FFN performs efficient test-time training on multi-hour videos by forgetting the exiting frame, anticipating the next, and adapting only when a surprise metric exceeds a dynamic threshold.
Haiji Liang and Ruize Han
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.CV 2years
2026 2representative citing papers
Introduces the TSBOW dataset and benchmark for occluded vehicle detection in traffic surveillance under diverse and extreme weather conditions.
citing papers explorer
-
Forget, Anticipate and Adapt: Test Time Training for Long Videos
FFN performs efficient test-time training on multi-hour videos by forgetting the exiting frame, anticipating the next, and adapting only when a surprise metric exceeds a dynamic threshold.
-
TSBOW -- Traffic Surveillance Benchmark for Occluded Vehicles Under Various Weather Conditions
Introduces the TSBOW dataset and benchmark for occluded vehicle detection in traffic surveillance under diverse and extreme weather conditions.