The key to the success of few-shot segmentation (FSS) lies in how to
effectively utilize support samples. Most solutions compress support foreground
(FG) features into prototypes, but lose some spatial details. Instead, others
use cross attention to fuse query features with uncompressed support FG. Query
FG could be fused with support FG, however, query background (BG) cannot find
matched BG features in support FG, yet inevitably integrates dissimilar
features. Besides, as both query FG and BG are combined with support FG, they
get entangled, thereby leading to ineffective segmentation. To cope with these
issues, we design a self-calibrated cross attention (SCCA) block. For efficient
patch-based attention, query and support features are firstly split into
patches. Then, we design a patch alignment module to align each query patch
with its most similar support patch for better cross attention. Specifically,
SCCA takes a query patch as Q, and groups the patches from the same query image
and the aligned patches from the support image as K&V. In this way, the query
BG features are fused with matched BG features (from query patches), and thus
the aforementioned issues will be mitigated. Moreover, when calculating SCCA,
we design a scaled-cosine mechanism to better utilize the support features for
similarity calculation. Extensive experiments conducted on PASCAL-5^i and
COCO-20^i demonstrate the superiority of our model, e.g., the mIoU score under
5-shot setting on COCO-20^i is 5.6%+ better than previous state-of-the-arts.
The code is available at this https URL

通用关键词：少样本分割、支持样本、交叉注意、自校准交叉注意、mIoU 分数。摘要：本文提出了一种自校准交叉注意模块来解决几个问题，包括在少样本分割中如何有效利用支持样本、如何融合查询背景特征、以及如何计算相似度。实验证明，该模型在 COCO-20i 上的 5-shot 设置下的 mIoU 分数比之前的最新技术提升了 5.6%+

自校准交叉注意力网络用于少样本分割

Self-Calibrated Cross Attention Network for Few-Shot Segmentation

Most existing unsupervised person re-identification (Re-ID) methods use
clustering to generate pseudo labels for model training. Unfortunately,
clustering sometimes mixes different true identities together or splits the
same identity into two or more sub clusters. Training on these noisy clusters
substantially hampers the Re-ID accuracy. Due to the limited samples in each
identity, we suppose there may lack some underlying information to well reveal
the accurate clusters. To discover these information, we propose an Implicit
Sample Extension (\OurWholeMethod) method to generate what we call support
samples around the cluster boundaries. Specifically, we generate support
samples from actual samples and their neighbouring clusters in the embedding
space through a progressive linear interpolation (PLI) strategy. PLI controls
the generation with two critical factors, i.e., 1) the direction from the
actual sample towards its K-nearest clusters and 2) the degree for mixing up
the context information from the K-nearest clusters. Meanwhile, given the
support samples, ISE further uses a label-preserving loss to pull them towards
their corresponding actual samples, so as to compact each cluster.
Consequently, ISE reduces the "sub and mixed" clustering errors, thus improving
the Re-ID performance. Extensive experiments demonstrate that the proposed
method is effective and achieves state-of-the-art performance for unsupervised
person Re-ID. Code is available at:
https://github.com/PaddlePaddle/PaddleClas.

本文提出了一种基于隐式样本扩展的无监督人物再识别方法，通过生成支持样本来减少聚类中混合和分离的误差，同时通过一个能够压缩每个簇的标签保持损失，使得簇更加紧凑，从而提高了人物再识别的准确性。