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20242026
most citedTuning Large Multimodal Models for Videos using Reinforcement Learning from AI Feedback

2 citations · 3 across the 12 of their papers we have counts for

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cs.CV2025

What Happens When: Learning Temporal Orders of Events in Videos

Daechul Ahn, Yura Choi, Hyeonbeom Choi +3

Video Large Multimodal Models (VLMMs) have shown impressive performance in video understanding, yet their ability to accurately capture the temporal order of multiple events remain…

cs.CV2025

DialNav: Multi-turn Dialog Navigation with a Remote Guide

Leekyeung Han, Hyunji Min, Gyeom Hwangbo +2

We introduce DialNav, a novel collaborative embodied dialog task, where a navigation agent (Navigator) and a remote guide (Guide) engage in multi-turn dialog to reach a goal locati…

cs.CV2025

ISAC: Training-Free Instance-to-Semantic Attention Control for Multi-Instance Generation

Sanghyun Jo, Wooyeol Lee, Ziseok Lee +3

Recent open-weight text-to-image (T2I) diffusion models still struggle with multi-instance prompts, often omitting or merging instances and mixing semantics among similar objects.…

cs.CV2024

ISR-DPO: Aligning Large Multimodal Models for Videos by Iterative Self-Retrospective DPO

Daechul Ahn, Yura Choi, San Kim +3

Iterative self-improvement, a concept extending beyond personal growth, has found powerful applications in machine learning, particularly in transforming weak models into strong on…

cs.CV2024★ 2 cited

Tuning Large Multimodal Models for Videos using Reinforcement Learning from AI Feedback

Daechul Ahn, Yura Choi, Youngjae Yu +2

Recent advancements in large language models have influenced the development of video large multimodal models (VLMMs). The previous approaches for VLMMs involved Supervised Fine-Tu…