EgoExoMem is the first benchmark for cross-view memory reasoning on synchronized egocentric-exocentric videos, where E2-Select raises MLLM accuracy from 55.3% to 58.2% over baselines.
EPFL-Smart- Kitchen: An ego-exo multi-modal dataset for challenging action and motion understanding in video-language models
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SP-CoR is a multimodal LLM framework using dynamics-aware sampling, spectral-physics view fusion, and prompt distillation that outperforms baselines on the new CoopSR benchmark and EgoTeam dataset for multi-robot cooperative spatial reasoning.
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EgoExoMem: Cross-View Memory Reasoning over Synchronized Egocentric and Exocentric Videos
EgoExoMem is the first benchmark for cross-view memory reasoning on synchronized egocentric-exocentric videos, where E2-Select raises MLLM accuracy from 55.3% to 58.2% over baselines.
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Seeing Together: Multi-Robot Cooperative Egocentric Spatial Reasoning with Multimodal Large Language Models
SP-CoR is a multimodal LLM framework using dynamics-aware sampling, spectral-physics view fusion, and prompt distillation that outperforms baselines on the new CoopSR benchmark and EgoTeam dataset for multi-robot cooperative spatial reasoning.