MFT: Memory-Aware Fine-Tuning of SAM2 for Efficient Long-Sequence Video Object Segmentation

Adapting large foundation segmentation models such as SAM2 to video object segmentation (VOS), especially for long sequences, is often limited by the high training cost of full fine-tuning. We present Memory-Aware Fine-tuning (MFT), a parameter-efficient adaptation strategy that freezes the image encoder and selectively updates only memory-related modules responsible for temporal reasoning (memory attention, mask decoder, and memory encoder). To improve robustness on extended sequences, we further introduce a lightweight Memory Compression (MC) network that periodically condenses short-term memory embeddings into a compact long-term representation, keeping the memory bank bounded while preserving historical context. Extensive experiments demonstrate that MFT achieves state-of-the-art results on long-sequence benchmarks while maintaining efficient resource utilization, offering an accessible approach for fine-tuning SAM2 on resource-constrained hardware.