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Learning a Simple Low-light Image Enhancer from Paired Low-light Instances

Learning a Simple Low-light Image Enhancer from Paired Low-l...

引用

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

作者： Fu, Zhenqi Yang, Yan Tu, Xiaotong Huang, Yue Ding, Xinghao Ma, Kai-Kuang Xiamen Univ Sch Informat Key Lab Multimedia Trusted Percept & Efficient Co Minist Educ China Xiamen Peoples R China Hangzhou Dianzi Univ Sch Comp Sci & Technol Hangzhou Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore Singapore

ISBN: (纸本)9798350301298

Low-light Image Enhancement (LIE) aims at improving contrast and restoring details for images captured in low-light conditions. Most of the previous LIE algorithms adjust illumination using a single input image with several handcrafted priors. Those solutions, however, often fail in revealing image details due to the limited information in a single image and the poor adaptability of handcrafted priors. To this end, we propose PairLIE, an unsupervised approach that learns adaptive priors from low-light image pairs. First, the network is expected to generate the same clean images as the two inputs share the same image content. To achieve this, we impose the network with the Retinex theory and make the two reflectance components consistent. Second, to assist the Retinex decomposition, we propose to remove inappropriate features in the raw image with a simple self-supervised mechanism. Extensive experiments on public datasets show that the proposed PairLIE achieves comparable performance against the state-of-the-art approaches with a simpler network and fewer handcrafted priors. Code is available at: https: //***/zhenqifu/PairLIE.

关键词： Low-level vision

来源：评论

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SparseViT: Revisiting Activation Sparsity for Efficient High-Resolution vision Transformer

SparseViT: Revisiting Activation Sparsity for Efficient High...

引用

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

作者： Chen, Xuanyao Liu, Zhijian Tang, Haotian Yi, Li Zhao, Hang Han, Song Shanghai Qi Zhi Inst Shanghai Peoples R China Fudan Univ Shanghai Peoples R China Tsinghua Univ Beijing Peoples R China MIT Cambridge MA USA

ISBN: (纸本)9798350301298

High-resolution images enable neural networks to learn richer visual representations. However, this improved performance comes at the cost of growing computational complexity, hindering their usage in latency-sensitive applications. As not all pixels are equal, skipping computations for less-important regions offers a simple and effective measure to reduce the computation. This, however, is hard to be translated into actual speedup for CNNs since it breaks the regularity of the dense convolution workload. In this paper, we introduce SparseViT that revisits activation sparsity for recent window-based vision transformers (ViTs). As window attentions are naturally batched over blocks, actual speedup with window activation pruning becomes possible: i.e., similar to 50% latency reduction with 60% sparsity. Different layers should be assigned with different pruning ratios due to their diverse sensitivities and computational costs. We introduce sparsity-aware adaptation and apply the evolutionary search to efficiently find the optimal layerwise sparsity configuration within the vast search space. SparseViT achieves speedups of 1.5x, 1.4x, and 1.3x compared to its dense counterpart in monocular 3D object detection, 2D instance segmentation, and 2D semantic segmentation, respectively, with negligible to no loss of accuracy.

关键词： Efficient and scalable vision

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Fast GraspNeXt: A Fast Self-Attention Neural Network Architecture for Multi-task Learning in computer vision Tasks for Robotic Grasping on the Edge

Fast GraspNeXt: A Fast Self-Attention Neural Network Archite...

