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Learning deep feature representations with Domain Guided Dropout for person re-identification

Learning deep feature representations with Domain Guided Dro...

引用

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

作者： Xiao, Tong Li, Hongsheng Ouyang, Wanli Wang, Xiaogang Department of Electronic Engineering Chinese University of Hong Kong Hong Kong

ISBN: (纸本)9781467388511

Learning generic and robust feature representations with data from multiple domains for the same problem is of great value, especially for the problems that have multiple datasets but none of them are large enough to provide abundant data variations. In this work, we present a pipeline for learning deep feature representations from multiple domains with Convolutional Neural Networks (CNNs). When training a CNN with data from all the domains, some neurons learn representations shared across several domains, while some others are effective only for a specific one. Based on this important observation, we propose a Domain Guided Dropout algorithm to improve the feature learning procedure. Experiments show the effectiveness of our pipeline and the proposed algorithm. Our methods on the person re-identification problem outperform stateof-the-art methods on multiple datasets by large margins. This work is partially supported by SenseTime Group Limited, the General Research Fund sponsored by the Research Grants Council of Hong Kong (Project Nos. CUHK14206114, CUHK14205615, CUHK417011, CUHK14207814, CUHK14203015), and National Natural Science Foundation of China (NSFC, NO.61371192).

关键词： pattern recognition

来源：评论

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We are humor beings: Understanding and predicting visual humor

We are humor beings: Understanding and predicting visual hum...

引用

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

作者： Chandrasekaran, Arjun Vijayakumar, Ashwin K. Antol, Stanislaw Bansal, Mohit Batra, Dhruv Zitnick, C. Lawrence Parikh, Devi Virginia Tech United States TTI-Chicago United States Facebook AI Research United States

ISBN: (纸本)9781467388511

Humor is an integral part of human lives. Despite being tremendously impactful, it is perhaps surprising that we do not have a detailed understanding of humor yet. As interactions between humans and AI systems increase, it is imperative that these systems are taught to understand subtleties of human expressions such as humor. In this work, we are interested in the question - what content in a scene causes it to be funny? As a first step towards understanding visual humor, we analyze the humor manifested in abstract scenes and design computational models for them. We collect two datasets of abstract scenes that facilitate the study of humor at both the scene-level and the object-level. We analyze the funny scenes and explore the different types of humor depicted in them via human studies. We model two tasks that we believe demonstrate an understanding of some aspects of visual humor. The tasks involve predicting the funniness of a scene and altering the funniness of a scene. We show that our models perform well quantitatively, and qualitatively through human studies. Our datasets are publicly available.

关键词： computer vision

来源：评论

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STCT: Sequentially training convolutional networks for visual tracking

STCT: Sequentially training convolutional networks for visua...

引用

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

作者： Wang, Lijun Ouyang, Wanli Wang, Xiaogang Lu, Huchuan Dalian University of Technology China Chinese University of Hong Kong Hong Kong

ISBN: (纸本)9781467388511

Due to the limited amount of training samples, finetuning pre-trained deep models online is prone to overfitting. In this paper, we propose a sequential training method for convolutional neural networks (CNNs) to effectively transfer pre-trained deep features for online applications. We regard a CNN as an ensemble with each channel of the output feature map as an individual base learner. Each base learner is trained using different loss criterions to reduce correlation and avoid over-training. To achieve the best ensemble online, all the base learners are sequentially sampled into the ensemble via important sampling. To further improve the robustness of each base learner, we propose to train the convolutional layers with random binary masks, which serves as a regularization to enforce each base learner to focus on different input features. The proposed online training method is applied to visual tracking problem by transferring deep features trained on massive annotated visual data and is shown to significantly improve tracking performance. Extensive experiments are conducted on two challenging benchmark data set and demonstrate that our tracking algorithm can outperform state-of-the-art methods with a considerable margin.

关键词： pattern recognition

来源：评论

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RIFD-CNN: Rotation-invariant and Fisher discriminative convolutional neural networks for object detection

RIFD-CNN: Rotation-invariant and Fisher discriminative convo...

