Tremendous efforts have been made on instance segmentation but the mask
quality is still not satisfactory. The boundaries of predicted instance masks
are usually imprecise due to the low spatial resolution of feature maps and the
imbalance problem caused by the extremely low proportion of boundary pixels. To
address these issues, we propose a conceptually simple yet effective
post-processing refinement framework to improve the boundary quality based on
the results of any instance segmentation model, termed BPR. Following the idea
of looking closer to segment boundaries better, we extract and refine a series
of small boundary patches along the predicted instance boundaries. The
refinement is accomplished by a boundary patch refinement network at higher
resolution. The proposed BPR framework yields significant improvements over the
Mask R-CNN baseline on Cityscapes benchmark, especially on the boundary-aware
metrics. Moreover, by applying the BPR framework to the PolyTransform + SegFix
baseline, we reached 1st place on the Cityscapes leaderboard.

提出一种基于后处理细化的框架，名为 BPR，以改善任何实例分割模型的结果，该框架通过沿预测边界提取和细化一系列小的边界补丁来提高边界质量，在 Cityscapes 基准测试中比 Mask R-CNN 基线得到显著提升，特别是在边界感知度量方面；此外，通过将 BPR 框架应用于 PolyTransform + SegFix 基线，我们在 Cityscapes 排行榜上达到第一名。

更近距离观察以进行更好的分割：用于实例分割的边界补丁细化

Look Closer to Segment Better: Boundary Patch Refinement for Instance  Segmentation

Current state-of-the-art methods for image segmentation form a dense image
representation where the color, shape and texture information are all processed
together inside a deep CNN. This however may not be ideal as they contain very
different type of information relevant for recognition. Here, we propose a new
two-stream CNN architecture for semantic segmentation that explicitly wires
shape information as a separate processing branch, i.e. shape stream, that
processes information in parallel to the classical stream. Key to this
architecture is a new type of gates that connect the intermediate layers of the
two streams. Specifically, we use the higher-level activations in the classical
stream to gate the lower-level activations in the shape stream, effectively
removing noise and helping the shape stream to only focus on processing the
relevant boundary-related information. This enables us to use a very shallow
architecture for the shape stream that operates on the image-level resolution.
Our experiments show that this leads to a highly effective architecture that
produces sharper predictions around object boundaries and significantly boosts
performance on thinner and smaller objects. Our method achieves
state-of-the-art performance on the Cityscapes benchmark, in terms of both mask
(mIoU) and boundary (F-score) quality, improving by 2% and 4% over strong
baselines.

本文提出了一种新的双流 CNN 架构，其中显式地将形状信息作为单独的处理分支，即形状流，以并行方式处理信息。此架构的关键是一种连接两个流的中间层的新型门，用于连接经典流的高级激活以控制形状流的低级激活，有效地消除噪声并帮助形状流仅关注处理相关边界信息。实验表明，该方法在 Cityscapes 基准上可以显着提高细小物体的性能，并取得了最先进的性能。

门控形状卷积神经网络用于语义分割

Gated-SCNN: Gated Shape CNNs for Semantic Segmentation

Current state-of-the-art instance segmentation methods are not suited for
real-time applications like autonomous driving, which require fast execution
times at high accuracy. Although the currently dominant proposal-based methods
have high accuracy, they are slow and generate masks at a fixed and low
resolution. Proposal-free methods, by contrast, can generate masks at high
resolution and are often faster, but fail to reach the same accuracy as the
proposal-based methods. In this work we propose a new clustering loss function
for proposal-free instance segmentation. The loss function pulls the spatial
embeddings of pixels belonging to the same instance together and jointly learns
an instance-specific clustering bandwidth, maximizing the
intersection-over-union of the resulting instance mask. When combined with a
fast architecture, the network can perform instance segmentation in real-time
while maintaining a high accuracy. We evaluate our method on the challenging
Cityscapes benchmark and achieve top results (5\% improvement over Mask R-CNN)
at more than 10 fps on 2MP images. Code will be available at
this https URL .

本文提出新的聚类损失函数，结合快速的架构，在保持高准确性的同时，能够在实时应用场景中进行实例分割，取得了 Cityscapes 基准测试的最佳结果，并在 2MP 图像上以 10 fps 以上的速度进行了测试。