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Transformer for Single Image Super-Resolution

Transformer for Single Image Super-Resolution

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Lu, Zhisheng Li, Juncheng Liu, Hong Huang, Chaoyan Zhang, Linlin Zeng, Tieyong Peking Univ Shenzhen Grad Sch Shenzhen Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China Nanjing Univ Posts & Telecommun Nanjing Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Single image super-resolution (SISR) has witnessed great strides with the development of deep learning. However, most existing studies focus on building more complex networks with a massive number of layers. Recently, more and more researchers start to explore the application of Transformer in computer vision tasks. However, the heavy computational cost and high GPU memory occupation of the vision Transformer cannot be ignored. In this paper, we propose a novel Efficient Super-Resolution Transformer (ESRT) for SISR. ESRT is a hybrid model, which consists of a Lightweight CNN Backbone (LCB) and a Lightweight Transformer Backbone (LTB). Among them, LCB can dynamically adjust the size of the feature map to extract deep features with a low computational costs. LTB is composed of a series of Efficient Transformers (ET), which occupies a small GPU memory occupation, thanks to the specially designed Efficient Multi-Head Attention (EMHA). Extensive experiments show that ESRT achieves competitive results with low computational cost. Compared with the original Transformer which occupies 16,057M GPU memory, ESRT only occupies 4,191M GPU memory. All codes are available at https://***/luissen/ESRT.

关键词： computer vision Adaptation models Computational modeling Superresolution Memory management Graphics processing units Transformers

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IMDeception: Grouped Information Distilling Super-Resolution Network

IMDeception: Grouped Information Distilling Super-Resolution...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Ayazoglu, Mustafa Aselsan Res Ankara Turkey

ISBN: (纸本)9781665487399

Single-Image-Super-Resolution (SISR) is a classical computer vision problem that has benefited from the recent advancements in deep learning methods, especially the advancements of convolutional neural networks (CNN). Although state-of-the-art methods improve the performance of SISR on several datasets, direct application of these networks for practical use is still an issue due to heavy computational load. For this purpose, recently, researchers have focused on more efficient and high-performing network structures. Information multi-distilling network (IMDN) is one of the highly efficient SISR networks with high performance and low computational load. IMDN achieves this efficiency with various mechanisms such as Intermediate Information Collection (IIC), working in a global setting, Progressive Refinement Module (PRM), and Contrast Aware Channel Attention (CCA), employed in a local setting. These mechanisms, however, do not equally contribute to the efficiency and performance of IMDN. In this work, we propose the Global Progressive Refinement Module (GPRM) as a less parameter-demanding alternative to the IIC module for feature aggregation. To further decrease the number of parameters and floating point operations per second (FLOPS), we also propose Grouped Information Distilling Blocks (GIDB). Using the proposed structures, we design an efficient SISR network called IMDeception. Experiments reveal that the proposed network performs on par with state-of-the-art models despite having a limited number of parameters and FLOPS. Furthermore, using grouped convolutions as a building block of GIDB increases room for further optimization during deployment. To show its potential, the proposed model was deployed on NVIDIA Jetson Xavier AGX and it has been shown that it can run in real-time on this edge device.

关键词： Deep learning computer vision conferences Superresolution Real-time systems Hardware Timing

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Learning CLIP Guided Visual-Text Fusion Transformer for Video-based Pedestrian Attribute recognition

Learning CLIP Guided Visual-Text Fusion Transformer for Vide...

