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cs.CV2025
RewardMap: Tackling Sparse Rewards in Fine-grained Visual Reasoning via Multi-Stage Reinforcement Learning
Sicheng Feng, Kaiwen Tuo, Song Wang +3
Fine-grained visual reasoning remains a core challenge for multimodal large language models (MLLMs). The recently introduced ReasonMap highlights this gap by showing that even adva…
cs.CV2025
A Survey of Token Compression for Efficient Multimodal Large Language Models
Kele Shao, Keda Tao, Kejia Zhang +7
Multimodal large language models (MLLMs) have made remarkable strides, largely driven by their ability to process increasingly long and complex contexts, such as high-resolution im…
cs.CV2025
ReasonMap: Towards Fine-Grained Visual Reasoning from Transit Maps
Sicheng Feng, Song Wang, Shuyi Ouyang +5
Multimodal large language models (MLLMs) have demonstrated significant progress in semantic scene understanding and text-image alignment, with reasoning variants enhancing performa…