引用

2023 ieee/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Wong, Alexander Wu, Yifan Abbasi, Saad Nair, Saeejith Chen, Yuhao Shafiee, Mohammad Javad Vision and Image Processing Research Group University of Waterloo Canada Waterloo Artificial Intelligence Institute WaterlooON Canada DarwinAI Corp. WaterlooON Canada

ISBN: (纸本)9798350302493

Multi-task learning has shown considerable promise for improving the performance of deep learning-driven vision systems for the purpose of robotic grasping. However, high architectural and computational complexity can result in poor suitability for deployment on embedded devices that are typically leveraged in robotic arms for real-world manufacturing and warehouse environments. As such, the design of highly efficient multi-task deep neural network architectures tailored for computer vision tasks for robotic grasping on the edge is highly desired for widespread adoption in manufacturing environments. Motivated by this, we propose Fast GraspNeXt, a fast self-attention neural network architecture tailored for embedded multi-task learning in computer vision tasks for robotic grasping. To build Fast GraspNeXt, we leverage a generative network architecture search strategy with a set of architectural constraints customized to achieve a strong balance between multitask learning performance and embedded inference efficiency. Experimental results on the MetaGraspNet benchmark dataset show that the Fast GraspNeXt network design achieves the highest performance (average precision (AP), accuracy, and mean squared error (MSE)) across multiple computer vision tasks when compared to other efficient multi-task network architecture designs, while having only 17.8M parameters (about >5× smaller), 259 GFLOPs (as much as >5× lower) and as much as >3.15× faster on a NVIDIA Jetson TX2 embedded processor. © 2023 ieee.

关键词： computer vision

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ScaleKD: Distilling Scale-Aware Knowledge in Small Object Detector

ScaleKD: Distilling Scale-Aware Knowledge in Small Object De...

引用

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

作者： Zhu, Yichen Zhou, Qiqi Liu, Ning Xu, Zhiyuan Ou, Zhicai Mou, Xiaofeng Tang, Jian Midea Grp Foshan Peoples R China Shanghai Univ Elect Power Shanghai Peoples R China

ISBN: (纸本)9798350301298

Despite the prominent success of general object detection, the performance and efficiency of Small Object Detection (SOD) are still unsatisfactory. Unlike existing works that struggle to balance the trade-off between inference speed and SOD performance, in this paper, we propose a novel Scale-aware Knowledge Distillation (ScaleKD), which transfers knowledge of a complex teacher model to a compact student model. We design two novel modules to boost the quality of knowledge transfer in distillation for SOD: 1) a scale-decoupled feature distillation module that disentangled teacher's feature representation into multi-scale embedding that enables explicit feature mimicking of the student model on small objects. 2) a cross-scale assistant to refine the noisy and uninformative bounding boxes prediction student models, which can mislead the student model and impair the efficacy of knowledge distillation. A multi-scale cross-attention layer is established to capture the multi-scale semantic information to improve the student model. We conduct experiments on COCO and VisDrone datasets with diverse types of models, i.e., two-stage and one-stage detectors, to evaluate our proposed method. Our ScaleKD achieves superior performance on general detection performance and obtains spectacular improvement regarding the SOD performance.

关键词： Efficient and scalable vision

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Spatial-Frequency Mutual Learning for Face Super-Resolution

Spatial-Frequency Mutual Learning for Face Super-Resolution

引用

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

作者： Wang, Chenyang Jiang, Junjun Zhong, Zhiwei Liu, Xianming Harbin Inst Technol Sch Comp Sci & Technol Harbin Peoples R China

ISBN: (纸本)9798350301298

Face super-resolution (FSR) aims to reconstruct high-resolution (HR) face images from the low-resolution (LR) ones. With the advent of deep learning, the FSR technique has achieved significant breakthroughs. However, existing FSR methods either have a fixed receptive field or fail to maintain facial structure, limiting the FSR performance. To circumvent this problem, Fourier transform is introduced, which can capture global facial structure information and achieve image-size receptive field. Relying on the Fourier transform, we devise a spatial-frequency mutual network (SFMNet) for FSR, which is the first FSR method to explore the correlations between spatial and frequency domains as far as we know. To be specific, our SFMNet is a two-branch network equipped with a spatial branch and a frequency branch. Benefiting from the property of Fourier transform, the frequency branch can achieve image-size receptive field and capture global dependency while the spatial branch can extract local dependency. Considering that these dependencies are complementary and both favorable for FSR, we further develop a frequency-spatial interaction block (FSIB) which mutually amalgamates the complementary spatial and frequency information to enhance the capability of the model. Quantitative and qualitative experimental results show that the proposed method outperforms state-of-the-art FSR methods in recovering face images. The implementation and model will be released at https://***/wcy-cs/SFMNet.