引用

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

作者： Cheng, Gong Zhou, Peicheng Han, Junwei School of Automation Northwestern Polytechnical University Xi'an China

ISBN: (纸本)9781467388511

Thanks to the powerful feature representations obtained through deep convolutional neural network (CNN), the performance of object detection has recently been substantially boosted. Despite the remarkable success, the problems of object rotation, within-class variability, and between-class similarity remain several major challenges. To address these problems, this paper proposes a novel and effective method to learn a rotation-invariant and Fisher discriminative CNN (RIFD-CNN) model. This is achieved by introducing and learning a rotation-invariant layer and a Fisher discriminative layer, respectively, on the basis of the existing high-capacity CNN architectures. Specifically, the rotation-invariant layer is trained by imposing an explicit regularization constraint on the objective function that enforces invariance on the CNN features before and after rotating. The Fisher discriminative layer is trained by imposing the Fisher discrimination criterion on the CNN features so that they have small within-class scatter but large between-class separation. In the experiments, we comprehensively evaluate the proposed method for object detection task on a public available aerial image dataset and the PASCAL VOC 2007 dataset. State-of-the-art results are achieved compared with the existing baseline methods.

关键词： pattern recognition

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Semantic channels for fast pedestrian detection

Semantic channels for fast pedestrian detection

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

作者： Costea, Arthur Daniel Nedevschi, Sergiu Image Processing and Pattern Recognition Research Center Technical University of Cluj-Napoca Romania

ISBN: (纸本)9781467388511

Pedestrian detection and semantic segmentation are high potential tasks for many real-time applications. However most of the top performing approaches provide state of art results at high computational costs. In this work we propose a fast solution for achieving state of art results for both pedestrian detection and semantic segmentation. As baseline for pedestrian detection we use sliding windows over cost efficient multiresolution filtered LUV+HOG channels. We use the same channels for classifying pixels into eight semantic classes. Using short range and long range multiresolution channel features we achieve more robust segmentation results compared to traditional codebook based approaches at much lower computational costs. The resulting segmentations are used as additional semantic channels in order to achieve a more powerful pedestrian detector. To also achieve fast pedestrian detection we employ a multiscale detection scheme based on a single flexible pedestrian model and a single image scale. The proposed solution provides competitive results on both pedestrian detection and semantic segmentation benchmarks at 8 FPS on CPU and at 15 FPS on GPU, being the fastest top performing approach.

关键词： Semantics

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A probabilistic framework for color-based point set registration

A probabilistic framework for color-based point set registra...

引用

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

作者： Danelljan, Martin Meneghetti, Giulia Khan, Fahad Shahbaz Felsberg, Michael Computer Vision Laboratory Department of Electrical Engineering Linköping University Sweden

ISBN: (纸本)9781467388511

In recent years, sensors capable of measuring both color and depth information have become increasingly popular. Despite the abundance of colored point set data, stateof-the-art probabilistic registration techniques ignore the available color information. In this paper, we propose a probabilistic point set registration framework that exploits available color information associated with the points. Our method is based on a model of the joint distribution of 3D-point observations and their color information. The proposed model captures discriminative color information, while being computationally efficient. We derive an EM algorithm for jointly estimating the model parameters and the relative transformations. Comprehensive experiments are performed on the Stanford Lounge dataset, captured by an RGB-D camera, and two point sets captured by a Lidar sensor. Our results demonstrate a significant gain in robustness and accuracy when incorporating color information. On the Stanford Lounge dataset, our approach achieves a relative reduction of the failure rate by 78% compared to the baseline. Furthermore, our proposed model outperforms standard strategies for combining color and 3D-point information, leading to state-of-the-art results.

关键词： Color

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3D action recognition from novel viewpoints

3D action recognition from novel viewpoints

引用

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

作者： Rahmani, Hossein Mian, Ajmal School of Computer Science and Software Engineering University of Western Australia Australia

ISBN: (纸本)9781467388511

We propose a human pose representation model that transfers human poses acquired from different unknown views to a view-invariant high-level space. The model is a deep convolutional neural network and requires a large corpus of multiview training data which is very expensive to acquire. Therefore, we propose a method to generate this data by fitting synthetic 3D human models to real motion capture data and rendering the human poses from numerous viewpoints. While learning the CNN model, we do not use action labels but only the pose labels after clustering all training poses into k clusters. The proposed model is able to generalize to real depth images of unseen poses without the need for re-training or fine-tuning. Real depth videos are passed through the model frame-wise to extract viewinvariant features. For spatio-temporal representation, we propose group sparse Fourier Temporal Pyramid which robustly encodes the action specific most discriminative output features of the proposed human pose model. Experiments on two multiview and three single-view benchmark datasets show that the proposed method dramatically outperforms existing state-of-the-art in action recognition.

