Semantic-Aware Adaptive Visual Memory for Streaming Video Understanding

TL;DR

SAVEMem is a training-free, semantic-aware memory framework for streaming video understanding that dynamically manages memory and retrieval scope to improve performance and efficiency.

Contribution

It introduces a dual-stage, semantic-aware memory system that adapts retrieval scope based on query relevance without requiring additional training.

Findings

Improves OVO-Bench score from 52.27 to 62.69

Reduces peak GPU memory by 48% at 128 frames

Achieves consistent gains on multiple benchmarks.

Abstract

Online streaming video understanding requires models to process continuous visual inputs and respond to user queries in real time, where the unbounded stream and unpredictable query timing turn memory management into a central challenge. Existing methods typically compress visual tokens via visual similarity heuristics, or augment compression with KV-cache-level retrieval. However, compression decisions rarely incorporate semantic signals, and retrieval is often added after compression is finalized, making the two stages hard to coordinate. We present SAVEMem, a training-free dual-stage framework that brings semantic awareness into memory generation and lets the retrieval scope adapt per query. In Stage~1, SAVEMem builds a three-tier streaming memory online under a constant memory budget. A fixed pseudo-question bank provides a lightweight semantic prior, so that long-term retention is shaped by semantic salience rather than visual similarity alone. In Stage~2, SAVEMem performs query-aware retrieval over this memory. An anchor-conditioned recency gate adapts the retrieval scope from short-term to mid- and long-term memory based on whether the query targets the present or the distant past. Within this scope, late interaction between query and memory tokens selects candidate frames for answering. Applied to Qwen2.5-VL without training, SAVEMem improves the OVO-Bench overall score from 52.27 to 62.69 and yields consistent gains on StreamingBench and ODV-Bench, while reducing peak GPU memory by 48\% at 128 frames over the backbone.