GateMOT: Q-Gated Attention for Dense Object Tracking

TL;DR

GateMOT introduces Q-Gated Attention, a novel efficient attention mechanism for dense object tracking that enables high performance on crowded scenes by reducing computational complexity.

Contribution

The paper proposes Q-Gated Attention, a new attention variant that improves dense object tracking by enabling explicit relevance selection with linear complexity.

Findings

Achieves state-of-the-art HOTA, MOTA, and IDF1 scores on BEE24.

Demonstrates strong performance on multiple dense object tracking benchmarks.

Q-Attention is effective, transferable, and computationally efficient.

Abstract

While large models demonstrate the strong representational power of vanilla attention, this core mechanism cannot be directly applied to Dense Object Tracking: its quadratic all-to-all interactions are computationally prohibitive for dense motion estimation on high-resolution features. This mismatch prevents Dense Object Tracking from fully leveraging attention-based modeling in crowded and occlusion-heavy scenes. To address this challenge, we introduce GateMOT, an online tracking framework centered on Q-Gated Attention (Q-Attention), an efficient and spatially aware attention variant. Our key idea is to repurpose the Query from a similarity-conditioning term into a learnable gating unit. This Gating-Query (Gating-Q) produces a probabilistic gate that modulates Key features in an element-wise manner, enabling explicit relevance selection instead of costly global aggregation. Built on this mechanism, parallel Q-Attention heads transform one shared feature map into task-specific yet consistent representations for detection, motion, and re-identification, yielding a tightly coupled multi-task decoder with linear-complexity gating operations. GateMOT achieves state-of-the-art HOTA of 48.4, MOTA of 67.8, and IDF1 of 64.5 on BEE24, and demonstrates strong performance on additional Dense Object Tracking benchmarks. These results show that Q-Attention is a simple, effective, and transferable building block for attention-based tracking in dense tracking scenarios.