Semantic segmentation is a key prerequisite to robust image understanding for
applications in \acrlong{ai} and Robotics. \acrlong{fss}, in particular,
concerns the extension and optimization of traditional segmentation methods in
challenging conditions where limited training examples are available. A
predominant approach in \acrlong{fss} is to rely on a single backbone for
visual feature extraction. Choosing which backbone to leverage is a deciding
factor contributing to the overall performance. In this work, we interrogate on
whether fusing features from different backbones can improve the ability of
\acrlong{fss} models to capture richer visual features. To tackle this
question, we propose and compare two ensembling techniques-Independent Voting
and Feature Fusion. Among the available \acrlong{fss} methods, we implement the
proposed ensembling techniques on PANet. The module dedicated to predicting
segmentation masks from the backbone embeddings in PANet avoids trainable
parameters, creating a controlled `in vitro' setting for isolating the impact
of different ensembling strategies. Leveraging the complementary strengths of
different backbones, our approach outperforms the original single-backbone
PANet across standard benchmarks even in challenging one-shot learning
scenarios. Specifically, it achieved a performance improvement of +7.37\% on
PASCAL-5\textsuperscript{i} and of +10.68\% on COCO-20\textsuperscript{i} in
the top-performing scenario where three backbones are combined. These results,
together with the qualitative inspection of the predicted subject masks,
suggest that relying on multiple backbones in PANet leads to a more
comprehensive feature representation, thus expediting the successful
application of \acrlong{fss} methods in challenging, data-scarce environments.

利用多个骨干网络在 PANet 中的特征融合可以提高在挑战性、数据稀缺环境中对全景分割方法的性能。

超越局部：集成骨干网络进行少样本分割

More than the Sum of Its Parts: Ensembling Backbone Networks for  Few-Shot Segmentation

Reliable LiDAR panoptic segmentation (LPS), including both semantic and
instance segmentation, is vital for many robotic applications, such as
autonomous driving. This work proposes a new LPS framework named PANet to
eliminate the dependency on the offset branch and improve the performance on
large objects, which are always over-segmented by clustering algorithms.
Firstly, we propose a non-learning Sparse Instance Proposal (SIP) module with
the ``sampling-shifting-grouping" scheme to directly group thing points into
instances from the raw point cloud efficiently. More specifically, balanced
point sampling is introduced to generate sparse seed points with more uniform
point distribution over the distance range. And a shift module, termed bubble
shifting, is proposed to shrink the seed points to the clustered centers. Then
we utilize the connected component label algorithm to generate instance
proposals. Furthermore, an instance aggregation module is devised to integrate
potentially fragmented instances, improving the performance of the SIP module
on large objects. Extensive experiments show that PANet achieves
state-of-the-art performance among published works on the SemanticKITII
validation and nuScenes validation for the panoptic segmentation task.

本篇论文提出了名为 PANet 的新型 LiDAR 宽景分割 (LPS) 框架，采用便于生成稀疏种子点的平衡采样，泡沫位移和连通组件标签算法直接将物点分组形成实例，通过实例聚合模块来提高大型物体的 LPS 性能。与其他文献相比，在 SemanticKITII 验证和 nuScenes 验证中，PANet 在宽景分割任务中的表现达到了最高水平。

PANet: 稀疏实例提议和聚合的 LiDAR 全景分割

PANet: LiDAR Panoptic Segmentation with Sparse Instance Proposal and  Aggregation

Despite the great progress made by deep CNNs in image semantic segmentation,
they typically require a large number of densely-annotated images for training
and are difficult to generalize to unseen object categories. Few-shot
segmentation has thus been developed to learn to perform segmentation from only
a few annotated examples. In this paper, we tackle the challenging few-shot
segmentation problem from a metric learning perspective and present PANet, a
novel prototype alignment network to better utilize the information of the
support set. Our PANet learns class-specific prototype representations from a
few support images within an embedding space and then performs segmentation
over the query images through matching each pixel to the learned prototypes.
With non-parametric metric learning, PANet offers high-quality prototypes that
are representative for each semantic class and meanwhile discriminative for
different classes. Moreover, PANet introduces a prototype alignment
regularization between support and query. With this, PANet fully exploits
knowledge from the support and provides better generalization on few-shot
segmentation. Significantly, our model achieves the mIoU score of 48.1% and
55.7% on PASCAL-5i for 1-shot and 5-shot settings respectively, surpassing the
state-of-the-art method by 1.8% and 8.6%.

本研究提出了一种基于度量学习的原型对齐网络（PANet）来解决仅使用少量样本进行图像分割的问题，它通过学习每个语义类别的原型表示，并通过匹配每个像素到所学习的原型来执行查询图像的分割，最终在 PASCAL-5i 上超越了现有技术达到了 48.1% 和 55.7% 的 mIoU 得分（1-shot 和 5-shot 设置），是一种有效的 few-shot segmentation 方法。