FastBench: Can Streaming VLMs Perceive High-Dynamic Real-World Streams?
Streaming Video Large Language Models (VLMs) enable continuous video understanding, yet existing benchmarks focus on low-dynamic scenarios. Under bounded context budgets, models must balance temporal history, spatial resolution, and temporal granularity; sparse sampling at 1--2 FPS misses fast events. We introduce FastBench to evaluate high-dynamic perception in real-world vide
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Streaming Video Large Language Models (VLMs) enable continuous video understanding, yet existing benchmarks focus on low-dynamic scenarios. Under bounded context budgets, models must balance temporal history, spatial resolution, and temporal granularity; sparse sampling at 1--2 FPS misses fast events. We introduce FastBench to evaluate high-dynamic perception in real-world vide
This paper introduces a critical benchmark for Streaming Video Large Language Models (VLMs), highlighting limitations in existing evaluations for high-dynamic real-world scenarios. For ML engineers, understanding these limitations and the proposed evaluation methodology is crucial for developing robust VLM applications that can handle fast-paced visual information, moving beyond static or low-dynamic video analysis.