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Exploring Multi-Scale Feature Propagation and Communication for Image Super Resolution

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arXiv 2020年

作者： Feng, Ruicheng Guan, Weipeng Qiao, Yu Dong, Chao Shenzhen Key Lab of Computer Vision and Pattern Recognition SIAT-SenseTime Joint Lab Shenzhen Institutes of Advanced Technology Chinese Acedamy of Sciences China Chinese University of Hong Kong Hong Kong

Multi-scale techniques have achieved great success in a wide range of computer vision tasks. However, while this technique is incorporated in existing works, there still lacks a comprehensive investigation on variants of multi-scale convolution in image super resolution. In this work, we present a unified formulation over widely-used multi-scale structures. With this framework, we systematically explore the two factors of multi-scale convolution – feature propagation and cross-scale communication. Based on the investigation, we propose a generic and efficient multi-scale convolution unit – Multi-Scale cross-Scale Share-weights convolution (MS3-Conv). Extensive experiments demonstrate that the proposed MS3-Conv can achieve better SR performance than the standard convolution with less parameters and computational cost. Beyond quantitative analysis, we comprehensively study the visual quality, which show that MS3-Conv behave better to recover high-frequency details. Copyright © 2020, The Authors. All rights reserved.

关键词： Optical resolving power

Suppressing Model Overfitting for Image Super-Resolution Networks

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Suppressing Model Overfitting for Image Super-Resolution Net...

IEEE/CVF Conference on computer vision and pattern recognition Workshops

作者： Ruicheng Feng Jinjin Gu Yu Qiao Chao Dong ShenZhen Key Lab of Computer Vision and Pattern Recognition SIAT-SenseTime Joint Lab Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences The School of Science and Engineering The Chinese University of Hong Kong

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

Large deep networks have demonstrated competitive performance in single image super-resolution (SISR), with a huge volume of data involved. However, in real-world scenarios, due to the limited accessible training pairs, large models exhibit undesirable behaviors such as overfitting and memorization. To suppress model overfitting and further enjoy the merits of large model capacity, we thoroughly investigate generic approaches for supplying additional training data pairs. In particular, we introduce a simple learning principle MixUp to train networks on interpolations of sample pairs, which encourages networks to support linear behavior in-between training samples. In addition, we propose a data synthesis method with learned degradation, enabling models to use extra high-quality images with higher content diversity. This strategy proves to be successful in reducing biases of data. By combining these components - MixUp and synthetic training data, large models can be trained without overfitting under very limited data samples and achieve satisfactory generalization performance. Our method won the second place in NTIRE2019 Real SR Challenge.

关键词： super-resolution Network images Training data Data sampling Imagery (Psychotherapy) Image

Geometry sharing network for 3D point cloud classification and segmentation

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arXiv 2019年

作者： Xu, Mingye Zhou, Zhipeng Qiao, Yu ShenZhen Key Lab of Computer Vision and Pattern Recognition SIAT-SenseTime Joint Lab Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Siat Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society

In spite of the recent progresses on classifying 3D point cloud with deep CNNs, large geometric transformations like rotation and translation remain challenging problem and harm the final classification performance. To address this challenge, we propose Geometry Sharing Network (GS-Net) which effectively learns point descriptors with holistic context to enhance the robustness to geometric transformations. Compared with previous 3D point CNNs which perform convolution on nearby points, GS-Net can aggregate point features in a more global way. Specially, GS-Net consists of Geometry Similarity Connection (GSC) modules which exploit Eigen-Graph to group distant points with similar and relevant geometric information, and aggregate features from nearest neighbors in both Euclidean space and Eigenvalue space. This design allows GS-Net to efficiently capture both local and holistic geometric features such as symmetry, curvature, convexity and connectivity. Theoretically, we show the nearest neighbors of each point in Eigenvalue space are invariant to rotation and translation. We conduct extensive experiments on public datasets, ModelNet40, ShapeNet Part. Experiments demonstrate that GS-Net achieves the state-of-the-art performances on major datasets, 93.3% on ModelNet40, and are more robust to geometric transformations. Code is released on https://***/MingyeXu/GS-Net. Copyright © 2019, The Authors. All rights reserved.

