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cs.CV2025
MMGeoLM: Hard Negative Contrastive Learning for Fine-Grained Geometric Understanding in Large Multimodal Models
Kai Sun, Yushi Bai, Zhen Yang +4
Large Multimodal Models (LMMs) typically build on ViTs (e.g., CLIP), yet their training with simple random in-batch negatives limits the ability to capture fine-grained visual diff…
cs.CV2025
An LMM for Efficient Video Understanding via Reinforced Compression of Video Cubes
Ji Qi, Yuan Yao, Yushi Bai +4
Large Multimodal Models (LMMs) uniformly perceive video frames, creating computational inefficiency for videos with inherently varying temporal information density. This paper pres…