refinement module, which sharpens the model’s localization accuracy by focusing on critical boundary details. Addressing the scarcity of datasets tailored, for instance, segmentation in real-world foggy scenes,
了解决这些问题,我们提出了一个概念上简单但有效的后处理细化框架,该方法能基于任何实例分割模型的结果来提高边界质量,称为BPR。根据看的近边缘分割效果好的想法,我们沿着预测实例的边界提取和细化一系列小的边界块(patch)。我们通过一个更高分辨率的边界补丁细化网络来完成边缘的重新细化。在Cityscapes 数据集上,所提出...
Methods and apparatuses of boundary refinement for instance segmentation are disclosed. The methods for instance segmentation comprise receiving an image and an instance mask identifying an instance in the image; extracting a set of image patches from the image based on a boundary of the instance ...
首先来回顾一下 instance level segmentation 都有哪些方法: 1)Proposal based: 基于候选区域提取的方法,首先提取物体的候选区域,然后再对候选区域进行细化分割 refinement 2)Deep structured models: CNN+ conditional random field (CRF) 3)Template matching: CNN+ template matching scheme 4) Recurrent Networks: C...
这样做的好处在于,使boundary两边的pixel能大程度的区分(opposite direction)。同样这也带来了risk:很大程度依赖segmentation source(见 5 Exp Eval: Influence of semantic segementation)2. WTN部分拿到DN的输出,watershed网络就可以开始注水了:输出一个K=16的one-hot vector用于不同程度的分割(e.g., K=1用于细小...
Paper tables with annotated results for Look Closer to Segment Better: Boundary Patch Refinement for Instance Segmentation
Li et al. [17] designed a new crack detection network called Dense Boundary Refinement Network (DBR-Net), achieving real-time crack detection with 37.0 images per second (IPS). Jun et al. [18] proposed a lightweight crack segmentation model based on a bilateral segmentation network, capable...
PBR is a conceptually simple yet effective post-processing refinement framework to improve the boundary quality of instance segmentation. Following the idea of looking closer to segment boundaries better, BPR extracts and refines a series of small boundary patches along the predicted instance boundaries...
We consider the problem of amodal instance segmentation, the objective of which is to predict the region encompassing both visible and occluded parts of each object. Thus far, the lack of publicly available amodal segmentation annotations has stymied the
Instance segmentation usually requires mask annotation, which has a much higher labeling cost than bounding box annotation. In recent years, weakly supervised instance segmentation (WSIS) has drawn great attention with only bounding box annotation needed during training. However, bounding box annotation ...