5 papers · 1 filter
ES-VP : Energy-Shaped Dynamic Visual Prompting for Efficient Model Adaptation
Can Jin, Ying Li, Jingchen Sun +5
Visual prompting (VP) has emerged as a parameter-efficient method for adapting pre-trained models to downstream tasks. However, existing approaches encounter a trade-off between fl…
RankE: End-to-End Post-Training for Discrete Text-to-Image Generation with Decoder Co-Evolution
Siyong Jian, Siyuan Li, Luyuan Zhang +5
Discrete autoregressive (AR) text-to-image (T2I) models pair a VQ tokenizer with an AR policy, and current post-training pipelines optimize only the policy while keeping the VQ dec…
TRIO: Token Reduction via Inference-Objective Guidance for Efficient Vision-Language Models
Haokui Zhang, Congyang Ou, Dawei Yan +5
Recently, reducing redundant visual tokens in vision-language models (VLMs) to accelerate VLM inference has emerged as a hot topic. However, most existing methods rely on heuristic…
CC-Pan: Channel-wise Compression based Diffusion for Efficient Pan-Sharpening
Junjie Li, Congyang Ou, Haokui Zhang +3
Recently, diffusion models have brought novel insights to pan-sharpening and notably boosted fusion precision. However, most existing models perform diffusion in the pixel space an…
LoR-VP: Low-Rank Visual Prompting for Efficient Vision Model Adaptation
Can Jin, Ying Li, Mingyu Zhao +6
Visual prompting has gained popularity as a method for adapting pre-trained models to specific tasks, particularly in the realm of parameter-efficient tuning. However, existing vis…