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Matching papers

TimeLens2: Generalist Video Temporal Grounding with Multimodal LLMs

154 upvotes · 19 JUL 2026 · Yuhan Zhu, Changlian Ma, Xiangyu Zeng et al.

This paper develops a new approach to understanding videos by predicting when specific events or evidence occur within the video. Practitioners working on video analysis and AI models might care about this research because it could lead to more accurate and robust video understanding systems.

multimodal LLMsvideo temporal groundingcontinual learninginterval setsWasserstein reward

Computational Humor with Multimodal LLMs: Methods, Datasets, Evaluation, and Challenges

2 upvotes · 21 JUL 2026 · Tuo Liang, Zhe Hu, Disheng Liu et al.

This paper explores how to make AI systems understand and generate humor, particularly in visual formats like memes and comics, and why this is important for developing more human-like AI that can understand and create humor. Practitioners might care because humor is a key aspect of human communication and understanding.

multimodal LLMshumor understandingvisual humormultimodal alignmentcontinual learningAI safety