关键词： Low-level vision

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Decoupled Semantic Prototypes enable learning from diverse annotation types for semi-weakly segmentation in expert-driven domains

Decoupled Semantic Prototypes enable learning from diverse a...

引用

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

作者： Reiss, Simon Seibold, Constantin Freytag, Alexander Rodner, Erik Stiefelhagen, Rainer Karlsruhe Inst Technol Karlsruhe Germany Carl Zeiss Oberkochen Germany Univ Appl Sci Berlin Berlin Germany

ISBN: (纸本)9798350301298

A vast amount of images and pixel-wise annotations allowed our community to build scalable segmentation solutions for natural domains. However, the transfer to expert-driven domains like microscopy applications or medical healthcare remains difficult as domain experts are a critical factor due to their limited availability for providing pixel-wise annotations. To enable affordable segmentation solutions for such domains, we need training strategies which can simultaneously handle diverse annotation types and are not bound to costly pixel-wise annotations. In this work, we analyze existing training algorithms towards their flexibility for different annotation types and scalability to small annotation regimes. We conduct an extensive evaluation in the challenging domain of organelle segmentation and find that existing semi- and semi-weakly supervised training algorithms are not able to fully exploit diverse annotation types. Driven by our findings, we introduce Decoupled Semantic Prototypes (DSP) as a training method for semantic segmentation which enables learning from annotation types as diverse as image-level-, point-, bounding box-, and pixel-wise annotations and which leads to remarkable accuracy gains over existing solutions for semi-weakly segmentation.

关键词： cell microscopy Medical and biological vision

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Deep Discriminative Spatial and Temporal Network for Efficient Video Deblurring

Deep Discriminative Spatial and Temporal Network for Efficie...

引用

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

作者： Pan, Jinshan Xu, Boming Dong, Jiangxin Ge, Jianjun Tang, Jinhui Nanjing Univ Sci & Technol Nanjing Peoples R China China Elect Technol Grp Corp Beijing Peoples R China

ISBN: (纸本)9798350301298

How to effectively explore spatial and temporal information is important for video deblurring. In contrast to existing methods that directly align adjacent frames without discrimination, we develop a deep discriminative spatial and temporal network to facilitate the spatial and temporal feature exploration for better video deblurring. We first develop a channel-wise gated dynamic network to adaptively explore the spatial information. As adjacent frames usually contain different contents, directly stacking features of adjacent frames without discrimination may affect the latent clear frame restoration. Therefore, we develop a simple yet effective discriminative temporal feature fusion module to obtain useful temporal features for latent frame restoration. Moreover, to utilize the information from long-range frames, we develop a wavelet-based feature propagation method that takes the discriminative temporal feature fusion module as the basic unit to effectively propagate main structures from long-range frames for better video deblurring. We show that the proposed method does not require additional alignment methods and performs favorably against state-of-the-art ones on benchmark datasets in terms of accuracy and model complexity.

关键词： Low-level vision

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Spectral Enhanced Rectangle Transformer for Hyperspectral Image Denoising

Spectral Enhanced Rectangle Transformer for Hyperspectral Im...

