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This paper investigates a phenomenon where query-based object detectorsmispredict at the last decoding stage while predicting correctly at anintermediate stage. We review the training process and attribute the overlookedphenomenon to two limitations: lack of training emphasis and cascading errorsfrom decoding sequence. We design and present Selective Query Recollection(SQR), a simple and effective training strategy for query-based objectdetectors. It cumulatively collects intermediate queries as decoding stages godeeper and selectively forwards the queries to the downstream stages aside fromthe sequential structure. Such-wise, SQR places training emphasis on laterstages and allows later stages to work with intermediate queries from earlierstages directly. SQR can be easily plugged into various query-based objectdetectors and significantly enhances their performance while leaving theinference 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.

Enhanced Training of Query-Based Object Detection via Selective Query Recollection