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17th European Conference on computer vision, ECCV 2022

作者： Zhou, Lin Cai, Haoming Gu, Jinjin Li, Zheyuan Liu, Yingqi Chen, Xiangyu Qiao, Yu Dong, Chao ShenZhen Key Lab of Computer Vision and Pattern Recognition SIAT-SenseTime Joint Lab Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Shanghai AI Laboratory Shanghai China The University of Sydney Sydney Australia University of Macau Zhuhai China

ISBN: (纸本)9783031250620

The attention mechanism plays a pivotal role in designing advanced super-resolution (SR) networks. In this work, we design an efficient SR network by improving the attention mechanism. We start from a simple pixel attention module and gradually modify it to achieve better super-resolution performance with reduced parameters. The specific approaches include: (1) increasing the receptive field of the attention branch, (2) replacing large dense convolution kernels with depthwise separable convolutions, and (3) introducing pixel normalization. These approaches paint a clear evolutionary roadmap for the design of attention mechanisms. Based on these observations, we propose VapSR, the Vast-receptive-field Pixel attention network. Experiments demonstrate the superior performance of VapSR. VapSR outperforms the present lightweight networks with even fewer parameters. And the light version of VapSR can use only 21.68% and 28.18% parameters of IMDB and RFDN to achieve similar performances to those networks. The code and models are available at https://***/zhoumumu/VapSR. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Convolution

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An Hybrid Attention-Based System for the Prediction of Facial Attributes 4th

An Hybrid Attention-Based System for the Prediction of Fac...

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4th International Workshop on Brain-Inspired Computing, BrainComp 2019

作者： Khellat-Kihel, Souad Sun, Zhenan Tistarelli, Massimo Computer Vision Laboratory University of Sassari Viale Italia 39 Sassari07100 Italy Center for Research on Intelligent Perception and Computing National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Room 1605 Intelligence Bulding 95 Zhongguancun East Road Beijing100190 China Computer Vision Laboratory Department of Biomedical Sciences and Information Technology University of Sassari Viale S. Pietro 43/b Sassari07100 Italy

ISBN: (纸本)9783030824266

Recent research on face analysis has demonstrated the richness of information embedded in feature vectors extracted from a deep convolutional neural network. Even though deep learning achieved a very high performance on several challenging visual tasks, such as determining the identity, age, gender and race, it still lacks a well grounded theory which allows to properly understand the processes taking place inside the network layers. Therefore, most of the underlying processes are unknown and not easy to control. On the other hand, the human visual system follows a well understood process in analyzing a scene or an object, such as a face. The direction of the eye gaze is repeatedly directed, through purposively planned saccadic movements, towards salient regions to capture several details. In this paper we propose to capitalize on the knowledge of the saccadic human visual processes to design a system to predict facial attributes embedding a biologically-inspired network architecture, the HMAX. The architecture is tailored to predict attributes with different textural information and conveying different semantic meaning, such as attributes related and unrelated to the subject’s identity. Salient points on the face are extracted from the outputs of the S2 layer of the HMAX architecture and fed to a local texture characterization module based on LBP (Local Binary pattern). The resulting feature vector is used to perform a binary classification on a set of pre-defined visual attributes. The devised system allows to distill a very informative, yet robust, representation of the imaged faces, allowing to obtain high performance but with a much simpler architecture as compared to a deep convolutional neural network. Several experiments performed on publicly available, challenging, large datasets demonstrate the validity of the proposed approach. © 2021, The Author(s).

关键词： Network architecture

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COCAS+: Large-Scale Clothes-Changing Person Re-Identification With Clothes Templates

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IEEE Transactions on Circuits and Systems for Video Technology 2023年第4期33卷 1839-1853页

作者： Li, Shihua Chen, Haobin Yu, Shijie He, Zhiqun Zhu, Feng Zhao, Rui Chen, Jie Qiao, Yu Institute of Microelectronics Chinese Academy of Sciences Beijing100029 China University of Chinese Academy of Sciences School of Microelectronics Beijing100049 China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen Key Laboratory of Computer Vision and Pattern Recognition The SIAT-SenseTime Joint Laboratory Beijing100045 China SenseTime Research Shenzhen518048 China Qing Yuan Research Institute Shanghai Jiao Tong University Shanghai200240 China

