论文标题
通过选择性查询回忆增强基于查询的对象检测的训练
Enhanced Training of Query-Based Object Detection via Selective Query Recollection
论文作者
论文摘要
本文研究了一种现象，其中基于查询的对象检测到最后一个解码阶段错误预测的同时在中间阶段正确预测。我们回顾了训练过程，并将被忽视的现象归因于两个局限性：缺乏训练重点和解码顺序的级联错误。我们设计并目前选择性查询回忆（SQR），这是一种基于查询的对象探测器的简单有效的培训策略。它累积收集中间查询，因为解码阶段更深，并选择性地将查询转发到顺序结构之外的下游阶段。从这样的角度来看，SQR将训练重点放在后期阶段，并允许以后的阶段直接从早期阶段进行中间查询。 SQR可以轻松地插入各种基于查询的对象检测器中，并在使推理管道保持不变的同时显着提高其性能。结果，我们将SQR应用于Adamixer，DAB-DET和可变形 - 跨各种设置（骨干，查询数，时间表），并始终带来1.4-2.8 AP改进。
This paper investigates a phenomenon where query-based object detectors mispredict at the last decoding stage while predicting correctly at an intermediate stage. We review the training process and attribute the overlooked phenomenon to two limitations: lack of training emphasis and cascading errors from decoding sequence. We design and present Selective Query Recollection (SQR), a simple and effective training strategy for query-based object detectors. It cumulatively collects intermediate queries as decoding stages go deeper and selectively forwards the queries to the downstream stages aside from the sequential structure. Such-wise, SQR places training emphasis on later stages and allows later stages to work with intermediate queries from earlier stages directly. SQR can be easily plugged into various query-based object detectors and significantly enhances their performance while leaving the inference pipeline unchanged. As a result, we apply SQR on Adamixer, DAB-DETR, and Deformable-DETR across various settings (backbone, number of queries, schedule) and consistently brings 1.4-2.8 AP improvement.
