TRACE improves multi-video event understanding by grounding evidence in structured timelines before visual reasoning, raising MiRAGE F1 from 0.705 to 0.811 on MAGMaR 2026.
Kate Sanders, David Etter, Reno Kriz, and Benjamin Van Durme
3 Pith papers cite this work. Polarity classification is still indexing.
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IRR-Drive adds an adaptive multimodal reflection step (text intention plus predicted future BEV) that lets a VLA model self-correct its trajectory plan according to scene complexity and reports SOTA on NAVSIM.
This review organizes literature on large multimodal models and object-centric vision into four themes—understanding, referring segmentation, editing, and generation—while summarizing paradigms, strategies, and challenges like instance permanence and consistent interaction.
citing papers explorer
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TRACE: Evidence Grounding-Guided Multi-Video Event Understanding and Claim Generation
TRACE improves multi-video event understanding by grounding evidence in structured timelines before visual reasoning, raising MiRAGE F1 from 0.705 to 0.811 on MAGMaR 2026.
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Intend, Reflect, Refine: An Adaptive Multimodal Reflection Framework for Autonomous Driving
IRR-Drive adds an adaptive multimodal reflection step (text intention plus predicted future BEV) that lets a VLA model self-correct its trajectory plan according to scene complexity and reports SOTA on NAVSIM.
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LMMs Meet Object-Centric Vision: Understanding, Segmentation, Editing and Generation
This review organizes literature on large multimodal models and object-centric vision into four themes—understanding, referring segmentation, editing, and generation—while summarizing paradigms, strategies, and challenges like instance permanence and consistent interaction.