搜索论文: YOLACT++ Better Real-Time Instance Segmentation
搜索论文: http://www.studyai.com/search/whole-site/?q=YOLACT+++Better+Real-Time+Instance+Segmentation
Prototypes; Real-time systems; Image segmentation; Object detection; Detectors; Task analysis; Shape; Instance segmentation; real time
机器视觉
检测分割
We present a simple, fully-convolutional model for real-time ( > 30 >30 >30>30 fps) instance segmentation that achieves competitive results on MS COCO evaluated on a single Titan Xp, which is significantly faster than any previous state-of-the-art approach.
我们为实时( > 30 >30 >30>30 fps)实例分割提供了一个简单的、完全卷积的模型,该模型在MS COCO上实现了在单个Titan Xp上评估的竞争结果,比以前任何最先进的方法都要快得多。.
Moreover, we obtain this result after training on only one GPU.
此外,我们只在一个GPU上进行训练就得到了这个结果。.
We accomplish this by breaking instance segmentation into two parallel subtasks: (1) generating a set of prototype masks and (2) predicting per-instance mask coefficients.
我们通过将实例分割分成两个并行子任务来实现这一点:(1)生成一组原型掩码;(2)预测每个实例的掩码系数。.
Then we produce instance masks by linearly combining the prototypes with the mask coefficients.
然后,我们通过将原型与掩码系数线性组合来生成实例掩码。.
We find that because this process doesn’t depend on repooling, this approach produces very high-quality masks and exhibits temporal stability for free.
我们发现,因为这个过程不依赖于重新冷却,所以这种方法可以产生非常高质量的掩模,并且免费展示时间稳定性。.
Furthermore, we analyze the emergent behavior of our prototypes and show they learn to localize instances on their own in a translation variant manner, despite being fully-convolutional.
此外,我们还分析了原型的紧急行为,并表明它们能够以翻译变体的方式自行对实例进行本地化,尽管它们是完全卷积的。.
We also propose Fast NMS, a drop-in 12 ms faster replacement for standard NMS that only has a marginal performance penalty.
我们还提出了快速NMS,即以12毫秒的速度下降,以替代性能损失较小的标准NMS。.
Finally, by incorporating deformable convolutions into the backbone network, optimizing the prediction head with better anchor scales and aspect ratios, and adding a novel fast mask re-scoring branch, our YOLACT++ model can achieve 34.1 mAP on MS COCO at 33.5 fps, which is fairly close to the state-of-the-art approaches while still running at real-time…
最后,通过将可变形卷积合并到主干网络中,优化具有更好锚定尺度和纵横比的预测头,并添加一个新的快速掩码重新计分分支,我们的YOLACT++模型可以在MS COCO上以33.5 fps的速度实现34.1 mAP,这相当接近最先进的方法,同时仍然实时运行。。.
[‘Daniel Bolya’, ‘Chong Zhou’, ‘Fanyi Xiao’, ‘Yong Jae Lee’]