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VmambaIR: Visual State Space Model for Image Restoration
Yuan Shi;Bin Xia;Xiaoyu Jin;Xing Wang;Tianyu Zhao;Xin Xia;Xuefeng Xiao;Wenming Yang
IEEE Transactions on Circuits and Systems for Video Technology
Year: 2025 | Early Access Article |
Cited by: Papers (4)
Image restoration is a critical task in low-level computer vision, aiming to restore high-quality images from degraded inputs. Various models, such as convolutional neural networks (CNNs), generative adversarial networks (GANs), transformers, and diffusion models (DMs), have been employed to address this problem with significant impact. However, CNNs have limitations in capturing long-range depend...Show More
Multi-Objective Evolutionary for Object Detection Mobile Architectures Search
2024 2nd International Conference on Machine Vision, Image Processing & Imaging Technology (MVIPIT)
Year: 2024 | Conference Paper |
Recently, neural architecture search has achieved great success on classification tasks for mobile devices. The backbone network for object detection is usually obtained on image classification tasks. However, the architecture searched through the classification task is sub-optimal because of the gap between the task of classification and object detection. While work focuses on backbone network ar...Show More
AutoDiffusion: Training-Free Optimization of Time Steps and Architectures for Automated Diffusion Model Acceleration
Lijiang Li;Huixia Li;Xiawu Zheng;Jie Wu;Xuefeng Xiao;Rui Wang;Min Zheng;Xin Pan;Fei Chao;Rongrong Ji
2023 IEEE/CVF International Conference on Computer Vision (ICCV)
Year: 2023 | Conference Paper |
Cited by: Papers (4)
Diffusion models are emerging expressive generative models, in which a large number of time steps (inference steps) are required for a single image generation. To accelerate such tedious process, reducing steps uniformly is considered as an undisputed principle of diffusion models. We consider that such a uniform assumption is not the optimal solution in practice; i.e., we can find different optim...Show More
UGC: Unified GAN Compression for Efficient Image-to-Image Translation
Yuxi Ren;Jie Wu;Peng Zhang;Manlin Zhang;Xuefeng Xiao;Qian He;Rui Wang;Min Zheng;Xin Pan
2023 IEEE/CVF International Conference on Computer Vision (ICCV)
Year: 2023 | Conference Paper |
Cited by: Papers (3)
Recent years have witnessed the prevailing progress of Generative Adversarial Networks (GANs) in image-to-image translation. However, the success of these GAN models hinges on ponderous computational costs and labor-expensive training data. Current efficient GAN learning techniques often fall into two orthogonal aspects: i) model slimming via reduced calculation costs; ii) data/label-efficient lea...Show More
AlignDet: Aligning Pre-training and Fine-tuning in Object Detection
Ming Li;Jie Wu;Xionghui Wang;Chen Chen;Jie Qin;Xuefeng Xiao;Rui Wang;Min Zheng;Xin Pan
2023 IEEE/CVF International Conference on Computer Vision (ICCV)
Year: 2023 | Conference Paper |
Cited by: Papers (8)
The paradigm of large-scale pre-training followed by downstream fine-tuning has been widely employed in various object detection algorithms. In this paper, we reveal discrepancies in data, model, and task between the pre-training and fine-tuning procedure in existing practices, which implicitly limit the detector’s performance, generalization ability, and convergence speed. To this end, we propose...Show More
Solving Oscillation Problem in Post-Training Quantization Through a Theoretical Perspective
Yuexiao Ma;Huixia Li;Xiawu Zheng;Xuefeng Xiao;Rui Wang;Shilei Wen;Xin Pan;Fei Chao;Rongrong Ji
2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2023 | Conference Paper |
Cited by: Papers (9)
Post-training quantization (PTQ) is widely regarded as one of the most efficient compression methods practically, benefitting from its data privacy and low computation costs. We argue that an overlooked problem of oscillation is in the PTQ methods. In this paper, we take the initiative to explore and present a theoretical proof to explain why such a problem is essential in PTQ. And then, we try to...Show More
FreeSeg: Unified, Universal and Open-Vocabulary Image Segmentation
Jie Oin;Jie Wu;Pengxiang Yan;Ming Li;Ren Yuxi;Xuefeng Xiao;Yitong Wang;Rui Wang;Shilei Wen;Xin Pan;Xingang Wang
2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2023 | Conference Paper |
Cited by: Papers (29)
Recently, open-vocabulary learning has emerged to accomplish segmentation for arbitrary categories of text-based descriptions, which popularizes the segmentation system to more general-purpose application scenarios. However, existing methods devote to designing specialized architectures or parameters for specific segmentation tasks. These customized design paradigms lead to fragmentation between v...Show More
Progressive Automatic Design of Search Space for One-Shot Neural Architecture Search
2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
Year: 2022 | Conference Paper |
Cited by: Papers (14)
