  This paper describes our champion solution to the LOVEU Challenge @ CVPR'24,
Track 1 (Long Video VQA). Processing long sequences of visual tokens is
computationally expensive and memory-intensive, making long video
question-answering a challenging task. The key is to compress visual tokens
effectively, reducing memory footprint and decoding latency, while preserving
the essential information for accurate question-answering. We adopt a
hierarchical memory mechanism named STAR Memory, proposed in Flash-VStream,
that is capable of processing long videos with limited GPU memory (VRAM). We
further utilize the video and audio data of MovieChat-1K training set to
fine-tune the pretrained weight released by Flash-VStream, achieving 1st place
in the challenge. Code is available at project homepage
https://invinciblewyq.github.io/vstream-page
