SAM2MOT: A Novel Paradigm of Multi-Object Tracking by Segmentation

Segment Anything 2 (SAM2) enables robust single-object tracking using segmentation. To extend this to multi-object tracking (MOT), we propose SAM2MOT, introducing a novel Tracking by Segmentation paradigm. Unlike Tracking by Detection or Tracking by Query, SAM2MOT directly generates tracking boxes from segmentation masks, reducing reliance on detection accuracy. SAM2MOT has two key advantages: zero-shot generalization, allowing it to work across datasets without fine-tuning, and strong object association, inherited from SAM2. To further improve performance, we integrate a trajectory manager system for precise object addition and removal, and a cross-object interaction module to handle occlusions. Experiments on DanceTrack, UAVDT, and BDD100K show state-of-the-art results. Notably, SAM2MOT outperforms existing methods on DanceTrack by +2.1 HOTA and +4.5 IDF1, highlighting its effectiveness in MOT.

SARA: Singular-Value Based Adaptive Low-Rank Adaption

With the increasing number of parameters in large pre-trained models, LoRA as a parameter-efficient fine-tuning(PEFT) method is widely used for not adding inference overhead. The LoRA method assumes that weight changes during fine-tuning can be approximated by low-rank matrices. However, the rank values need to be manually verified to match different downstream tasks, and they cannot accommodate the varying importance of different layers in the model. In this work, we first analyze the relationship between the performance of different layers and their ranks using SVD. Based on this, we design the Singular-Value Based Adaptive Low-Rank Adaption(SARA), which adaptively finds the rank during initialization by performing SVD on the pre-trained weights. Additionally, we explore the Mixture-of-SARA(Mo-SARA), which significantly reduces the number of parameters by fine-tuning only multiple parallel sets of singular values controlled by a router. Extensive experiments on various complex tasks demonstrate the simplicity and parameter efficiency of our methods. They can effectively and adaptively find the most suitable rank for each layer of each model.