arXiv Computation and Language
Aug 31

MemoryCard: Topic-Aware Multi-Modal Clue Compression for Long-Video Question Answering

MemoryCard is a video-memory-based augmentation framework designed to improve long-video question answering for Vision‑Language Models. It segments lengthy videos into semantically coherent units—each representing a distinct topic or event—by performing a self‑reading process over the video and aligned utterances. For each unit, the framework generates an event‑level video gist and selects representative visual moments, which are compiled into unified Memory Cards that are used for retrieval and answering questions, yielding up to a 21.8% relative accuracy improvement under comparable visual‑token budgets.

By Qing Yang, Pengcheng Huang, Xinze Li, Zhenghao Liu, Yukun Yan, Yu Gu, Ge Yu, Gang Li, Maosong Sun
arXiv Computer Vision
Sep 4

StreamTTT: Reconciling Real-Time Perception and Long-Term Memory in Streaming VLMs

StreamTTT is a streaming vision-language model that balances real-time perception with long-term memory by writing long-range history into fast weights outside the attention context, while keeping a short sliding key-value cache for recent evidence. The model is trained on both offline long-video QA and a new real-time QA corpus, and it outperforms SimpleStream-4B on OVO-Bench by 1.4 points in real-time perception and 3.7 points in backward tracing. StreamTTT-4B also competes with the larger SimpleStream-8B on the StreamingBench Real-Time Visual Understanding subset.

By Joya Chen, Zeyun Zhong, Mike Zheng Shou