Recent years person re-identification (ReID) has been developed rapidly due to its broad practical applications. Most existing benchmarks assume that the same person wears the same clothes across captured images, while, in real-world scenarios, person may change his/her clothes frequently. Thus the Clothes-Changing person ReID (CC-ReID) problem is introduced and several related benchmarks are established. CC-ReID is a very difficult task as the main visual characteristics of a human body, clothes, are different between query and gallery, and clothes-irrelevant features are relatively weak. To promote the research and applications of person ReID in clothes-changing scenarios, in this paper, we introduce a new task called Clothes Template based Clothes-Changing person ReID (CTCC-ReID), where the query image is enhanced by a clothes template which shares similar visual patterns with the clothes of the target person image in the gallery. So, ReID methods are encouraged to jointly consider the original query image and the given clothes template for retrieval in the proposed CTCC-ReID setting. To facilitate research works on CTCC-ReID, we construct a novel large-scale ReID dataset named ClOthes ChAnging person Set Plus (COCAS+), which contains both realistic and synthetic clothes-changing person images with manually collected clothes templates. Furthermore, we propose a novel Dual-Attention Biometric-Clothes Transfusion Network (DualBCT-Net) for CTCC-ReID, which can effectively learn to extract biometric features from the original query person image and clothes features from the given clothes template and then fuse them through a Dual-Attention Fusion Module. Extensive experimental results show that the proposed CTCC-ReID setting and COCAS+ dataset can help greatly push the performance of clothes-changing ReID toward practical applications, and synthetic data is impressively effective for CTCC-ReID. What's more, the proposed DualBCT-Net shows significant improvements over

关键词： Biometrics

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UDC-UNet: Under-Display Camera Image Restoration via U-shape Dynamic Network 17th

UDC-UNet: Under-Display Camera Image Restoration via U-shap...

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17th European Conference on computer vision, ECCV 2022

作者： Liu, Xina Hu, Jinfan Chen, Xiangyu Dong, Chao Shenzhen Key Lab of Computer Vision and Pattern Recognition SIAT-SenseTime Joint Lab Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China University of Macau Zhuhai China Shanghai AI Laboratory Shanghai China

ISBN: (纸本)9783031250712

Under-Display Camera (UDC) has been widely exploited to help smartphones realize full-screen displays. However, as the screen could inevitably affect the light propagation process, the images captured by the UDC system usually contain flare, haze, blur, and noise. Particularly, flare and blur in UDC images could severely deteriorate the user experience in high dynamic range (HDR) scenes. In this paper, we propose a new deep model, namely UDC-UNet, to address the UDC image restoration problem with an estimated PSF in HDR scenes. Our network consists of three parts, including a U-shape base network to utilize multi-scale information, a condition branch to perform spatially variant modulation, and a kernel branch to leverage the prior knowledge of the PSF. According to the characteristics of HDR data, we additionally design a tone mapping loss to stabilize network optimization and achieve better visual quality. Experimental results show that the proposed UDC-UNet outperforms the state-of-the-art methods in quantitative and qualitative comparisons. Our approach won second place in the UDC image restoration track of the MIPI challenge. Codes and models are available at https://***/J-FHu/UDCUNet. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Image reconstruction

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Online non-feedback image re-ranking via dominant data selection

Online non-feedback image re-ranking via dominant data selec...

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20th ACM International Conference on Multimedia, MM 2012

作者： Cao, Chen Chen, Shifeng Li, Yuhong Liu, Jianzhuang Shenzhen Key Laboratory for Computer Vision and Pattern Recognition Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Department of Information Engineering Chinese University of Hong Kong Hong Kong Media Lab. Huawei Technologies Co. Ltd. China

ISBN: (纸本)9781450310895

Image re-ranking aims at improving the precision of keyword-based image retrieval, mainly by introducing visual features to re-rank. Many existing approaches require offline training for every keyword, which are unsuitable for online image search. Other real-time approaches demand user interaction, which are inappropriate for large-scale image collection. To improve the accuracy of web image retrieval in a practicable manner, we propose a novel re-ranking algorithm to explore the cluster information of the image set. First, we build spectral graph on images that retrieved bysearch engine, and remove isolated nodes as noisy images. Then, we select positive samples from the most dominant cluster in initial top-ranked images, and the samples are used for semi-supervised learning and ranking. Our algorithm is online and non-feedback. Experiments on two public databases demonstrate that our algorithm outperforms the state-of-the-art approaches. © 2012 ACM.

关键词： Supervised learning

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Recovering epipolar geometry by reactive tabu search

Recovering epipolar geometry by reactive tabu search

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International Conference on computer vision (ICCV)

作者： Qifa Ke Gang Xu Song De Ma National Laboratory of Pattern Recognition Institute of Automation Chinese Academy and Sciences Beijing China Computer Vision Laboratory Department of Computer Science Ritsumeikan University Kusatsu Shiga Japan

ISBN: (纸本)8173192219

In this paper we propose a new approach to recovering epipolar geometry from a pair of uncalibrated images. We first detect the feature points. By minimizing a proposed cost function, we match the feature points, discard the outliers and recover the epipolar geometry in one step. Experiments on real images show that this approach is effective and fast.

关键词： Equations Cost function computer vision Laboratories pattern recognition Automation computer science Computational geometry Image reconstruction Layout

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Temporal Conditional Random Fields: A conditional state space predictor for visual tracking

Temporal Conditional Random Fields: A conditional state spac...

