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EfficientFCN: Holistically-Guided Decoding for Semantic Segmentation 1

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16th European Conference on computer vision, ECCV 2020

作者： Liu, Jianbo He, Junjun Zhang, Jiawei Ren, Jimmy S. Li, Hongsheng CUHK-SenseTime Joint Laboratory The Chinese University of Hong Kong Shatin Hong Kong Shenzhen Key Lab of Computer Vision and Pattern Recognition Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Beijing China SenseTime Research Beijing China

ISBN: (数字)9783030585747

ISBN: (纸本)9783030585730

Both performance and efficiency are important to semantic segmentation. State-of-the-art semantic segmentation algorithms are mostly based on dilated Fully Convolutional Networks (dilatedFCN), which adopt dilated convolutions in the backbone networks to extract high-resolution feature maps for achieving high-performance segmentation performance. However, due to many convolution operations are conducted on the high-resolution feature maps, such dilatedFCN-based methods result in large computational complexity and memory consumption. To balance the performance and efficiency, there also exist encoder-decoder structures that gradually recover the spatial information by combining multi-level feature maps from the encoder. However, the performances of existing encoder-decoder methods are far from comparable with the dilatedFCN-based methods. In this paper, we propose the EfficientFCN, whose backbone is a common ImageNet pretrained network without any dilated convolution. A holistically-guided decoder is introduced to obtain the high-resolution semantic-rich feature maps via the multi-scale features from the encoder. The decoding task is converted to novel codebook generation and codeword assembly task, which takes advantages of the high-level and low-level features from the encoder. Such a framework achieves comparable or even better performance than state-of-the-art methods with only 1/3 of the computational cost. Extensive experiments on PASCAL Context, PASCAL VOC, ADE20K validate the effectiveness of the proposed EfficientFCN. © 2020, Springer Nature Switzerland AG.

关键词： Semantic Segmentation

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Robust text line detection in equipment nameplate images

Robust text line detection in equipment nameplate images

引用

2019 IEEE International Conference on Robotics and Biomimetics, ROBIO 2019

作者： Lai, Jiangyu Guo, Lanqing Qiao, Yu Chen, Xiaolong Zhang, Zhengfu Liu, Canping Li, Ying Fu, Bin Guangzhou Power Supply Bureau Co. Ltd. Guangzhou China ShenZhen Key Lab of Computer Vision and Pattern Recognition SIATSenseTime Joint Lab Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society China

ISBN: (纸本)9781728163215

Scene text detection for equipment nameplates in the wild is important for equipment inspection robot since it enables inspection robot to take specific actions for different equipment's. Although text detection in images has achieved great progress in recent years, the detection for equipment nameplates faces several challenges such as extreme illumination and distortion which significantly decrease the detection performance. In this paper, we propose a deep text detection model Robust Text Line Detection (RTLD) for locating word level text instances in equipment cards. Specifically, the proposed model first employs a corner detection module to determine the four corner points of each nameplate, and then a carefully designed image transformed module transforms the irregular nameplate region into a rectangular region. Finally, text detection module is introduced to locate every word level text instance in the transformed images. We conduct extensive experiments to examine our proposed methods on real equipment nameplate images. Our model achieves 91.2% precision and 92.6% recall on Equipment Nameplate Dataset. The experimental results demonstrate the effectiveness of our models. © 2019 IEEE.

关键词： Nameplates

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Tensor Low-Rank Reconstruction for Semantic Segmentation 1

引用

16th European Conference on computer vision, ECCV 2020

作者： Chen, Wanli Zhu, Xinge Sun, Ruoqi He, Junjun Li, Ruiyu Shen, Xiaoyong Yu, Bei The Chinese University of Hong Kong New Territories Hong Kong Shanghai Jiao Tong University Shanghai China ShenZhen Key Lab of Computer Vision and Pattern Recognition SIAT-SenseTime Joint Lab Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Beijing China SmartMore Shenzhen China

