180 citations · 284 across the 30 of their papers we have counts for
6 papers · 1 filter
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…
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…
LongWriter-V: Enabling Ultra-Long and High-Fidelity Generation in Vision-Language Models
Shangqing Tu, Yucheng Wang, Daniel Zhang-Li +8
Existing Large Vision-Language Models (LVLMs) can process inputs with context lengths up to 128k visual and text tokens, yet they struggle to generate coherent outputs beyond 1,000…
CogCoM: A Visual Language Model with Chain-of-Manipulations Reasoning
Ji Qi, Ming Ding, Weihan Wang +8
Vision-Language Models (VLMs) have demonstrated their broad effectiveness thanks to extensive training in aligning visual instructions to responses. However, such training of concl…
Text-Image Conditioned Diffusion for Consistent Text-to-3D Generation
Yuze He, Yushi Bai, Matthieu Lin +5
By lifting the pre-trained 2D diffusion models into Neural Radiance Fields (NeRFs), text-to-3D generation methods have made great progress. Many state-of-the-art approaches usually…
TBench: Benchmarking Current Progress in Text-to-3D Generation
Yuze He, Yushi Bai, Matthieu Lin +6
Recent methods in text-to-3D leverage powerful pretrained diffusion models to optimize NeRF. Notably, these methods are able to produce high-quality 3D scenes without training on 3…