28 citations · 55 across the 7 of their papers we have counts for
7 papers
OtterHD: A High-Resolution Multi-modality Model
Bo Li, Peiyuan Zhang, Jingkang Yang +3
In this paper, we present OtterHD-8B, an innovative multimodal model evolved from Fuyu-8B, specifically engineered to interpret high-resolution visual inputs with granular precisio…
Large Language Models are Visual Reasoning Coordinators
Liangyu Chen, Bo Li, Sheng Shen +5
Visual reasoning requires multimodal perception and commonsense cognition of the world. Recently, multiple vision-language models (VLMs) have been proposed with excellent commonsen…
MIMIC-IT: Multi-Modal In-Context Instruction Tuning
Bo Li, Yuanhan Zhang, Liangyu Chen +5
High-quality instructions and responses are essential for the zero-shot performance of large language models on interactive natural language tasks. For interactive vision-language…
SAD: Segment Any RGBD
Jun Cen, Yizheng Wu, Kewei Wang +6
The Segment Anything Model (SAM) has demonstrated its effectiveness in segmenting any part of 2D RGB images. However, SAM exhibits a stronger emphasis on texture information while…
On-Device Domain Generalization
Kaiyang Zhou, Yuanhan Zhang, Yuhang Zang +3
We present a systematic study of domain generalization (DG) for tiny neural networks. This problem is critical to on-device machine learning applications but has been overlooked in…
Panoptic Scene Graph Generation
Jingkang Yang, Yi Zhe Ang, Zujin Guo +3
Existing research addresses scene graph generation (SGG) -- a critical technology for scene understanding in images -- from a detection perspective, i.e., objects are detected usin…