[R][P] SOLOv2 for Object Segmentation and Detection, Realtime, Matrix NMS, Better Results[reddit]https://arxiv.org/abs/2003.10152/r/MachineLearningThanks to its simplicity (being proposal free, anchor free, FCN-like), strong performance in both accuracy and speed, and potentially being capable of solve many instance-level tasks, we hope that SOLOv2 can be a strong baseline approach to instance recognition, and inspires future work such that its full potential can be exploited as we believe that there is still much room for improvement.
Abstract In this work, we aim at building a simple, direct, and fast instance segmentation framework with strong performance. We follow the principle of the SOLO method of Wang et al. “SOLO: segmenting objects by locations” . Importantly, we take one step further by dynamically learning the mask head of the object segmenter such that the mask head is conditioned on the location. Specifically, the mask branch is decoupled into a mask kernel branch and mask feature branch, which are responsible for learning the convolution kernel and the convolved features respectively. Moreover, we propose Matrix NMS (non maximum suppression) to significantly reduce the inference time overhead due to NMS of masks. Our Matrix NMS performs NMS with parallel matrix operations in one shot, and yields better results. We demonstrate a simple direct instance segmentation system, outperforminga few state-of-the-art methods in both speed and accuracy. A light-weight version of SOLOv2 executes at 31.3 FPS and yields 37.1% AP. Moreover, our state-of-the-art results in object detection (from our mask byproduct) and panoptic segmentation show the potential to serve as a new strong baseline for many instancelevel recognition tasks besides instance segmentation. Code is available at: git.io/AdelaiDet