most citedDynamic Contrastive Knowledge Distillation for Efficient Image Restoration

2 citations · 2 across the 4 of their papers we have counts for

collaborators

5 papers

cs.CV2025

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…

cs.CV2024★ 2 cited

Dynamic Contrastive Knowledge Distillation for Efficient Image Restoration

Yunshuai Zhou, Junbo Qiao, Jincheng Liao +6

Knowledge distillation (KD) is a valuable yet challenging approach that enhances a compact student network by learning from a high-performance but cumbersome teacher model. However…

cs.CL2024

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…

cs.CV2024

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…

cs.CV2024

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…