引用

2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Zhu, Jun Jin, Jiandong Yang, Zihan Wu, Xiaohao Wang, Xiao Anhui University School of Computer Science and Technology Hefei230601 China Anhui University School of Artificial Intelligence Hefei230601 China

ISBN: (纸本)9798350302493

Existing pedestrian attribute recognition (PAR) algorithms are mainly developed based on a static image. However, the performance is not reliable for images with challenging factors, such as heavy occlusion, motion blur, etc. In this work, we propose to understand human attributes using video frames that can make full use of temporal information. Specifically, we formulate the video-based PAR as a vision-language fusion problem and adopt pre-trained big models CLIP to extract the feature embeddings of given video frames. To better utilize the semantic information, we take the attribute list as another input and transform the attribute words/phrase into the corresponding sentence via split, expand, and prompt. Then, the text encoder of CLIP is utilized for language embedding. The averaged visual tokens and text tokens are concatenated and fed into a fusion Transformer for multi-modal interactive learning. The enhanced tokens will be fed into a classification head for pedestrian attribute prediction. Extensive experiments on a large-scale video-based PAR dataset fully validated the effectiveness of our proposed framework. Both the source code and pre-trained models will be released at https://***/Event-AHU/VTF-PAR. © 2023 ieee.

关键词： Embeddings

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Fast building segmentation from satellite imagery and few local labels

Fast building segmentation from satellite imagery and few lo...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Robinson, Caleb Ortiz, Anthony Park, Hogeun Gracia, Nancy Lozano Kaw, Jon Kher Sederholm, Tina Dodhia, Rahul Ferres, Juan M. Lavista Microsoft AI Good Res Lab Redmond WA 98052 USA World Bank 1818 H St NW Washington DC 20433 USA

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Innovations in computer vision algorithms for satellite image analysis can enable us to explore global challenges such as urbanization and land use change at the planetary level. However, domain shift problems are a common occurrence when trying to replicate models that drive these analyses to new areas, particularly in the developing world. If a model is trained with imagery and labels from one location, then it usually will not generalize well to new locations where the content of the imagery and data distributions are different. In this work, we consider the setting in which we have a single large satellite imagery scene over which we want to solve an applied problem - building footprint segmentation. Here, we do not necessarily need to worry about creating a model that generalizes past the borders of our scene but can instead train a local model. We show that surprisingly few labels are needed to solve the building segmentation problem with very high-resolution (0.5m/px) satellite imagery with this setting in mind. Our best model trained with just 527 sparse polygon annotations (an equivalent of 1500x1500 densely labeled pixels) has a recall of 0.87 over held out footprints and a R2 of 0.93 on the task of counting the number of buildings in 200x200 meter windows. We apply our models over high-resolution imagery in Amman, Jordan in a case study on urban change detection. [GRAPHICS] .

关键词： Training Image segmentation computer vision Technological innovation Satellites Computational modeling Buildings

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NTIRE 2024 Image Shadow Removal Challenge Report

