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Discarding Non-Maximum Suppression (NMS) post-processing and realizing fully end-to-end object detection is a recent research focus. Previous works have proved that the one-to-one label assignment strategy provides the chance to eliminate NMS during inference. However, this strategy might also result in multiple predictions with high scores due to the inconsistency of label assignment during training. Thus, how to adaptively identify only one positive sample as a final prediction for each Ground-Truth instance remains important. In this paper, we propose an Enhanced Positive Sample Filter (EPSF) to filter out the single positive sample for each Ground-Truth instance and lower the confidence of other negative samples. This is mainly achieved with two components: a Dual-stream Feature Enhancement module (DsFE) and a Disentangled Max Pooling Filter (DeMF). DsFE makes full use of representations trained with different targets so as to provide rich information clues for positive sample selection, while DeMF enhances the feature discriminability in potential foreground regions with disentangled pooling. With the proposed methods, our end-to-end detector achieves a better performances against existing NMS-free object detectors on COCO, PASCAL VOC, CrowdHuman and Caltech datasets. [ABSTRACT FROM AUTHOR]

Discarding Non-Maximum Suppression (NMS) post-processing and realizing fully end-to-end object detection is a recent research focus. Previous works have proved that the one-to-one label assignment strategy provides the chance to eliminate NMS during inference. However, this strategy might also result in multiple predictions with high scores due to the inconsistency of label assignment during training. Thus, how to adaptively identify only one positive sample as a final prediction for each Ground-Truth instance remains important. In this paper, we propose an Enhanced Positive Sample Filter (EPSF) to filter out the single positive sample for each Ground-Truth instance and lower the confidence of other negative samples. This is mainly achieved with two components: a Dual-stream Feature Enhancement module (DsFE) and a Disentangled Max Pooling Filter (DeMF). DsFE makes full use of representations trained with different targets so as to provide rich information clues for positive sample selection, while DeMF enhances the feature discriminability in potential foreground regions with disentangled pooling. With the proposed methods, our end-to-end detector achieves a better performances against existing NMS-free object detectors on COCO, PASCAL VOC, CrowdHuman and Caltech datasets.