3 citations · 10 across the 12 of their papers we have counts for
14 papers
Allo{SR}: Rectifying One-Step Super-Resolution to Stay Real via Allomorphic Generative Flows
Zihan Wang, Xudong Huang, Junbo Qiao +4
Real-world image super-resolution (Real-SR) has been revolutionized by leveraging the powerful generative priors from Diffusion Models (DMs) and Flow Matching (FM). However, existi…
From Sequential to Spatial: Reordering Autoregression for Efficient Visual Generation
Siyang Wang, Hanting Li, Wei Li +3
Inspired by the remarkable success of autoregressive models in language modeling, this paradigm has been widely adopted in visual generation. However, the sequential token-by-token…
DSPO: Direct Semantic Preference Optimization for Real-World Image Super-Resolution
Miaomiao Cai, Simiao Li, Wei Li +4
Recent advances in diffusion models have improved Real-World Image Super-Resolution (Real-ISR), but existing methods lack human feedback integration, risking misalignment with huma…
Multi-Granularity Semantic Revision for Large Language Model Distillation
Xiaoyu Liu, Yun Zhang, Wei Li +7
Knowledge distillation plays a key role in compressing the Large Language Models (LLMs), which boosts a small-size student model under large teacher models' guidance. However, exis…
GIM: A Million-scale Benchmark for Generative Image Manipulation Detection and Localization
Yirui Chen, Xudong Huang, Quan Zhang +9
The extraordinary ability of generative models emerges as a new trend in image editing and generating realistic images, posing a serious threat to the trustworthiness of multimedia…
Collaboration of Teachers for Semi-supervised Object Detection
Liyu Chen, Huaao Tang, Yi Wen +4
Recent semi-supervised object detection (SSOD) has achieved remarkable progress by leveraging unlabeled data for training. Mainstream SSOD methods rely on Consistency Regularizatio…
LIPT: Latency-aware Image Processing Transformer
Junbo Qiao, Wei Li, Haizhen Xie +5
Transformer is leading a trend in the field of image processing. Despite the great success that existing lightweight image processing transformers have achieved, they are tailored…
Knowledge Distillation with Multi-granularity Mixture of Priors for Image Super-Resolution
Simiao Li, Yun Zhang, Wei Li +5
Knowledge distillation (KD) is a promising yet challenging model compression technique that transfers rich learning representations from a well-performing but cumbersome teacher mo…
Distilling Semantic Priors from SAM to Efficient Image Restoration Models
Quan Zhang, Xiaoyu Liu, Wei Li +6
In image restoration (IR), leveraging semantic priors from segmentation models has been a common approach to improve performance. The recent segment anything model (SAM) has emerge…
IPT-V2: Efficient Image Processing Transformer using Hierarchical Attentions
Zhijun Tu, Kunpeng Du, Hanting Chen +4
Recent advances have demonstrated the powerful capability of transformer architecture in image restoration. However, our analysis indicates that existing transformerbased methods c…
GenDet: Towards Good Generalizations for AI-Generated Image Detection
Mingjian Zhu, Hanting Chen, Mouxiao Huang +4
The misuse of AI imagery can have harmful societal effects, prompting the creation of detectors to combat issues like the spread of fake news. Existing methods can effectively dete…
CBQ: Cross-Block Quantization for Large Language Models
Xin Ding, Xiaoyu Liu, Zhijun Tu +8
Post-training quantization (PTQ) has played a key role in compressing large language models (LLMs) with ultra-low costs. However, existing PTQ methods only focus on handling the ou…
IFT: Image Fusion Transformer for Ghost-free High Dynamic Range Imaging
Hailing Wang, Wei Li, Yuanyuan Xi +4
Multi-frame high dynamic range (HDR) imaging aims to reconstruct ghost-free images with photo-realistic details from content-complementary but spatially misaligned low dynamic rang…
Data Upcycling Knowledge Distillation for Image Super-Resolution
Yun Zhang, Wei Li, Simiao Li +6
Knowledge distillation (KD) compresses deep neural networks by transferring task-related knowledge from cumbersome pre-trained teacher models to compact student models. However, cu…