NTIRE 2024 Image Shadow Removal Challenge Report

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Vasluianu, Florin-Alexandru Seizinger, Tim Wu, Zongwei Zhou, Zhuyun Chen, Cailian Zhou, Han Timofte, Radu Dong, Wei Tian, Yuqiong Chen, Jun Lul, Xin Zhu, Yurui Wang, Xi Li, Dong Xiao, Jie Zhang, Yunpeng Fu, Xueyang Zha, Zheng-Jun Zhang, Zhao Zhao, Suiyi Wang, Bo Luo, Yan Wei, Yanyan Xiaol, Jie Ful, Xueyang Zhal, Zheng-Jun Lu, Xin Zhao, Zhihao Sun, Long Yang, Tingting Pan, Jinshan Tang, Jinhui Dong, Jiangxin Benjdira, Bilel Nassif, Mohammed Koubaa, Anis Elhayek, Ahmed Ali, Anas M. Tokoro, Kyotaro Kawai, Kento Yokoyama, Kaname Seno, Takuya Kondo, Yuki Ukita, Norimichi Li, Chenghua Yang, Bo Wu, Zhiqi Chen, Gao Yu, Yihan Chen, Sixiang Mane, Kai Ye, Tian Zou, Wenbin Lin, Yunlong Xing, Zhaohu Bai, Jinbin Chai, Wenhao Zhu, Lei Maheshwari, Ritik Verma, Rakshank Tekchandanil, Rahul Hambarde, Praful Tazil, Satya Narayan Vipparthi, Santosh Kumar Murala, Subrahmanyam Lee, Jaeho Kim, Seongwan Sharif, S. M. A. Khujaev, Nodirkhuja Tsoy, Roman Gao, Fan Yan, Weidan Shao, Wenze Zhang, Dengyin Chen, Bin Zhang, Siqi Qian, Yanxin Chen, Yuanbin Zhou, Yuanbo Tong, Tong Wei, Rongfeng Sun, Ruiqi Liu, Yue Akalwadi, Nikhil Joshi, Amogh Malagi, Sampada Desai, Chaitra Tabib, Ramesh Ashok Mudenagudi, Uma Murtaza, Ali Khairuddin, Uswah Faudzi, Ahmad'Athif Mohd Dukre, Adinath Deshmukh, Vivek Phutke, Shruti S. Kulkarni, Ashutosh Gonde, Anil Karthik, Arun K. Manasa, N. Priyal, Shri Hari Hao, Wei Yan, Xingzhuo Fu, Minghan Univ Wurzburg Comp Vis Lab IFI & CAIDAS Wurzburg Germany Shanghai Jiao Tong Univ Shanghai Peoples R China McMaster Univ Dept Elect & Comp Engn Hamilton ON Canada Univ Sci & Technol China Hefei Peoples R China Hefei Univ Technol Hefei Peoples R China Nanjing Univ Sci & Technol Nanjing Jiangsu Peoples R China Prince Sultan Univ Robot & Internet Things Lab Riyadh 12435 Saudi Arabia Prince Muqrin Univ Artificial Intelligence Dept Medinah 41311 Saudi Arabia Toyota Technol Inst Intelligent Informat Media Lab Nagoya Japan Nanjing Artificial Intelligence Res IA AiRiA Nanjing Peoples R China Nanjing Normal Univ High Sch Jiangning Campus Nanjing Peoples R China Hong Kong Univ Sci & Technol Guangzhou Guangzhou Peoples R China South China Univ Technol Guangzhou Peoples R China Xiamen Univ Xiamen Peoples R China Natl Univ Singapore Singapore Singapore Univ Washington Seattle WA 98195 USA GEC Ajmer Kiranipura India CVPR Lab IIT Ropar Rupnagar India SCSS Trinity Coll Dublin Dublin Ireland Opt AI Seoul South Korea Nanjing Univ Posts & Telecommun Nanjing Peoples R China Fuzhou Univ Fuzhou Peoples R China Univ Hong Kong Logist & Supply Chain MultiTech R&D Ctr Hong Kong Peoples R China Sun Yat Sen Univ Guangzhou Peoples R China KLE Technol Univ Ctr Excellence Visual Intelligence CEVI Hubballi Karnataka India KLE Technol Univ Sch Elect & Commun Engn Hubballi Karnataka India KLE Technol Univ Sch Comp Sci & Engn Hubballi Karnataka India Univ Teknol Malaysia Malaysia Japan Int Inst Technol MMT Kuala Lumpur Malaysia Univ Teknol Malaysia Ctr Artificial Intelligence & Robot CAIRO Kuala Lumpur Malaysia Shri Guru Gobind Singhji Inst Engn & Technol Nanded India Indian Inst Technol Ropar Comp Vis & Pattern Recognit Lab Rupnagar India Trinity Coll Dublin Sch Comp Sci & Stat CVPR Lab Dublin Ireland Shiv Nadar Univ Sch Engn Chennai Tamil Nadu India Fortinet Inc Sunnyvale CA USA Bosch Investment Ltd Shanghai Peoples R China Univ Saskatchewan Saskatoon

