论文标题
实例实例分割的稀疏实例激活
Sparse Instance Activation for Real-Time Instance Segmentation
论文作者
论文摘要
在本文中，我们提出了一个概念上的新颖，高效且完全卷积的框架，以进行实时实例分割。以前，大多数实例分割方法在很大程度上依赖对象检测并根据边界框或密集中心执行掩盖预测。相比之下，我们提出了一个稀疏的实例激活图作为新对象表示形式，以突出每个前景对象的信息区域。然后，通过根据突出显示的识别和分割的突出显示区域汇总特征来获得实例级级特征。此外，基于双方匹配，实例激活图可以以一对一的样式预测对象，从而避免在后处理中避免非最大抑制（NMS）。由于具有实例激活图的简单而有效的设计，SparseInst具有非常快的推理速度，并且在可可基准上获得了40 fps和37.9 AP，这在速度和准确性方面大大优于同行。代码和模型可在https://github.com/hustvl/sparseinst上找到。
In this paper, we propose a conceptually novel, efficient, and fully convolutional framework for real-time instance segmentation. Previously, most instance segmentation methods heavily rely on object detection and perform mask prediction based on bounding boxes or dense centers. In contrast, we propose a sparse set of instance activation maps, as a new object representation, to highlight informative regions for each foreground object. Then instance-level features are obtained by aggregating features according to the highlighted regions for recognition and segmentation. Moreover, based on bipartite matching, the instance activation maps can predict objects in a one-to-one style, thus avoiding non-maximum suppression (NMS) in post-processing. Owing to the simple yet effective designs with instance activation maps, SparseInst has extremely fast inference speed and achieves 40 FPS and 37.9 AP on the COCO benchmark, which significantly outperforms the counterparts in terms of speed and accuracy. Code and models are available at https://github.com/hustvl/SparseInst.
