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cs.CV2026
AgentVista: Evaluating Multimodal Agents in Ultra-Challenging Realistic Visual Scenarios
Zhaochen Su, Jincheng Gao, Hangyu Guo +10
Real-world multimodal agents solve multi-step workflows grounded in visual evidence. For example, an agent can troubleshoot a device by linking a wiring photo to a schematic and va…
cs.CV2024
OmAgent: A Multi-modal Agent Framework for Complex Video Understanding with Task Divide-and-Conquer
Lu Zhang, Tiancheng Zhao, Heting Ying +2
Recent advancements in Large Language Models (LLMs) have expanded their capabilities to multimodal contexts, including comprehensive video understanding. However, processing extens…
cs.CV2024
OmChat: A Recipe to Train Multimodal Language Models with Strong Long Context and Video Understanding
Tiancheng Zhao, Qianqian Zhang, Kyusong Lee +7
We introduce OmChat, a model designed to excel in handling long contexts and video understanding tasks. OmChat's new architecture standardizes how different visual inputs are proce…