ISBN: (数字)9783030585204

ISBN: (纸本)9783030585198

Context information plays an indispensable role in the success of semantic segmentation. Recently, non-local self-attention based methods are proved to be effective for context information collection. Since the desired context consists of spatial-wise and channel-wise attentions, 3D representation is an appropriate formulation. However, these non-local methods describe 3D context information based on a 2D similarity matrix, where space compression may lead to channel-wise attention missing. An alternative is to model the contextual information directly without compression. However, this effort confronts a fundamental difficulty, namely the high-rank property of context information. In this paper, we propose a new approach to model the 3D context representations, which not only avoids the space compression but also tackles the high-rank difficulty. Here, inspired by tensor canonical-polyadic decomposition theory (i.e, a high-rank tensor can be expressed as a combination of rank-1 tensors.), we design a low-rank-to-high-rank context reconstruction framework (i.e, RecoNet). Specifically, we first introduce the tensor generation module (TGM), which generates a number of rank-1 tensors to capture fragments of context feature. Then we use these rank-1 tensors to recover the high-rank context features through our proposed tensor reconstruction module (TRM). Extensive experiments show that our method achieves state-of-the-art on various public datasets. Additionally, our proposed method has more than 100 times less computational cost compared with conventional non-local-based methods. © 2020, Springer Nature Switzerland AG.

关键词： Tensors

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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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Learning Discriminative Representation For Facial Expression recognition From Uncertainties

Learning Discriminative Representation For Facial Expression...

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IEEE International Conference on Image Processing

作者： Xingyu Fan Zhongying Deng Kai Wang Xiaojiang Peng Yu Qiao Shenzhen Key Lab of Computer Vision and Pattern Recognition Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China

ISBN: (数字)9781728163956

ISBN: (纸本)9781728163963

Recent progresses on Facial Expression recognition (FER) heavily rely on deep learning models trained with large scale datasets. However, large-scale facial expression datasets always suffer from annotation uncertainties caused by ambiguous expressions, low-quality facial images, and the subjectiveness of annotators, which limits FER performance. To address this challenge, this paper introduces novel Rayleigh and weighted-softmax loss from two aspects. First, we propose Rayleigh loss to extract discriminative representation, which aims at minimizing within-class distances and maximizing inter-class distances simultaneously. Moreover, Rayleigh loss has a Euclidean form which make it easily be optimized with SGD and be combined with other forms. Second, we introduce a weight to measure the uncertainty of a given sample, by considering its distance to class center. Extensive experiments on RAF-DB, FERPlus and AffectNet show the effectiveness of our method with SOTA performance.

关键词： Weight measurement Emotion recognition Uncertainty Face recognition Image processing Measurement uncertainty Task analysis

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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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The equipment nameplate dataset for scene text detection and recognition

The equipment nameplate dataset for scene text detection and...

引用

2019 IEEE International Conference on Robotics and Biomimetics, ROBIO 2019

作者： Chen, Xiaolong Zhang, Zhengfu Qiao, Yu Zhang, Pu Guo, Lanqing Chen, Wenrui Chen, Chen Fu, Bin Guangzhou Power Supply Bureau Co. Ltd. Guangzhou China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences ShenZhen Key Lab of Computer Vision and Pattern Recognition SIAT-SenseTime Joint Lab China SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society China

ISBN: (纸本)9781728163215

In this paper, we introduce the Equipment Nameplate Dataset, a large dataset for scene text detection and recognition. Natural images in this dataset are taken in the wild and thus this dataset includes various intra-class inconsistency such as ill illumination conditions and partly occluded, which makes our dataset more challenging than other datasets. In order to make people train detection and recognition model separately, we annotate our dataset not only word instance, but also text region by using rectangle bounding boxes. Some detailed statistics information about our dataset will be given so that people can use them to analyse and develop their own models. Moreover, we use our dataset to test some famous detection and recognition models and present the corresponding results in order to make researcher compare them with their own models. Dataset will be publicly available on the website. © 2019 IEEE.

