  This paper presents VideoStreaming, an advanced vision-language large model
(VLLM) for video understanding, that capably understands arbitrary-length video
with a constant number of video tokens streamingly encoded and adaptively
selected. The challenge of video understanding in the vision language area
mainly lies in the significant computational burden caused by the great number
of tokens extracted from long videos. Previous works rely on sparse sampling or
frame compression to reduce tokens. However, such approaches either disregard
temporal information in a long time span or sacrifice spatial details,
resulting in flawed compression. To address these limitations, our
VideoStreaming has two core designs: Memory-Propagated Streaming Encoding and
Adaptive Memory Selection. The Memory-Propagated Streaming Encoding
architecture segments long videos into short clips and sequentially encodes
each clip with a propagated memory. In each iteration, we utilize the encoded
results of the preceding clip as historical memory, which is integrated with
the current clip to distill a condensed representation that encapsulates the
video content up to the current timestamp. After the encoding process, the
Adaptive Memory Selection strategy selects a constant number of
question-related memories from all the historical memories and feeds them into
the LLM to generate informative responses. The question-related selection
reduces redundancy within the memories, enabling efficient and precise video
understanding. Meanwhile, the disentangled video extraction and reasoning
design allows the LLM to answer different questions about a video by directly
selecting corresponding memories, without the need to encode the whole video
for each question. Our model achieves superior performance and higher
efficiency on long video benchmarks, showcasing precise temporal comprehension
for detailed question answering.
