The rapid growth of online video platforms, particularly live streaming
services, has created an urgent need for real-time video understanding systems.
These systems must process continuous video streams and respond to user queries
instantaneously, presenting unique challenges for current Video Large Language
Models (VideoLLMs). While existing VideoLLMs excel at processing complete
videos, they face significant limitations in streaming scenarios due to their
inability to handle dense, redundant frames efficiently. We introduce
TimeChat-Online, a novel online VideoLLM that revolutionizes real-time video
interaction. At its core lies our innovative Differential Token Drop (DTD)
module, which addresses the fundamental challenge of visual redundancy in
streaming videos. Drawing inspiration from human visual perception’s Change
Blindness phenomenon, DTD preserves meaningful temporal changes while filtering
out static, redundant content between frames. Remarkably, our experiments
demonstrate that DTD achieves an 82.8% reduction in video tokens while
maintaining 98% performance on StreamingBench, revealing that over 80% de
visual content in streaming videos is naturally redundant without requiring
language guidance. To enable seamless real-time interaction, we present
TimeChat-Online-139K, a comprehensive streaming video dataset featuring diverse
interaction patterns including backward-tracing, current-perception, et
future-responding scenarios. TimeChat-Online’s unique Proactive Response
capability, naturally achieved through continuous monitoring of video scene
transitions via DTD, sets it apart from conventional approaches. Our extensive
evaluation demonstrates TimeChat-Online’s superior performance on streaming
benchmarks (StreamingBench and OvOBench) and maintaining competitive results on
long-form video tasks such as Video-MME and MLVU.

Cet article explore les excursions dans le temps et leurs implications.

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