FreshMem: Brain-Inspired Frequency-Space Hybrid Memory for Streaming Video Understanding
Authors: Kangcong Li, Peng Ye, Lin Zhang, Chao Wang, Huafeng Qin, Jiayuan Fan, Tao Chen
Organizations: College of Future Information Technology, Fudan University, Shanghai, China · Shanghai Artificial Intelligence Laboratory, Shanghai, China · The Chinese University of Hong Kong, Hong Kong, China · National Research Base of Intelligent Manufacturing Service, Chongqing Technology and Business University, Chongqing, China · Shanghai Innovation Institute, Shanghai, China
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, complemented 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. As a training-free solution, FreshMem outperforms several fully fine-tuned methods, offering a highly efficient paradigm for long-horizon streaming video understanding.