arXiv Computer Vision
SpatialMem: Metric-Aligned Long-Horizon Video Memory for Language Grounding and QA
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R4DSG: Relative 4D Scene Graph Memory for Object-Centric Question Answering in Long Egocentric Video
arXiv:2608. 11017v1 Announce Type: cross Abstract: Long-horizon egocentric video is a rich substrate for wearable AI assistants, but object-centric questions such as where an item was moved, when it last changed state, or why it was relocated remain difficult because caption- and transcript-based memories rarely preserve persistent object identity or structured spatial change.
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R4DSG: Relative 4D Scene Graph Memory for Object-Centric Question Answering in Long Egocentric Video
Long-horizon egocentric video is a rich substrate for wearable AI assistants, but object-centric questions such as where an item was moved, when it last changed state, or why it was relocated remain difficult because caption- and transcript-based memories rarely preserve persistent object identity or structured spatial change. Existing long-video QA methods mainly emphasize temporal grounding and clip retrieval, while prior 3D scene-graph methods typically assume stronger geometry than free-motion wearable RGB video provides, including point clouds, RGB-D input, posed views, sparse reconstruction, or reconstructed scenes.
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S3Mem: Structured Spatiotemporal Scene-Event Memory for Long-Horizon Interactive Question Answering
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Linguistic Trajectory Encoding for Efficient Long-Horizon Spatial Memory in Embodied Agents
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