关键词： Geometry

Suppressing uncertainties for large-scale facial expression recognition

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arXiv 2020年

作者： Wang, Kai Peng, Xiaojiang Yang, Jianfei Lu, Shijian Qiao, Yu ShenZhen Key Lab of Computer Vision and Pattern Recognition SIAT-SenseTime Joint Lab Shenzhen Institutes of Advanced Technology Chinese Academy of Science University of Chinese Academy of Sciences China Nanyang Technological University Singapore

Annotating a qualitative large-scale facial expression dataset is extremely difficult due to the uncertainties caused by ambiguous facial expressions, low-quality facial images, and the subjectiveness of annotators. These uncertainties lead to a key challenge of large-scale Facial Expression recognition (FER) in deep learning era. To address this problem, this paper proposes a simple yet efficient Self-Cure Network (SCN) which suppresses the uncertainties efficiently and prevents deep networks from over-fitting uncertain facial images. Specifically, SCN suppresses the uncertainty from two different aspects: 1) a self-attention mechanism over mini-batch to weight each training sample with a ranking regularization, and 2) a careful relabeling mechanism to modify the labels of these samples in the lowest-ranked group. Experiments on synthetic FER datasets and our collected WebEmotion dataset validate the effectiveness of our method. Results on public benchmarks demonstrate that our SCN outperforms current state-of-the-art methods with 88.14% on RAF-DB, 60.23% on AffectNet, and 89.35% on FERPlus. The code will be available at https://***/kaiwang960112/Self-Cure-Network. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep learning

Context-transformer: Tackling object confusion for few-shot detection

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arXiv 2020年

作者： Yang, Ze Wang, Yali Chen, Xianyu Liu, Jianzhuang Qiao, Yu ShenZhen Key Lab of Computer Vision and Pattern Recognition SIAT-SenseTime Joint Lab. Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Huawei Noah’s Ark Lab. SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society

Few-shot object detection is a challenging but realistic scenario, where only a few annotated training images are available for training detectors. A popular approach to handle this problem is transfer learning, i.e., fine-tuning a detector pretrained on a source-domain benchmark. However, such transferred detector often fails to recognize new objects in the target domain, due to low data diversity of training samples. To tackle this problem, we propose a novel Context-Transformer within a concise deep transfer framework. Specifically, Context-Transformer can effectively leverage source-domain object knowledge as guidance, and automatically exploit contexts from only a few training images in the target domain. Subsequently, it can adaptively integrate these relational clues to enhance the discriminative power of detector, in order to reduce object confusion in few-shot scenarios. Moreover, Context-Transformer is flexibly embedded in the popular SSD-style detectors, which makes it a plug-and-play module for end-to-end few-shot learning. Finally, we evaluate Context-Transformer on the challenging settings of few-shot detection and incremental few-shot detection. The experimental results show that, our framework outperforms the recent state-of-the-art approaches. The codes are available at https://***/Ze-Yang/ContextTransformer. Copyright © 2020, The Authors. All rights reserved.

关键词： Object detection

Neighbourhood-guided feature reconstruction for occluded person re-identification

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arXiv 2021年

作者： Yu, Shijie Chen, Dapeng Zhao, Rui Chen, Haobin Qiao, Yu ShenZhen Key Lab of Computer Vision and Pattern Recognition SIAT-SenseTime Joint Lab Shenzhen Institute of Advanced Technology Chinese Academy of Sciences University of Chinese Academy of Sciences China SenseTime Group Limited Shanghai AI Lab Shanghai China

Person images captured by surveillance cameras are often occluded by various obstacles, which lead to defective feature representation and harm person re-identification (Re-ID) performance. To tackle this challenge, we propose to reconstruct the feature representation of occluded parts by fully exploiting the information of its neighborhood in a gallery image set. Specifically, we first introduce a visible part-based feature by body mask for each person image. Then we identify its neighboring samples using the visible features and reconstruct the representation of the full body by an outlierremovable graph neural network with all the neighboring samples as input. Extensive experiments show that the proposed approach obtains significant improvements. In the large-scale Occluded- DukeMTMC benchmark, our approach achieves 64.2% mAP and 67.6% rank-1 accuracy which outperforms the state-of-the-art approaches by large margins, i.e.,20.4% and 12.5%, respectively, indicating the effectiveness of our method on occluded Re-ID problem. Copyright © 2021, The Authors. All rights reserved.

关键词： Security systems

Suppressing Uncertainties for Large-Scale Facial Expression recognition

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Suppressing Uncertainties for Large-Scale Facial Expression ...

