168 citations · 1.8k across the 114 of their papers we have counts for
60 papers · 1 filter
Diffusion Models for Image Restoration and Enhancement: A Comprehensive Survey
Xin Li, Yulin Ren, Xin Jin +5
Image restoration (IR) has been an indispensable and challenging task in the low-level vision field, which strives to improve the subjective quality of images distorted by various…
NVTC: Nonlinear Vector Transform Coding
Runsen Feng, Zongyu Guo, Weiping Li +1
In theory, vector quantization (VQ) is always better than scalar quantization (SQ) in terms of rate-distortion (R-D) performance. Recent state-of-the-art methods for neural image c…
Prompt-ICM: A Unified Framework towards Image Coding for Machines with Task-driven Prompts
Ruoyu Feng, Jinming Liu, Xin Jin +3
Image coding for machines (ICM) aims to compress images to support downstream AI analysis instead of human perception. For ICM, developing a unified codec to reduce information red…
Inpaint Anything: Segment Anything Meets Image Inpainting
Tao Yu, Runseng Feng, Ruoyu Feng +4
Modern image inpainting systems, despite the significant progress, often struggle with mask selection and holes filling. Based on Segment-Anything Model (SAM), we make the first at…
Learning Distortion Invariant Representation for Image Restoration from A Causality Perspective
Xin Li, Bingchen Li, Xin Jin +2
In recent years, we have witnessed the great advancement of Deep neural networks (DNNs) in image restoration. However, a critical limitation is that they cannot generalize well to…
Semantic-aware Message Broadcasting for Efficient Unsupervised Domain Adaptation
Xin Li, Cuiling Lan, Guoqiang Wei +1
Vision transformer has demonstrated great potential in abundant vision tasks. However, it also inevitably suffers from poor generalization capability when the distribution shift oc…
Task Residual for Tuning Vision-Language Models
Tao Yu, Zhihe Lu, Xin Jin +2
Large-scale vision-language models (VLMs) pre-trained on billion-level data have learned general visual representations and broad visual concepts. In principle, the well-learned kn…
MiNL: Micro-images based Neural Representation for Light Fields
Hanxin Zhu, Henan Wang, Zhibo Chen
Traditional representations for light fields can be separated into two types: explicit representation and implicit representation. Unlike explicit representation that represents li…
Learned Lossless JPEG Transcoding via Joint Lossy and Residual Compression
Xiaoshuai Fan, Xin Li, Zhibo Chen
As a commonly-used image compression format, JPEG has been broadly applied in the transmission and storage of images. To further reduce the compression cost while maintaining the q…
HST: Hierarchical Swin Transformer for Compressed Image Super-resolution
Bingchen Li, Xin Li, Yiting Lu +3
Compressed Image Super-resolution has achieved great attention in recent years, where images are degraded with compression artifacts and low-resolution artifacts. Since the complex…
StyleAM: Perception-Oriented Unsupervised Domain Adaption for Non-reference Image Quality Assessment
Yiting Lu, Xin Li, Jianzhao Liu +1
Deep neural networks (DNNs) have shown great potential in non-reference image quality assessment (NR-IQA). However, the annotation of NR-IQA is labor-intensive and time-consuming,…
Image Coding for Machines with Omnipotent Feature Learning
Ruoyu Feng, Xin Jin, Zongyu Guo +6
Image Coding for Machines (ICM) aims to compress images for AI tasks analysis rather than meeting human perception. Learning a kind of feature that is both general (for AI tasks) a…
SwinIQA: Learned Swin Distance for Compressed Image Quality Assessment
Jianzhao Liu, Xin Li, Yanding Peng +2
Image compression has raised widespread interest recently due to its significant importance for multimedia storage and transmission. Meanwhile, a reliable image quality assessment…
Deep Frequency Filtering for Domain Generalization
Shiqi Lin, Zhizheng Zhang, Zhipeng Huang +9
Improving the generalization ability of Deep Neural Networks (DNNs) is critical for their practical uses, which has been a longstanding challenge. Some theoretical studies have unc…
Active Token Mixer
Guoqiang Wei, Zhizheng Zhang, Cuiling Lan +2
The three existing dominant network families, i.e., CNNs, Transformers, and MLPs, differ from each other mainly in the ways of fusing spatial contextual information, leaving design…
Semantically Video Coding: Instill Static-Dynamic Clues into Structured Bitstream for AI Tasks
Xin Jin, Ruoyu Feng, Simeng Sun +3
Traditional media coding schemes typically encode image/video into a semantic-unknown binary stream, which fails to directly support downstream intelligent tasks at the bitstream l…
SelectAugment: Hierarchical Deterministic Sample Selection for Data Augmentation
Shiqi Lin, Zhizheng Zhang, Xin Li +2
Data augmentation (DA) has been widely investigated to facilitate model optimization in many tasks. However, in most cases, data augmentation is randomly performed for each trainin…
A Close Look at Few-shot Real Image Super-resolution from the Distortion Relation Perspective
Xin Li, Xin Jin, Jun Fu +3
Collecting amounts of distorted/clean image pairs in the real world is non-trivial, which seriously limits the practical applications of these supervised learning-based methods on…
Meta Clustering Learning for Large-scale Unsupervised Person Re-identification
Xin Jin, Tianyu He, Xu Shen +5
Unsupervised Person Re-identification (U-ReID) with pseudo labeling recently reaches a competitive performance compared to fully-supervised ReID methods based on modern clustering…
ToAlign: Task-oriented Alignment for Unsupervised Domain Adaptation
Guoqiang Wei, Cuiling Lan, Wenjun Zeng +2
Unsupervised domain adaptive classifcation intends to improve the classifcation performance on unlabeled target domain. To alleviate the adverse effect of domain shift, many approa…
Disentanglement-based Cross-Domain Feature Augmentation for Effective Unsupervised Domain Adaptive Person Re-identification
Zhizheng Zhang, Cuiling Lan, Wenjun Zeng +4
Unsupervised domain adaptive (UDA) person re-identification (ReID) aims to transfer the knowledge from the labeled source domain to the unlabeled target domain for person matching.…
MetaAlign: Coordinating Domain Alignment and Classification for Unsupervised Domain Adaptation
Guoqiang Wei, Cuiling Lan, Wenjun Zeng +1
For unsupervised domain adaptation (UDA), to alleviate the effect of domain shift, many approaches align the source and target domains in the feature space by adversarial learning…
Cloth-Changing Person Re-identification from A Single Image with Gait Prediction and Regularization
Xin Jin, Tianyu He, Kecheng Zheng +7
Cloth-Changing person re-identification (CC-ReID) aims at matching the same person across different locations over a long-duration, e.g., over days, and therefore inevitably meets…
Re-energizing Domain Discriminator with Sample Relabeling for Adversarial Domain Adaptation
Xin Jin, Cuiling Lan, Wenjun Zeng +1
Many unsupervised domain adaptation (UDA) methods exploit domain adversarial training to align the features to reduce domain gap, where a feature extractor is trained to fool a dom…