6 papers
When to Lock Attention: Training-Free KV Control in Video Diffusion
Tianyi Zeng, Jincheng Gao, Tianyi Wang +8
Maintaining background consistency while enhancing foreground quality remains a core challenge in video editing. Injecting full-image information often leads to background artifact…
QUSR: Quality-Aware and Uncertainty-Guided Image Super-Resolution Diffusion Model
Junjie Yin, Jiaju Li, Hanfa Xing
Diffusion-based image super-resolution (ISR) has shown strong potential, but it still struggles in real-world scenarios where degradations are unknown and spatially non-uniform, of…
Enhancing Intent Understanding for Ambiguous prompt: A Human-Machine Co-Adaption Strategy
Yangfan He, Jianhui Wang, Yijin Wang +18
Current image generation systems produce high-quality images but struggle with ambiguous user prompts, making interpretation of actual user intentions difficult. Many users must mo…
Enhancing Low-Cost Video Editing with Lightweight Adaptors and Temporal-Aware Inversion
Yangfan He, Sida Li, Jianhui Wang +11
Recent advancements in text-to-image (T2I) generation using diffusion models have enabled cost-effective video-editing applications by leveraging pre-trained models, eliminating th…
PromptLNet: Region-Adaptive Aesthetic Enhancement via Prompt Guidance in Low-Light Enhancement Net
Jun Yin, Yangfan He, Miao Zhang +4
Learning and improving large language models through human preference feedback has become a mainstream approach, but it has rarely been applied to the field of low-light image enha…
TSCnet: A Text-driven Semantic-level Controllable Framework for Customized Low-Light Image Enhancement
Miao Zhang, Jun Yin, Pengyu Zeng +3
Deep learning-based image enhancement methods show significant advantages in reducing noise and improving visibility in low-light conditions. These methods are typically based on o…