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Baichuan Zhou
bczhou
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Perception Tokens Enhance Visual Reasoning in Multimodal Language Models
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Add dataset card, link to paper
#1 opened about 2 months ago
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nielsr
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2 months ago
MIA-DPO: Multi-Image Augmented Direct Preference Optimization For Large Vision-Language Models
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2410.17637
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AutoTrain: No-code training for state-of-the-art models
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2410.15735
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CompassJudger-1: All-in-one Judge Model Helps Model Evaluation and Evolution
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2410.16256
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updated
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2 months ago
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2 months ago
Harnessing Webpage UIs for Text-Rich Visual Understanding
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2410.13824
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MixEval-X: Any-to-Any Evaluations from Real-World Data Mixtures
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2410.13754
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DocLayout-YOLO: Enhancing Document Layout Analysis through Diverse Synthetic Data and Global-to-Local Adaptive Perception
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2410.12628
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TemporalBench: Benchmarking Fine-grained Temporal Understanding for Multimodal Video Models
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2410.10818
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MMIE: Massive Multimodal Interleaved Comprehension Benchmark for Large Vision-Language Models
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2410.10139
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Toward General Instruction-Following Alignment for Retrieval-Augmented Generation
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2410.09584
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2 months ago
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🚀⭐️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.
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.
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