Trends · 研究趋势
数据来自 Hugging Face 论文的 AI 提取关键词,按月统计研究方向的增长与热度。
Cong Wei, Zheyang Xiong, Weiming Ren +3 authors
An omnipotent image editor trained on multisource supervision, importance sampling, and a versatile architecture excels in multiple tasks with diverse aspect ratios and high data quality.
Sijie Cheng, Kechen Fang, Yangyang Yu +6 authors
VidEgoThink, a comprehensive benchmark for evaluating egocentric video understanding capabilities, highlights the need for advancements in Multi-modal Large Language Models for effective application in Embodied AI.
Seungone Kim, Juyoung Suk, Xiang Yue +7 authors
AgoraBench evaluates language models' data generation abilities, revealing their distinct strengths and demonstrating various factors that impact effectiveness.
Hyungjoo Chae, Namyoung Kim, Kai Tzu-iunn Ong +6 authors
A World-model-augmented web agent improves decision-making by simulating action outcomes, overcoming training challenges with free-form natural language descriptions of state differences.
Dongzhi Jiang, Renrui Zhang, Ziyu Guo +10 authors
MMSearch-Engine enables multimodal search capabilities in Large Multimodal Models (LMMs) using a comprehensive benchmark and demonstrates improved performance over commercial products.
Zehan Qi, Xiao Liu, Iat Long Iong +10 authors
WebRL enhances open LLMs as web agents through a self-evolving curriculum, robust outcome-supervised reward model, and adaptive reinforcement learning strategies, achieving high success rates compared to proprietary models.
Yixing Jiang, Jeremy Irvin, Ji Hun Wang +3 authors
Multimodal foundation models exhibit significant performance improvements with many-shot in-context learning compared to few-shot, with Gemini 1.5 Pro showing higher data efficiency and better batch processing capabilities across various domains.
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