Recent few-shot segmentation (FSS) methods introduce an extra pre-training
stage before meta-training to obtain a stronger backbone, which has become a
standard step in few-shot learning. Despite the effectiveness, current
pre-training scheme suffers from the merged background problem: only base
classes are labelled as foregrounds, making it hard to distinguish between
novel classes and actual background. In this paper, we propose a new
pre-training scheme for FSS via decoupling the novel classes from background,
called Background Clustering Pre-Training (BCPT). Specifically, we adopt online
clustering to the pixel embeddings of merged background to explore the
underlying semantic structures, bridging the gap between pre-training and
adaptation to novel classes. Given the clustering results, we further propose
the background mining loss and leverage base classes to guide the clustering
process, improving the quality and stability of clustering results. Experiments
on PASCAL-5i and COCO-20i show that BCPT yields advanced performance. Code will
be available.

本研究提出了一种新的针对少样本分割的预训练方案，通过将新颖类别与背景解耦来解决合并背景问题，称为背景聚类预训练（BCPT），实验证明 BCPT 在 PASCAL-5i 和 COCO-20i 数据集上获得了先进的性能表现。

基于背景聚类的少样本分割预训练

Background Clustering Pre-training for Few-shot Segmentation

Recently, weakly supervised person search is proposed to discard
human-annotated identities and train the model with only bounding box
annotations. A natural way to solve this problem is to separate it into
detection and unsupervised re-identification (Re-ID) steps. However, in this
way, two important clues in unconstrained scene images are ignored. On the one
hand, existing unsupervised Re-ID models only leverage cropped images from
scene images but ignore its rich context information. On the other hand, there
are numerous unpaired persons in real-world scene images. Directly dealing with
them as independent identities leads to the long-tail effect, while completely
discarding them can result in serious information loss. In light of these
challenges, we introduce a Context-Guided and Unpaired-Assisted (CGUA) weakly
supervised person search framework. Specifically, we propose a novel
Context-Guided Cluster (CGC) algorithm to leverage context information in the
clustering process and an Unpaired-Assisted Memory (UAM) unit to distinguish
unpaired and paired persons by pushing them away. Extensive experiments
demonstrate that the proposed approach can surpass the state-of-the-art weakly
supervised methods by a large margin (more than 5% mAP on CUHK-SYSU). Moreover,
our method achieves comparable or better performance to the state-of-the-art
supervised methods by leveraging more diverse unlabeled data. Codes and models
will be released soon.

本研究提出了一种基于上下文引导和非成对辅助的弱监督人物搜索框架，其中引入了一种新的上下文引导聚类算法来利用聚类过程中的上下文信息和一个非成对辅助存储器单元来通过远离操作来区分未成对和成对的人。