6 citations · 6 across the 7 of their papers we have counts for
6 papers · 1 filter
Efficiency Follows Global-Local Decoupling
Zhenyu Yang, Gensheng Pei, Tao Chen +4
Modern vision models must capture image-level context without sacrificing local detail while remaining computationally affordable. We revisit this tradeoff and advance a simple pri…
Towards Remote Sensing Change Detection with Neural Memory
Zhenyu Yang, Gensheng Pei, Yazhou Yao +3
Remote sensing change detection is essential for environmental monitoring, urban planning, and related applications. However, current methods often struggle to capture long-range d…
AbductiveMLLM: Boosting Visual Abductive Reasoning Within MLLMs
Boyu Chang, Qi Wang, Xi Guo +3
Visual abductive reasoning (VAR) is a challenging task that requires AI systems to infer the most likely explanation for incomplete visual observations. While recent MLLMs develop…
Decouple before Align: Visual Disentanglement Enhances Prompt Tuning
Fei Zhang, Tianfei Zhou, Jiangchao Yao +3
Prompt tuning (PT), as an emerging resource-efficient fine-tuning paradigm, has showcased remarkable effectiveness in improving the task-specific transferability of vision-language…
Seeing What Matters: Empowering CLIP with Patch Generation-to-Selection
Gensheng Pei, Tao Chen, Yujia Wang +4
The CLIP model has demonstrated significant advancements in aligning visual and language modalities through large-scale pre-training on image-text pairs, enabling strong zero-shot…
LFMamba: Light Field Image Super-Resolution with State Space Model
Wang xia, Yao Lu, Shunzhou Wang +3
Recent years have witnessed significant advancements in light field image super-resolution (LFSR) owing to the progress of modern neural networks. However, these methods often face…