引用

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

作者： Li, Miaoyu Liu, Ji Fu, Ying Zhang, Yulun Dou, Dejing Beijing Inst Technol Beijing Peoples R China Baidu Inc Beijing Peoples R China Swiss Fed Inst Technol Zurich Switzerland BCG X Boston MA USA

ISBN: (纸本)9798350301298

Denoising is a crucial step for hyperspectral image (HSI) applications. Though witnessing the great power of deep learning, existing HSI denoising methods suffer from limitations in capturing the non-local self-similarity. Transformers have shown potential in capturing long-range dependencies, but few attempts have been made with specifically designed Transformer to model the spatial and spectral correlation in HSIs. In this paper, we address these issues by proposing a spectral enhanced rectangle Transformer, driving it to explore the non-local spatial similarity and global spectral low-rank property of HSIs. For the former, we exploit the rectangle self-attention horizontally and vertically to capture the non-local similarity in the spatial domain. For the latter, we design a spectral enhancement module that is capable of extracting global underlying low-rank property of spatial-spectral cubes to suppress noise, while enabling the interactions among non-overlapping spatial rectangles. Extensive experiments have been conducted on both synthetic noisy HSIs and real noisy HSIs, showing the effectiveness of our proposed method in terms of both objective metric and subjective visual quality. The code is available at https://***/MyuLi/SERT.

关键词： Low-level vision

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Fair Federated Medical Image Segmentation via Client Contribution Estimation

Fair Federated Medical Image Segmentation via Client Contrib...

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

作者： Jiang, Meirui Roth, Holger R. Li, Wenqi Yang, Dong Zhao, Can Nath, Vishwesh Xu, Daguang Dou, Qi Xu, Ziyue Chinese Univ Hong Kong Hong Kong Peoples R China NVIDIA Santa Clara CA 95051 USA

ISBN: (纸本)9798350301298

How to ensure fairness is an important topic in federated learning (FL). Recent studies have investigated how to reward clients based on their contribution (collaboration fairness), and how to achieve uniformity of performance across clients (performance fairness). Despite achieving progress on either one, we argue that it is critical to consider them together, in order to engage and motivate more diverse clients joining FL to derive a high-quality global model. In this work, we propose a novel method to optimize both types of fairness simultaneously. Specifically, we propose to estimate client contribution in gradient and data space. In gradient space, we monitor the gradient direction differences of each client with respect to others. And in data space, we measure the prediction error on client data using an auxiliary model. Based on this contribution estimation, we propose a FL method, federated training via contribution estimation (FedCE), i.e., using estimation as global model aggregation weights. We have theoretically analyzed our method and empirically evaluated it on two real-world medical datasets. The effectiveness of our approach has been validated with significant performance improvements, better collaboration fairness, better performance fairness, and comprehensive analytical studies. Code is available at https://***. io/NVFlare/research/fed-ce

关键词： cell microscopy Medical and biological vision

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Stitchable Neural Networks

Stitchable Neural Networks

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

作者： Pan, Zizheng Cai, Jianfei Zhuang, Bohan Monash Univ ZIP Lab Clayton Vic Australia

ISBN: (纸本)9798350301298

The public model zoo containing enormous powerful pretrained model families (e.g., ResNet/DeiT) has reached an unprecedented scope than ever, which significantly contributes to the success of deep learning. As each model family consists of pretrained models with diverse scales (e.g., DeiT-Ti/S/B), it naturally arises a fundamental question of how to efficiently assemble these readily available models in a family for dynamic accuracy-efficiency trade-offs at runtime. To this end, we present Stitchable Neural Networks (SN-Net), a novel scalable and efficient framework for model deployment. It cheaply produces numerous networks with different complexity and performance trade-offs given a family of pretrained neural networks, which we call anchors. Specifically, SN-Net splits the anchors across the blocks/layers and then stitches them together with simple stitching layers to map the activations from one anchor to another. With only a few epochs of training, SN-Net effectively interpolates between the performance of anchors with varying scales. At runtime, SN-Net can instantly adapt to dynamic resource constraints by switching the stitching positions. Extensive experiments on ImageNet classification demonstrate that SN-Net can obtain on-par or even better performance than many individually trained networks while supporting diverse deployment scenarios. For example, by stitching Swin Transformers, we challenge hundreds of models in Timm model zoo with a single network. We believe this new elastic model framework can serve as a strong baseline for further research in wider communities.

关键词： Efficient and scalable vision

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