ISBN: (纸本)9798350365474

This work reviews the results of the NTIRE 2024 Challenge on Shadow Removal. Building on the last year edition, the current challenge was organized in two tracks, with a track focused on increased fidelity reconstruction, and a separate ranking for high performing perceptual quality solutions. Track 1 (fidelity) had 214 registered participants, with 17 teams submitting in the final phase, while Track 2 (perceptual) registered 185 participants, resulting in 18 final phase submissions. Both tracks were based on data from the WSRD dataset, simulating interactions between self-shadows and cast shadows, with a large variety of represented objects, textures, and materials. Improved image alignment enabled increased fidelity reconstruction, with restored frames mostly indistinguishable from the references images for top performing solutions.

关键词： lighting removal restoration shadow

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Beyond AUROC & co. for evaluating out-of-distribution detection performance

Beyond AUROC & co. for evaluating out-of-distribution detect...

引用

2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Humblot-Renaux, Galadrielle Escalera, Sergio Moeslund, Thomas B. Aalborg University Visual Analysis and Perception Lab Denmark Universitat Autònoma de Barcelona Computer Vision Center Spain Universitat de Barcelona Dept. of Mathematics and Informatics Spain

ISBN: (纸本)9798350302493

While there has been a growing research interest in developing out-of-distribution (OOD) detection methods, there has been comparably little discussion around how these methods should be evaluated. Given their relevance for safe(r) AI, it is important to examine whether the basis for comparing OOD detection methods is consistent with practical needs. In this work, we take a closer look at the go-to metrics for evaluating OOD detection, and question the approach of exclusively reducing OOD detection to a binary classification task with little consideration for the detection threshold. We illustrate the limitations of current metrics (AUROC & its friends) and propose a new metric - Area Under the Threshold Curve (AUTC), which explicitly penalizes poor separation between ID and OOD samples. Scripts and data are available at https://***/glhr/beyond-auroc © 2023 ieee.

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Blind Image Inpainting via Omni-dimensional Gated Attention and Wavelet Queries

Blind Image Inpainting via Omni-dimensional Gated Attention ...

引用

2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Phutke, Shruti S. Kulkarni, Ashutosh Vipparthi, Santosh Kumar Murala, Subrahmanyam Indian Institute of Technology Ropar Computer Vision and Pattern Recognition Lab Punjab Rupnagar India

ISBN: (纸本)9798350302493

Blind image inpainting is a crucial restoration task that does not demand additional mask information to restore the corrupted regions. Yet, it is a very less explored research area due to the difficulty in discriminating between corrupted and valid regions. There exist very few approaches for blind image inpainting which sometimes fail at producing plausible inpainted images. Since they follow a common practice of predicting the corrupted regions and then inpaint them. To skip the corrupted region prediction step and obtain better results, in this work, we propose a novel end-to-end architecture for blind image inpainting consisting of wavelet query multi-head attention transformer block and the omni-dimensional gated attention. The proposed wavelet query multi-head attention in the transformer block provides encoder features via processed wavelet coefficients as query to the multi-head attention. Further, the proposed omni-dimensional gated attention effectively provides all dimensional attentive features from the encoder to the respective decoder. Our proposed approach is compared numerically and visually with existing state-of-the-art methods for blind image inpainting on different standard datasets. The comparative and ablation studies prove the effectiveness of the proposed approach for blind image inpainting. The testing code is available at : https://***/shrutiphutke/Blind-Omni-Wav-Net © 2023 ieee.