关键词： Three dimensional computer graphics

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Adaptive decontamination of the training set: A unified formulation for discriminative visual tracking

Adaptive decontamination of the training set: A unified form...

引用

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

作者： Danelljan, Martin Häger, Gustav Khan, Fahad Shahbaz Felsberg, Michael Computer Vision Laboratory Department of Electrical Engineering Linköping University Sweden

ISBN: (纸本)9781467388511

Tracking-by-detection methods have demonstrated competitive performance in recent years. In these approaches, the tracking model heavily relies on the quality of the training set. Due to the limited amount of labeled training data, additional samples need to be extracted and labeled by the tracker itself. This often leads to the inclusion of corrupted training samples, due to occlusions, misalignments and other perturbations. Existing tracking-by-detection methods either ignore this problem, or employ a separate component for managing the training set. We propose a novel generic approach for alleviating the problem of corrupted training samples in tracking-bydetection frameworks. Our approach dynamically manages the training set by estimating the quality of the samples. Contrary to existing approaches, we propose a unified formulation by minimizing a single loss over both the target appearance model and the sample quality weights. The joint formulation enables corrupted samples to be downweighted while increasing the impact of correct ones. Experiments are performed on three benchmarks: OTB-2015 with 100 videos, VOT-2015 with 60 videos, and Temple-Color with 128 videos. On the OTB-2015, our unified formulation significantly improves the baseline, with a gain of 3.8% in mean overlap precision. Finally, our method achieves state-of-the-art results on all three datasets.

关键词： computer vision

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How hard can it be? Estimating the difficulty of visual search in an image

How hard can it be? Estimating the difficulty of visual sear...

引用

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

作者： Ionescu, Radu Tudor Alexe, Bogdan Leordeanu, Marius Popescu, Marius Papadopoulos, Dim P. Ferrari, Vittorio University of Bucharest Romania University of Edinburgh United Kingdom Institute of Mathematics Romanian Academy Romania Institute of Mathematical Statistics and Applied Mathematics Romanian Academy Romania

ISBN: (纸本)9781467388511

We address the problem of estimating image difficulty defined as the human response time for solving a visual search task. We collect human annotations of image difficulty for the PASCAL VOC 2012 data set through a crowd-sourcing platform. We then analyze what human interpretable image properties can have an impact on visual search difficulty, and how accurate are those properties for predicting difficulty. Next, we build a regression model based on deep features learned with state of the art convolutional neural networks and show better results for predicting the groundtruth visual search difficulty scores produced by human annotators. Our model is able to correctly rank about 75% image pairs according to their difficulty score. We also show that our difficulty predictor generalizes well to new classes not seen during training. Finally, we demonstrate that our predicted difficulty scores are useful for weakly supervised object localization (8% improvement) and semi-supervised object classification (1% improvement).

关键词： computer vision

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Hierarchical recurrent neural encoder for video representation with application to captioning

Hierarchical recurrent neural encoder for video representati...

引用

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

作者： Pan, Pingbo Xu, Zhongwen Yang, Yi Wu, Fei Zhuang, Yueting Zhejiang University China University of Technology Sydney Australia

ISBN: (纸本)9781467388511

Recently, deep learning approach, especially deep Convolutional Neural Networks (ConvNets), have achieved overwhelming accuracy with fast processing speed for image classification. Incorporating temporal structure with deep ConvNets for video representation becomes a fundamental problem for video content analysis. In this paper, we propose a new approach, namely Hierarchical Recurrent Neural Encoder (HRNE), to exploit temporal information of videos. Compared to recent video representation inference approaches, this paper makes the following three contributions. First, our HRNE is able to efficiently exploit video temporal structure in a longer range by reducing the length of input information flow, and compositing multiple consecutive inputs at a higher level. Second, computation operations are significantly lessened while attaining more nonlinearity. Third, HRNE is able to uncover temporal transitions between frame chunks with different granularities, i.e. it can model the temporal transitions between frames as well as the transitions between segments. We apply the new method to video captioning where temporal information plays a crucial role. Experiments demonstrate that our method outperforms the state-of-the-art on video captioning benchmarks.

关键词： pattern recognition

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