DQFormer: Transformer with Decoupled Query Augmentations for End-to-End Multi-Object Tracking
Yuanzhou Huang, Songwei Pei, Rui Zeng · ACM Transactions on Multimedia Computing Communications and Applications · 2025
Recent online Transformer-based multi-object tracking methods achieve end-to-end optimization by jointly performing detection and association. However, these trackers apply query augmentations uniformly to detect queries and track queries during training, which may limit their ability to fully exploit distinct features. The different augmentations serve distinct purposes and may have conflicting effects on detection and association. Moreover, the detect queries and the track queries with augmentations contain different semantic information. Jointly feeding these queries into self-attention modules for feature interaction may lead to suppression between the two types of queries. In this article, we present a novel Transformer-based end-to-end model, DQFormer, which mitigates conflicts and effectively learns task-specific features through the proposed Decoupled Query Augmentation (DQA) strategy. DQA categorizes query augmentations into separate detection and association branches, generating more discriminative queries tailored specifically for detection and association. To align with DQA, we decompose the self-attention module into a Dual Cross-Attention module. Furthermore, a Targeted Label Assignment strategy is applied to the augmented queries in each cross-attention module, helping the model to learn distinct features for tracking. DQFormer achieves competitive detection and association results across the MOT17, MOT20, and DanceTrack datasets.