Neural Information Processing Systems http://nips.cc/

To this end, we propose a novel and effective approach, termed SOLOv2, following the principle of the SOLO method [32].

Non-maximum suppression (NMS) is an indispensable post-processing step in object detection. With the continuous optimization of network models, NMS has become the ``last mile'' to enhance the efficiency of object detection.

Duplicate removal is a critical step to accomplish a reasonable amount of predictions in prevalent proposal-based object detection frameworks.

Non-maximum suppression (NMS) is an indispensable post-processing step in object detection. With the continuous optimization of network models, NMS has become the "last mile" to enhance the efficiency of object detection.

Neural Information Processing Systems http://nips.cc/

Non-maximum suppression (NMS) is an indispensable post-processing step in object detection. With the continuous optimization of network models, NMS has become the last mile'' to enhance the efficiency of object detection. Consequently, we propose two optimization methods, namely QSI-NMS and BOE-NMS. The former is a fast recursive divide-and-conquer algorithm with negligible mAP loss, and its extended version (eQSI-NMS) achieves optimal complexity of \mathcal{O}(n\log n) . The latter, concentrating on the locality of NMS, achieves an optimization at a constant level without an mAP loss penalty. Moreover, to facilitate rapid evaluation of NMS methods for researchers, we introduce NMS-Bench, the first benchmark designed to comprehensively assess various NMS methods.

Non-maximum suppression (NMS) is an indispensable post-processing step in object detection. With the continuous optimization of network models, NMS has become the ``last mile'' to enhance the efficiency of object detection. This paper systematically analyzes NMS from a graph theory perspective for the first time, revealing its intrinsic structure. Consequently, we propose two optimization methods, namely QSI-NMS and BOE-NMS. The former is a fast recursive divide-and-conquer algorithm with negligible mAP loss, and its extended version (eQSI-NMS) achieves optimal complexity of $\mathcal{O}(n\log n)$. The latter, concentrating on the locality of NMS, achieves an optimization at a constant level without an mAP loss penalty. Moreover, to facilitate rapid evaluation of NMS methods for researchers, we introduce NMS-Bench, the first benchmark designed to comprehensively assess various NMS methods. Taking the YOLOv8-N model on MS COCO 2017 as the benchmark setup, our method QSI-NMS provides $6.2\times$ speed of original NMS on the benchmark, with a $0.1\%$ decrease in mAP. The optimal eQSI-NMS, with only a $0.3\%$ mAP decrease, achieves $10.7\times$ speed. Meanwhile, BOE-NMS exhibits $5.1\times$ speed with no compromise in mAP.

This paper proposes a new Duplicate Removal method based on RNN. Based on each candidate area, informative features are extracted by using appearance feature, position and ranking information in addition to the score. Then, they are treated as series data and are input into the RNN-based model to improve the final accuracy by capturing global information. The number of candidate regions is enormous to the number of objects that are to be left. Therefore, this paper proposes to reduce the box gradually by dividing it into two stages. In the two stages, the RNN model of the same structure was used. In stage I, to remove simple boxes the model is trained by using NMS results as a teaching signal. In stage II, to remove difficult boxes, the model is trained by using the grand-truth boxes. Experiments showed that mAP is increased in the SOTA object detection methods (FPN, Mask R - CNN, PANet with DCN) with the proposed method.