arXiv Computer Vision
MEMO: Multi-Level Entity-Aware Memory for Streaming Video Understanding
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arXiv AI
4d ago
MemEvo: Automatic Discovery of Streaming Video Memory Mechanisms
MemEvo presents an automated approach to discover memory mechanisms for streaming video understanding. It frames memory design as a search over executable programs expressed in a lightweight domain-specific language, and uses a pretrained large language model to generate and refine candidate memory programs. The framework evaluates each candidate deterministically while keeping the underlying vision-language model frozen, ultimately producing a training-free, bounded-memory mechanism that shows strong performance on StreamingBench and OVO-Bench.
Hugging Face Trending Papers
Sep 3
Beyond Retrieval: Progressive Latent Memory Evolution for Streaming Video Understanding
The paper introduces LatentStream, a progressive latent working memory framework for streaming video understanding. It replaces the traditional store‑and‑retrieve approach with a retrieve‑and‑internalize strategy, organizing visual history into short, mid, and long‑term levels and progressively expanding memory receptive fields to internalize evidence into a compact latent memory. The method also employs confidence‑guided optimization to refine memory tokens, achieving state‑of‑the‑art results on online and offline video benchmarks.
arXiv Computer Vision
Sep 4
Beyond Retrieval: Progressive Latent Memory Evolution for Streaming Video Understanding
The paper introduces LatentStream, a progressive latent working memory framework for streaming video understanding that replaces the traditional store‑and‑retrieve paradigm with a retrieve‑and‑internalize approach. It organizes visual history into short, mid, and long‑term levels using Jenks‑guided adaptive consolidation, then expands memory receptive fields to iteratively retrieve and internalize evidence into a compact latent memory. A confidence‑guided optimization further refines this memory, leading to state‑of‑the‑art performance on online and offline video benchmarks.
arXiv AI
Jun 16
What Should a Streaming Video Model Remember?
arXiv:2606. 16353v1 Announce Type: cross Abstract: Streaming video understanding models must answer queries at any moment during an ongoing stream, using only what they have observed so far and under fixed memory and computation budgets.
arXiv Computer Vision
Aug 31
StreamEMS: Streaming Video Understanding with Self-Evolving Memory Scheme for Vision-Language Models
arXiv:2608.27881v1 Announce Type: new Abstract: Recently, many streaming video understanding methods have been proposed by constructing an external memory to store historical data for computational r...
arXiv Computer Vision
Sep 1
Dynamic Hub-and-Spoke Memory for Streaming Video Understanding
arXiv:2608.30294v1 Announce Type: new Abstract: Streaming video understanding requires answering questions at arbitrary times over a continuously growing visual stream. The central challenge is to co...