关键词： computer vision

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Orientation robust scene text recognition in natural scene

Orientation robust scene text recognition in natural scene

引用

2019 IEEE International Conference on Robotics and Biomimetics, ROBIO 2019

作者： Chen, Xiaolong Zhang, Zhengfu Qiao, Yu Lai, Jiangyu Jiang, Jian Zhang, Zeyu Fu, Bin Guangzhou Power Supply Bureau Co. Ltd. Guangzhou China ShenZhen Key Lab of Computer Vision and Pattern Recognition SIAT-SenseTime Joint Lab Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society China

ISBN: (纸本)9781728163215

In recent years, scene text recognition has achieved significant improvement and various state-of-the-art recognition approaches have been proposed. This paper focused on recognizing text in natural photos of equipment nameplates, which has wide applications in industrial automations. This task only receives little attentions in previous works. The challenge of this problem comes from multi-orientation, curved, noisy and blurry text patches in equipment nameplates. To address this problem, we propose a deep model for text recognition in multi-oriented nameplates, namely, Orientation Robust Scene Text recognition (ORSTR). Specifically, our model employs a rectification module to transform curved, distorted or multi-orientation text to near-horizontal text with a carefully designed rectification module. Once the near-horizontal text has been generated, recognition network will output the predictions of text patches. Our scene text recognition model achieves 90.8% recognition accuracy on equipment nameplate dataset which outperforms previous scene text recognition model (CRNN) about 0.8%. Several extensive experiments have been conducted to verify the effectiveness of our model. © 2019 IEEE.

关键词： Nameplates

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PIPAL: A Large-Scale Image Quality Assessment Dataset for Perceptual Image Restoration 16th

PIPAL: A Large-Scale Image Quality Assessment Dataset for Pe...

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16th European Conference on computer vision, ECCV 2020

作者： Jinjin, Gu Haoming, Cai Haoyu, Chen Xiaoxing, Ye Ren, Jimmy S. Chao, Dong The School of Data Science The Chinese University of Hong Kong Shenzhen China ShenZhen Key Lab of Computer Vision and Pattern Recognition SIAT-SenseTime Joint Lab Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China SenseTime Research Science Park Hong Kong SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

ISBN: (纸本)9783030586201

Image quality assessment (IQA) is the key factor for the fast development of image restoration (IR) algorithms. The most recent IR methods based on Generative Adversarial Networks (GANs) have achieved significant improvement in visual performance, but also presented great challenges for quantitative evaluation. Notably, we observe an increasing inconsistency between perceptual quality and the evaluation results. Then we raise two questions: (1) Can existing IQA methods objectively evaluate recent IR algorithms? (2) When focus on beating current benchmarks, are we getting better IR algorithms? To answer these questions and promote the development of IQA methods, we contribute a large-scale IQA dataset, called Perceptual Image Processing Algorithms (PIPAL) dataset. Especially, this dataset includes the results of GAN-based methods, which are missing in previous datasets. We collect more than 1.13 million human judgments to assign subjective scores for PIPAL images using the more reliable "Elo system". Based on PIPAL, we present new benchmarks for both IQA and super-resolution methods. Our results indicate that existing IQA methods cannot fairly evaluate GAN-based IR algorithms. While using appropriate evaluation methods is important, IQA methods should also be updated along with the development of IR algorithms. At last, we improve the performance of IQA networks on GAN-based distortions by introducing anti-aliasing pooling. Experiments show the effectiveness of the proposed method. © 2020, Springer Nature Switzerland AG.

关键词： Generative adversarial networks

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Fast single image dehazing through Edge-Guided Interpolated Filter

Fast single image dehazing through Edge-Guided Interpolated ...

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IAPR International Conference on Machine vision Applications (MVA)

作者： Ximei Zhu Ying Li Yu Qiao Shenzhen Key lab of Computer Vision Pattern Recognition The Chinese University of Hong Kong Hong Kong SAR

ISBN: (纸本)9781479982479

Images and videos taken in foggy weather often suffer from low visibility. Recent studies demonstrate the effectiveness of dark channel prior [3] and guided filter [4] based approaches for image dehazing. However, these methods require high computational cost which makes them infeasible for realtime and embedding systems. In this paper, we propose Edge-Guided Interpolated Filter (EGIF) for fast image and video dehazing. The main contributions are twofold. Firstly, we develop Guided Interpolated Filter (GIF) to significantly speed up the estimation of transmission map, which is the most computational cost step in previous methods. Secondly, we utilize edge map as guidance image in GIF to enhance the fine details in dehazed images. Experimental results show that GIF can largely improve the computational efficiency and achieve comparable dehazing performance as previous guided filter based methods. EGIF can further enhance the sharpness of transmission map. Our method can achieve real-time processing for image of size 1024 × 768 with single CPU core (2GHz).

关键词： Image edge detection Interpolation Videos Real-time systems Image restoration computer vision Atmosphere

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