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Iranian Machine vision and Image Processing (MVIP)

作者： M. J. Shafiee Z. Azimifar P. Fieguth Computer Vision and Pattern Recognition Laboratory School of Electrical & Computer Engineering Shiraz University Shiraz Iran Systems Design Engineering University of Waterloo Waterloo Canada

We present a modified Temporal Conditional Random Fields framework for modeling and predicting object motion. To facilitate such a powerful graphical model with prediction and come up with a CRF-based predictor, we propose a set of new temporal relations for object tracking, with feature functions such as optical flow (calculated among consequent frames). We evaluate our proposed Temporal Conditional Random Field method with real and synthetic data sequences and will show that the TCRF prediction is nearly equivalent with result of template matching. Experimental results show that our proposed method estimates future target state with zero error until target dynamic changes. Our proposed modified CRF method with simple and easy to implement feature functions, can learn any target dynamic, thus, it can predict next state of target with zero error.

关键词： Computational modeling Hidden Markov models computer vision Predictive models Kalman filters Image motion analysis Dynamics

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Warp that smile on your face: Optimal and smooth deformations for face recognition

Warp that smile on your face: Optimal and smooth deformation...

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International Conference on Automatic Face and Gesture recognition

作者： Tobias Gass Leonid Pishchulin Philippe Dreuw Hermann Ney Human Language Technology and Pattern Recognition Group RWTH Aachen University Germany Computer Vision Laboratory ETH Zurich Switzerland Computer Vision and Multimodal Computing MPI Informatics Saarbruecken Germany

In this work, we present novel warping algorithms for full 2D pixel-grid deformations for face recognition. Due to high variation in face appearance, face recognition is considered a very difficult task, especially if only a single reference image, for example a mug-shot, per face is available. Usually model-based approaches with additional training data are used to cope with several types of variation occurring in facial imaging. Image warping contrarily yields a distance measure which is invariant with regard to several types of variation. This allows for precise recognition even using only very few reference observations. Due to the computationally complex problem of optimal 2D warping, pseudo-2D warping-based approaches in the past represented strong approximations of the original problem, and were mainly successful on data with low variability or rectified images. We propose a novel 2D warping method which is globally optimal and makes no prior assumtions on the data variability besides two-dimensional smootheness constraints which both avoid local mirroring and gaps and significantly speed up the optimization. Furthermore, we show that occlusion handling is imperative to obtain smooth warpings in a variety of domains. We evaluate our novel algorithm on various well known databases, such as the AR-Face and CMU-PIE database, and provide a detailed comparison to existing warping approaches. We show that by using simple relative 2D constraints, strong local features and a kernel, which is robust w.r.t. occlusions, our computationally complex approaches outperform state-of-the-art results for recognizing faces under varying expressions, occlusions and poses. Most interestingly, we achieve higher accuracy using fewer training instances per class compared to methods learning a model of the 3D shape.

关键词： Pixel Face Face recognition Optimization Databases Approximation methods Hidden Markov models

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Robust stereo on multiple resolutions

Robust stereo on multiple resolutions

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International Conference on pattern recognition

作者： C. Menard A. Leonardis Department for Pattern Recognition and Image Processing Technical University of of Vienna Vienna Austria Computer and InformationScience. Computer Vision Laboratory University of Ljubljana Ljubljana Slovenia

Stereo computation is one of the vision problems where the presence of outliers cannot be neglected. Most standard algorithms make unrealistic assumptions about noise distributions, which leads to erroneous results that cannot be corrected in subsequent postprocessing stages. In this paper we present a modification of the standard area-based correlation approach so that it can tolerate a significant number of outliers. The approach exhibits a robust behavior not only in the presence of mismatches but also in the case of depth discontinuities. The confidence measure of the correlation and the number of outliers provide two complementary sources of information which, when implemented in a multiresolution framework, result in a robust and efficient method. We present the results of this approach on a number of synthetic and real images.

关键词： Stereo vision computer vision Noise robustness Information resources Correlation pattern recognition Image processing Layout Cameras Laboratories

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Performance evaluation of DTW and its variants for word spotting in degraded documents

Performance evaluation of DTW and its variants for word spot...

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International Conference on Document Analysis and recognition

作者： Tanmoy Mondal Nicolas Ragot Jean-Yves Ramel Umapada Pal Université François Rabelais Laboratory of Computer Science (LI) Tours France Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India

ISBN: (纸本)9781479918065

In word spotting literature, classical DTW has been widely employed. However there exists several other improved versions of DTW along with other robust sequence matching techniques. Very few of them have been studied in the context of word spotting and this scarcity of research work is the motivation of the paper. This paper presents a comparative study of classical Dynamic Time Warping (DTW) technique and many of its improved modifications, as well as other sequence matching techniques in the context of word spotting. An experimental study on historical documents is performed to evaluate the behavior of DTW's variants and other sequence matching techniques. A detailed comparative analysis along with wide range of experimentation is performed, which shows that classical DTW remains a good choice when there are no segmentation problems and when features are very local. In case of word segmentation errors, Continuous Dynamic Programming (CDP) seems to be a better choice. This research work has introduced several other improved sequence matching algorithms in the context of word spotting, which show interesting and improved results.

关键词： Biomedical imaging Biomedical measurement

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