Pith. sign in

REVIEW 2 cited by

Hierarchical Memory for Long Video QA

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2407.00603 v2 pith:OJYTNIZ7 submitted 2024-06-30 cs.CV

Yiqin Wang , Haoji Zhang , Yansong Tang , Yong Liu , Jiashi Feng , Jifeng Dai , Xiaojie Jin This is my paper · ORCID
classification cs.CV
keywords longmemoryvideochallengeflash-vstreamhierarchicalprocessingquestion-answering
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

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 .

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Flash-VStream: Efficient Real-Time Understanding for Long Video Streams

    cs.CV 2025-06 conditional novelty 6.0 of 10

    Flash-VStream uses a two-memory design (clustered low-res summaries plus high-res key-frame details) to answer questions about long videos with state-of-the-art benchmark scores and sub-second latency.

  2. Stepping Out of Similar Semantic Space for Open-Vocabulary Segmentation

    cs.CV 2025-06 conditional novelty 6.0 of 10

    OpenBench, a new benchmark with categories semantically far from the COCO training space, shows that fine-tuning CLIP hurts open-vocabulary segmentation, and the proposed OVSNet method achieves state-of-the-art on bot...

Pith tools