StreamTTT: Reconciling Real-Time Perception and Long-Term Memory in Streaming VLMs
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TL;DR - StreamTTT is a streaming vision-language model that stores long-range history in online-updated fast weights while reserving a short attention cache for recent visual evidence. This improves both real-time perception and historical recall without requiring a larger model.
- Separates long-term memory from the attention context to reduce attention dilution.
- Uses a short sliding key-value cache focused on current-scene perception.
- Jointly trains on offline long-video QA and a new real-time QA corpus.
- StreamTTT-4B beats SimpleStream-4B by 1.4 points in real-time perception and 3.7 points in backward tracing on OVO-Bench.
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StreamTTT: Reconciling Real-Time Perception and Long-Term Memory in Streaming VLMs
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Hugging Face upvotes 0
TL;DR - StreamTTT is a streaming vision-language model that stores long-range history in online-updated fast weights while reserving a short attention cache for recent visual evidence. This improves both real-time perception and historical recall without requiring a larger model.
- Separates long-term memory from the attention context to reduce attention dilution.
- Uses a short sliding key-value cache focused on current-scene perception.
- Jointly trains on offline long-video QA and a new real-time QA corpus.
- StreamTTT-4B beats SimpleStream-4B by 1.4 points in real-time perception and 3.7 points in backward tracing on OVO-Bench.