FreshMem: Brain-Inspired Frequency-Space Hybrid Memory for Streaming Video Understanding

Transitioning Multimodal Large Language Models (MLLMs) from offline to online streaming video understanding is essential for continuous perception. However, existing methods lack flexible adaptivity, leading to irreversible detail loss and context fragmentation. To resolve this, we propose FreshMem, a Frequency-Space Hybrid Memory network inspired by the brain's logarithmic perception and memory consolidation. FreshMem reconciles short-term fidelity with long-term coherence through two synergistic modules: Multi-scale Frequency Memory (MFM), which projects overflowing frames into representative frequency coefficients, complementing by residual details to reconstruct a global historical “gist”; and Space Thumbnail Memory (STM), which discretizes the continuous stream into episodic clusters by employing an adaptive compression strategy to distill them into high-density space thumbnails. Extensive experiments show that FreshMem significantly boosts the Qwen2-VL baseline, yielding gains of 5.20\%, 4.52\%, and 2.34\% on StreamingBench, OV-Bench, and OVO-Bench, respectively. Besides its plug-and-play design, following low-cost component-wise fine-tuning, FreshMem outperforms current fully fine-tuned methods and achieves a state-of-the-art performance of 82.31\% on StreamingBench, exceeding the baseline by 13.31\%, offering a highly efficient paradigm for long-horizon streaming video understanding.