Conference on computer vision and pattern recognition (CVPR)

作者： Kai Wang Xiaojiang Peng Jianfei Yang Shijian Lu Yu Qiao ShenZhen Key Lab of Computer Vision and Pattern Recognition SIAT-SenseTime Joint Lab Shenzhen Institutes of Advanced Technology Chinese Academy of Science University of Chinese Academy of Sciences China Nanyang Technological University Singapore

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

Annotating a qualitative large-scale facial expression dataset is extremely difficult due to the uncertainties caused by ambiguous facial expressions, low-quality facial images, and the subjectiveness of annotators. These uncertainties suspend the progress of large-scale Facial Expression recognition (FER) in data-driven deep learning era. To address this problelm, this paper proposes to suppress the uncertainties by a simple yet efficient Self-Cure Network (SCN). Specifically, SCN suppresses the uncertainty from two different aspects: 1) a self-attention mechanism over FER dataset to weight each sample in training with a ranking regularization, and 2) a careful relabeling mechanism to modify the labels of these samples in the lowest-ranked group. Experiments on synthetic FER datasets and our collected WebEmotion dataset validate the effectiveness of our method. Results on public benchmarks demonstrate that our SCN outperforms current state-of-the-art methods with \textbf{88.14}\% on RAF-DB, \textbf{60.23}\% on AffectNet, and \textbf{89.35}\% on FERPlus.

关键词： Uncertainty Face recognition Feature extraction Noise measurement Training Face computer vision

Multiple Transfer Learning and Multi-label Balanced Training Strategies for Facial AU Detection In the Wild

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Multiple Transfer Learning and Multi-label Balanced Training...

IEEE computer Society Conference on computer vision and pattern recognition Workshops (CVPRW)

作者： Sijie Ji Kai Wang Xiaojiang Peng Jianfei Yang Zhaoyang Zeng Yu Qiao Nanyang Technological University Singapore ShenZhen Key Lab of Computer Vision and Pattern Recognition SIAT-SenseTime Joint Lab Shenzhen Institutes of Advanced Technology Chinese Academy of Science Sun Yat-Sen University China

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

This paper 1 presents siat-NTU solution and results of facial action unit (AU) detection in the EmotiNet Challenge 2020. The task aims to detect 23 AUs from facial images in the wild, and its main difficulties lie in the imbalanced AU distribution and discriminative feature learning. We tackle these difficulties from the following aspects. First, to address the unconstrained heterogeneity of in-the-wild images, we detect and align faces with multi-task convolutional neural networks (MTCNN). Second, by using multiple transfer strategies, we pre-train large CNNs on multiple related datasets, e.g. face recognition datasets and facial expression datasets, and fine-tune them on the EmotiNetdataset. Third, we employ a multi-label balanced sampling strategy and a weighted loss to mitigate the imbalance problem. Last but not the least, to further improve performance, we ensemble multiple models and optimize the thresholds for each AU. Our proposed solution achieves an accuracy of 90.13% and F1 of 44.10% in the final test phase. Our Code is available at: https://***/kaiwang960112/enc2020_au_detection.

关键词： Gold Task analysis Feature extraction Face recognition Training Mathematical model Muscles

COCAS: A Large-Scale Clothes Changing Person Dataset for Re-Identification

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COCAS: A Large-Scale Clothes Changing Person Dataset for Re-...

Conference on computer vision and pattern recognition (CVPR)

作者： Shijie Yu Shihua Li Dapeng Chen Rui Zhao Junjie Yan Yu Qiao ShenZhen Key Lab of Computer Vision and Pattern Recognition SIAT-SenseTime Joint Lab Shenzhen Institutes of Advanced Technology Chinese Academy of Science University of Chinese Academy of Sciences China Institute of Microelectronics of the Chinese Academy of Sciences

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

Recent years have witnessed great progress in person re-identification (re-id). Several academic benchmarks such as Market1501, CUHK03 and DukeMTMC play important roles to promote the re-id research. To our best knowledge, all the existing benchmarks assume the same person will have the same clothes. While in real-world scenarios, it is very often for a person to change clothes. To address the clothes changing person re-id problem, we construct a novel large-scale re-id benchmark named Clothes Changing Person Set (COCAS), which provides multiple images of the same identity with different clothes. COCAS totally contains 62,382 body images from 5,266 persons. Based on COCAS, we introduce a new person re-id setting for clothes changing problem, where the query includes both a clothes template and a person image taking another clothes. Moreover, we propose a two-branch network named Biometric-Clothes Network (BC-Net) which can effectively integrate biometric and clothes feature for re-id under our setting. Experiments show that it is feasible for clothes changing re-id with clothes templates.

关键词： Face Feature extraction Benchmark testing Protocols Biological system modeling Shape