Search Results for author:
Found 4 papers, 3 papers with code
Mini-Gemini: Mining the Potential of Multi-modality Vision Language Models
We try to narrow the gap by mining the potential of VLMs for better performance and any-to-any workflow from three aspects, i. e., high-resolution visual tokens, high-quality data, and VLM-guided generation.
Ranked #9 on
Visual Question Answering
on MM-Vet
GroupContrast: Semantic-aware Self-supervised Representation Learning for 3D Understanding
To address this issue, we propose GroupContrast, a novel approach that combines segment grouping and semantic-aware contrastive learning.
LLaMA-VID: An Image is Worth 2 Tokens in Large Language Models
Current VLMs, while proficient in tasks like image captioning and visual question answering, face computational burdens when processing long videos due to the excessive visual tokens.
Image Captioning
Video-based Generative Performance Benchmarking
+2
Hierarchical Dense Correlation Distillation for Few-Shot Segmentation-Extended Abstract
We hope our work can benefit broader industrial applications where novel classes with limited annotations are required to be decently identified.
