Decoupled DETR: Spatially Disentangling Localization and Classification for Improved End-to-End Object Detection

TL;DR

This paper introduces SD-DETR, a novel approach that spatially decouples localization and classification in DETR, leading to significant performance improvements by addressing task misalignment.

Contribution

The paper proposes a task-aware query generation and disentangled feature learning scheme to decouple localization and classification in DETR, improving detection accuracy.

Findings

Achieves 4.5 AP improvement on MSCOCO with Conditional DETR

Effectively reduces task misalignment between classification and localization

Demonstrates significant performance gains over previous DETR variants

Abstract

The introduction of DETR represents a new paradigm for object detection. However, its decoder conducts classification and box localization using shared queries and cross-attention layers, leading to suboptimal results. We observe that different regions of interest in the visual feature map are suitable for performing query classification and box localization tasks, even for the same object. Salient regions provide vital information for classification, while the boundaries around them are more favorable for box regression. Unfortunately, such spatial misalignment between these two tasks greatly hinders DETR's training. Therefore, in this work, we focus on decoupling localization and classification tasks in DETR. To achieve this, we introduce a new design scheme called spatially decoupled DETR (SD-DETR), which includes a task-aware query generation module and a disentangled feature learning process. We elaborately design the task-aware query initialization process and divide the cross-attention block in the decoder to allow the task-aware queries to match different visual regions. Meanwhile, we also observe that the prediction misalignment problem for high classification confidence and precise localization exists, so we propose an alignment loss to further guide the spatially decoupled DETR training. Through extensive experiments, we demonstrate that our approach achieves a significant improvement in MSCOCO datasets compared to previous work. For instance, we improve the performance of Conditional DETR by 4.5 AP. By spatially disentangling the two tasks, our method overcomes the misalignment problem and greatly improves the performance of DETR for object detection.