Decoupled DETR：为改进端到端目标检测而空间解耦定位与分类
Decoupled DETR: Spatially Disentangling Localization and Classification for Improved End-to-End Object Detection
AI总结：
针对DETR中分类与定位任务空间不对齐阻碍训练的问题，提出空间解耦的SD-DETR方案，通过任务感知查询生成、解耦特征学习及对齐损失，显著提升了目标检测性能。
摘要：
DETR的引入代表了目标检测的一种新范式。然而，其解码器使用共享的查询和交叉注意力层来进行分类和边界框定位，导致结果次优。我们观察到，即使在同一个对象上，视觉特征图中不同的感兴趣区域也分别适合执行查询分类和边界框定位任务。显著区域为分类提供关键信息，而其周围的边界则更有利于边界框回归。遗憾的是，这两项任务之间的这种空间不对齐极大地阻碍了DETR的训练。因此，在本研究中，我们专注于解耦DETR中的定位和分类任务。为此，我们引入了一种名为空间解耦DETR（SD-DETR）的新设计方案，其中包含一个任务感知查询生成模块和一个解耦特征学习过程。我们精心设计了任务感知查询初始化过程，并拆分了解码器中的交叉注意力块，以允许任务感知查询匹配不同的视觉区域。同时，我们还观察到存在高分类置信度与精确定位之间的预测不对齐问题，因此我们提出了一种对齐损失，以进一步指导空间解耦DETR的训练。通过大量实验，我们证明了与之前的工作相比，我们的方法在MSCOCO数据集上取得了显著提升。例如，我们将Conditional DETR的性能提升了4.5 AP。通过空间解耦这两项任务，我们的方法克服了不对齐问题，并大幅提升了DETR在目标检测中的性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
