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Baichuan Zhou

bczhou

https://baichuanzhou.github.io/

baichuanzhou

AI & ML interests

Computer Vision

Recent Activity

New activity 18 days ago

bczhou/LOKI:Add dataset card, link to paper

upvoted a paper 29 days ago

MIA-DPO: Multi-Image Augmented Direct Preference Optimization For Large Vision-Language Models

upvoted a paper about 1 month ago

AutoTrain: No-code training for state-of-the-art models

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bczhou's activity

New activity in bczhou/LOKI 18 days ago

Add dataset card, link to paper

#1 opened 19 days ago by

nielsr

upvoted a paper 29 days ago

MIA-DPO: Multi-Image Augmented Direct Preference Optimization For Large Vision-Language Models

Paper • 2410.17637 • Published Oct 23 • 34

upvoted 2 papers about 1 month ago

AutoTrain: No-code training for state-of-the-art models

Paper • 2410.15735 • Published Oct 21 • 57

CompassJudger-1: All-in-one Judge Model Helps Model Evaluation and Evolution

Paper • 2410.16256 • Published Oct 21 • 58

updated a dataset about 1 month ago

bczhou/LOKI

Preview • Updated 18 days ago • 47

upvoted 6 papers about 1 month ago

Harnessing Webpage UIs for Text-Rich Visual Understanding

Paper • 2410.13824 • Published Oct 17 • 29

MixEval-X: Any-to-Any Evaluations from Real-World Data Mixtures

Paper • 2410.13754 • Published Oct 17 • 74

DocLayout-YOLO: Enhancing Document Layout Analysis through Diverse Synthetic Data and Global-to-Local Adaptive Perception

Paper • 2410.12628 • Published Oct 16 • 27

TemporalBench: Benchmarking Fine-grained Temporal Understanding for Multimodal Video Models

Paper • 2410.10818 • Published Oct 14 • 14

MMIE: Massive Multimodal Interleaved Comprehension Benchmark for Large Vision-Language Models

Paper • 2410.10139 • Published Oct 14 • 50

Toward General Instruction-Following Alignment for Retrieval-Augmented Generation

Paper • 2410.09584 • Published Oct 12 • 45

Reacted to QizhiPei's post with 👍 about 1 month ago

Post

🚀⭐️Introducing our new survey "Leveraging Biomolecule and Natural Language through Multi-Modal Learning: A Survey"

arxiv: https://arxiv.org/abs/2403.01528
github: https://github.com/QizhiPei/Awesome-Biomolecule-Language-Cross-Modeling

The integration of biomolecular modeling with natural language (BL) has emerged as a promising interdisciplinary area at the intersection of artificial intelligence, chemistry and biology. This approach leverages the rich, multifaceted descriptions of biomolecules contained within textual data sources to enhance our fundamental understanding and enable downstream computational tasks such as biomolecule property prediction. The fusion of the nuanced narratives expressed through natural language with the structural and functional specifics of biomolecules described via various molecular modeling techniques opens new avenues for comprehensively representing and analyzing biomolecules. By incorporating the contextual language data that surrounds biomolecules into their modeling, BL aims to capture a holistic view encompassing both the symbolic qualities conveyed through language as well as quantitative structural characteristics. In this review, we provide an extensive analysis of recent advancements achieved through cross modeling of biomolecules and natural language.