关键词： Restoration

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Unpaired Face Restoration via Learnable Cross-Quality Shift

Unpaired Face Restoration via Learnable Cross-Quality Shift

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Dong, Yangyi Zhang, Xiaoyun Wang, Zhixin Zhang, Ya Chen, Siheng Wang, Yanfeng Shanghai Jiao Tong Univ Cooperat Medianet Innovat Ctr Shanghai Peoples R China Shanghai AI Lab Shanghai Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Face restoration aims to recover high-quality (HQ) face images from low-quality (LQ) ones with various unknown degradations. Unpaired face restoration approaches focus on the adaptation to unseen degradations, which is a more challenging setting. Recently, generative facial priors of StyleGAN are used to improve the restoration capability of paired face restoration methods. For unpaired methods, however, using face priors is a challenge due to the lack of paired supervision. To address this issue, we take advantage of the editing capabilities of StyleGAN's latent code and propose a novel learnable cross-quality shift. The proposed learnable cross-quality shift not only introduces the generative facial priors into the unpaired framework, but also enables the straight-forward addition/subtraction in the latent feature space to achieve quality conversion. Furthermore, we design a two-branch framework with the proposed cross-quality shift to deal with unpaired data and improve the fidelity of restoration. With the unpaired framework, our method can be fine-tuned on images with unseen degradation. Experimental results show that (i) compared to state-of-the-art methods, our method improves performances under moderate and severe degradation situations;and (ii) both the proposed learnable cross-quality shift and the two-branch framework benefit the restoration performance.

关键词： Degradation computer vision Codes Face recognition conferences Image restoration Faces

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Does Interference Exist When Training a Once-For-All Network?

Does Interference Exist When Training a Once-For-All Network...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Shipard, Jordan Wiliem, Arnold Fookes, Clinton Queensland Univ Technol Signal Proc Artificial Intelligence & Vis Technol Brisbane Qld Australia Sentient Vis Syst Port Melbourne Vic Australia

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

The Once-For-All (OFA) method offers an excellent pathway to deploy a trained neural network model into multiple target platforms by utilising the supernet-subnet architecture. Once trained, a subnet can be derived from the supernet (both architecture and trained weights) and deployed directly to the target platform with little to no retraining or fine-tuning. To train the subnet population, OFA uses a novel training method called Progressive Shrinking (PS) which is designed to limit the negative impact of interference during training. It is believed that higher interference during training results in lower subnet population accuracies. In this work we take a second look at this interference effect. Surprisingly, we find that interference mitigation strategies do not have a large impact on the overall subnet population performance. Instead, we find the subnet architecture selection bias during training to be a more important aspect. To show this, we propose a simple-yet-effective method called Random Subnet Sampling (RSS), which does not have mitigation on the interference effect. Despite no mitigation, RSS is able to produce a better performing subnet population than PS in four small-to-medium-sized datasets;suggesting that the interference effect does not play a pivotal role in these datasets. Due to its simplicity, RSS provides a 1.9x reduction in training times compared to PS. A 6.1x reduction can also be achieved with a reasonable drop in performance when the number of RSS training epochs are reduced.

关键词： Training computer vision Codes conferences Sociology Neural networks Interference

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Inferring Affective Experience from the Big Picture Metaphor: A Two-dimensional Visual Breadth Model

Inferring Affective Experience from the Big Picture Metaphor...

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2023 ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2023

作者： Tong, Song Duan, Jingyi Liang, Xuefeng Kumada, Takatsune Peng, Kaiping Nakashima, Ryoichi Tsinghua University Department of Psychology Beijing China Xidian University School of Artificial Intelligence Shaanxi China Kyoto University Graduate School of Informatics Kyoto Japan

ISBN: (纸本)9798350302493

This study explores the psychological significance of the commonly used visual metaphor 'seeing the big picture' and examines whether and how it leads to positive experiences in real-life situations. To elucidate this phenomenon, a two-dimensional model of visual breadth is proposed, then respectively operationalized by two computer vision approaches. Our approaches are evaluated on a collected data set with 29,216 photos. The results revealed that physical and contextual breadth are two essential visual structures that compose a 'big picture'. Furthermore, these two visual breadths interactively shape people's affective experiences. This study provides insight into the psychological implications of the 'big picture' metaphor and sheds light on its practical potential for computer vision approaches and affective computing in the wild. © 2023 ieee.

关键词： computer vision

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