safety security
Event Streams and Frames Shared One Identity Memory
Paths joins spatial-temporal modeling with global and local RGB-event fusion for person re-identification.
Summary
Paths joins spatial-temporal modeling with global and local RGB-event fusion for person re-identification.
A memory-augmented backbone and prompt-aware transformer improved results on EvReID, MARS and iLIDS-VID. Benchmark gains do not resolve the privacy or governance risks of cross-camera identification.
Why it matters
Paths joins spatial-temporal modeling with global and local RGB-event fusion for person re-identification.
Limits and context
- Benchmark gains do not resolve the privacy or governance risks of cross-camera identification.
Key claims
Paths joins spatial-temporal modeling with global and local RGB-event fusion for person re-identification.
Qualification: Benchmark gains do not resolve the privacy or governance risks of cross-camera identification.
Evidence: source-2026-08-14-021
Sources
- arXiv preprint 2608.13092arXiv · primary research
Corrections
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