Neural Architecture Search (NAS) has attracted growing interest. To reduce the search cost, recent work has explored weight sharing across models and made major progress in One-Shot NAS. However, it has been observed that a model with higher one-shot model accuracy does not necessarily perform better when stand-alone trained. To address this issue, in this paper, we propose Progressive Automatic D...Show More
Online Multi-Granularity Distillation for GAN Compression
2021 IEEE/CVF International Conference on Computer Vision (ICCV)
Year: 2021 | Conference Paper |
Cited by: Papers (29)
Generative Adversarial Networks (GANs) have witnessed prevailing success in yielding outstanding images, however, they are burdensome to deploy on resource-constrained devices due to ponderous computational costs and hulking memory usage. Although recent efforts on compressing GANs have acquired remarkable results, they still exist potential model redundancies and can be further compressed. To sol...Show More
Box-Level Tube Tracking and Refinement for Vehicles Anomaly Detection
2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Year: 2021 | Conference Paper |
Cited by: Papers (6)
Traffic Anomaly detection is an essential computer vision task and plays a critical role in video structure analysis and urban traffic analysis. In this paper, we propose a box-level tracking and refinement algorithm to identify anomaly detection in road scenes. We first link the detection results to construct candidate spatio-temporal tubes via greedy search. Then the box-level refinement scheme ...Show More
Fast and Accurate Quantized Camera Scene Detection on Smartphones, Mobile AI 2021 Challenge: Report
Andrey Ignatov;Grigory Malivenko;Radu Timofte;Sheng Chen;Xin Xia;Zhaoyan Liu;Yuwei Zhang;Feng Zhu;Jiashi Li;Xuefeng Xiao;Yuan Tian;Xinglong Wu;Christos Kyrkou;Yixin Chen;Zexin Zhang;Yunbo Peng;Yue Lin;Saikat Dutta;Sourya Dipta Das;Nisarg A. Shah;Himanshu Kumar;Chao Ge;Pei-Lin Wu;Jin-Hua Du;Andrew Batutin;Juan Pablo Federico;Konrad Lyda;Levon Khojoyan;Abhishek Thanki;Sayak Paul;Shahid Siddiqui
2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Year: 2021 | Conference Paper |
Cited by: Papers (15)
Camera scene detection is among the most popular computer vision problem on smartphones. While many custom solutions were developed for this task by phone vendors, none of the designed models were available publicly up until now. To address this problem, we introduce the first Mobile AI challenge, where the target is to develop quantized deep learning-based camera scene classification solutions th...Show More
Fast and Accurate Quantized Camera Scene Detection on Smartphones, Mobile AI 2021 Challenge: Report
Andrey Ignatov;Grigory Malivenko;Radu Timofte;Sheng Chen;Xin Xia;Zhaoyan Liu;Yuwei Zhang;Feng Zhu;Jiashi Li;Xuefeng Xiao;Yuan Tian;Xinglong Wu;Christos Kyrkou;Yixin Chen;Zexin Zhang;Yunbo Peng;Yue Lin;Saikat Dutta;Sourya Dipta Das;Nisarg A. Shah;Himanshu Kumar;Chao Ge;Pei-Lin Wu;Jin-Hua Du;Andrew Batutin;Juan Pablo Federico;Konrad Lyda;Levon Khojoyan;Abhishek Thanki;Sayak Paul;Shahid Siddiqui
Year: 2021 | Conference Paper |
Accelerating and Compressing LSTM Based Model for Online Handwritten Chinese Character Recognition
Yafeng Yang;Kaihuan Liang;Xuefeng Xiao;Zecheng Xie;Lianwen Jin;Jun Sun;Weiying Zhou
2018 16th International Conference on Frontiers in Handwriting Recognition (ICFHR)
Year: 2018 | Conference Paper |
Cited by: Papers (12)
With the development of deep learning tools, the online handwritten Chinese character recognition (HCCR) performance has been greatly improved by using deep neural networks (DNNs) especially for long short-term memory (LSTM). However, DNNs suffer from large consumption of computation and storage resources, which may cause problems for service providers, such as server pressure, longer service late...Show More
Design of a Very Compact CNN Classifier for Online Handwritten Chinese Character Recognition Using DropWeight and Global Pooling
Xuefeng Xiao;Yafeng Yang;Tasweer Ahmad;Lianwen Jin;Tianhai Chang
2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR)
Year: 2017 | Conference Paper |
Cited by: Papers (13)
Currently, owing to the ubiquity of mobile devices, online handwritten Chinese character recognition (HCCR) has become one of the suitable choice for feeding input to cell phones and tablet devices. Over the past few years, larger and deeper convolutional neural networks (CNNs) have extensively been employed for improving character recognition performance. However, its substantial storage requirem...Show More
A Comprehensive Analysis of Misclassified Handwritten Chinese Character Samples by Incorporating Human Recognition
Kaihuan Liang;Lianwen Jin;Zecheng Xie;Xuefeng Xiao;Weiguo Huang
2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR)
Year: 2017 | Conference Paper |
Cited by: Papers (2)
The development of convolutional neural networks (CNN) has led to revolutionary progress in the resolution of the offline handwritten Chinese character recognition (HCCR) problem. As the recognition rate on a standard offline HCCR testbed is outstanding, a few samples that remain misclassified have kindled our interest. In this paper, with the help of human recognition results, we present a compre...Show More
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