1 citations · 1 across the 8 of their papers we have counts for
4 papers · 1 filter
CoF: Coarse to Fine-Grained Image Understanding for Multi-modal Large Language Models
Yeyuan Wang, Dehong Gao, Bin Li +7
The impressive performance of Large Language Model (LLM) has prompted researchers to develop Multi-modal LLM (MLLM), which has shown great potential for various multi-modal tasks.…
Enhancing Fine-Grained Vision-Language Pretraining with Negative Augmented Samples
Yeyuan Wang, Dehong Gao, Lei Yi +4
Existing Vision-Language Pretraining (VLP) methods have achieved remarkable improvements across a variety of vision-language tasks, confirming their effectiveness in capturing coar…
MQM-APE: Toward High-Quality Error Annotation Predictors with Automatic Post-Editing in LLM Translation Evaluators
Qingyu Lu, Liang Ding, Kanjian Zhang +2
Large Language Models (LLMs) have shown significant potential as judges for Machine Translation (MT) quality assessment, providing both scores and fine-grained feedback. Although a…
Shifting Spotlight for Co-supervision: A Simple yet Efficient Single-branch Network to See Through Camouflage
Yang Hu, Jinxia Zhang, Kaihua Zhang +4
Camouflaged object detection (COD) remains a challenging task in computer vision. Existing methods often resort to additional branches for edge supervision, incurring substantial c…