectors and classifiers. We test our method on "Pasadena Urban Trees", a new dataset of 80,000 trees with geographic and species annotations, and show that combining multiple views significantly improves both tree detection and tree species classification, rivaling human performance.

关键词： computer vision

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A probabilistic collaborative representation based approach for pattern classification

A probabilistic collaborative representation based approach ...

引用

2016 ieee conference on computer vision and pattern recognition, cvpr 2016

作者： Cai, Sijia Zhang, Lei Zuo, Wangmeng Feng, Xiangchu Dept. of Computing Hong Kong Polytechnic University Hong Kong Hong Kong School of Computer Science and Technology Harbin Institute of Technology Harbin China Dept. of Applied Mathematics Xidian University Xi'an China

ISBN: (纸本)9781467388511

Conventional representation based classifiers, ranging from the classical nearest neighbor classifier and nearest subspace classifier to the recently developed sparse representation based classifier (SRC) and collaborative representation based classifier (CRC), are essentially distance based classifiers. Though SRC and CRC have shown interesting classification results, their intrinsic classification mechanism remains unclear. In this paper we propose a probabilistic collaborative representation framework, where the probability that a test sample belongs to the collaborative subspace of all classes can be well defined and computed. Consequently, we present a probabilistic collaborative representation based classifier (ProCRC), which jointly maximizes the likelihood that a test sample belongs to each of the multiple classes. The final classification is performed by checking which class has the maximum likelihood. The proposed ProCRC has a clear probabilistic interpretation, and it shows superior performance to many popular classifiers, including SRC, CRC and SVM. Coupled with the CNN features, it also leads to state-of-the-art classification results on a variety of challenging visual datasets.

关键词： Classification (of information)

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