Category-agnostic pose estimation (CAPE) aims to predict keypoints for
arbitrary classes given a few support images annotated with keypoints. Existing
methods only rely on the features extracted at support keypoints to predict or
refine the keypoints on query image, but a few support feature vectors are
local and inadequate for CAPE. Considering that human can quickly perceive
potential keypoints of arbitrary objects, we propose a novel framework for CAPE
based on such potential keypoints (named as meta-points). Specifically, we
maintain learnable embeddings to capture inherent information of various
keypoints, which interact with image feature maps to produce meta-points
without any support. The produced meta-points could serve as meaningful
potential keypoints for CAPE. Due to the inevitable gap between inherency and
annotation, we finally utilize the identities and details offered by support
keypoints to assign and refine meta-points to desired keypoints in query image.
In addition, we propose a progressive deformable point decoder and a slacked
regression loss for better prediction and supervision. Our novel framework not
only reveals the inherency of keypoints but also outperforms existing methods
of CAPE. Comprehensive experiments and in-depth studies on large-scale MP-100
dataset demonstrate the effectiveness of our framework.

通过学习模型与图像特征交互生成称为 ' 超点 ' 的潜在关键点，综合利用支持关键点信息提供监督和精化，从而实现了更好的类别不可知姿态估计。

元点学习与细化的类别无关姿态估计

Meta-Point Learning and Refining for Category-Agnostic Pose Estimation

Traditional semantic segmentation tasks require a large number of labels and
are difficult to identify unlearned categories. Few-shot semantic segmentation
(FSS) aims to use limited labeled support images to identify the segmentation
of new classes of objects, which is very practical in the real world. Previous
researches were primarily based on prototypes or correlations. Due to colors,
textures, and styles are similar in the same image, we argue that the query
image can be regarded as its own support image. In this paper, we proposed the
Target-aware Bi-Transformer Network (TBTNet) to equivalent treat of support
images and query image. A vigorous Target-aware Transformer Layer (TTL) also be
designed to distill correlations and force the model to focus on foreground
information. It treats the hypercorrelation as a feature, resulting a
significant reduction in the number of feature channels. Benefit from this
characteristic, our model is the lightest up to now with only 0.4M learnable
parameters. Futhermore, TBTNet converges in only 10% to 25% of the training
epochs compared to traditional methods. The excellent performance on standard
FSS benchmarks of PASCAL-5i and COCO-20i proves the efficiency of our method.
Extensive ablation studies were also carried out to evaluate the effectiveness
of Bi-Transformer architecture and TTL.

使用少量有标签的支持图像来识别新类别对象的分割，本文提出了 Target-aware Bi-Transformer Network（TBTNet）以等效处理支持图像和查询图像，并设计了强大的 Target-aware Transformer Layer（TTL）以提取相关性并强制模型关注前景信息，该方法在 PASCAL-5i 和 COCO-20i 标准 FSS 基准测试上表现出色。

针对目标的双变压器用于少样本分割

Target-aware Bi-Transformer for Few-shot Segmentation

Few-shot semantic segmentation aims to learn to segment unseen class objects
with the guidance of only a few support images. Most previous methods rely on
the pixel-level label of support images. In this paper, we focus on a more
challenging setting, in which only the image-level labels are available. We
propose a general framework to firstly generate coarse masks with the help of
the powerful vision-language model CLIP, and then iteratively and mutually
refine the mask predictions of support and query images. Extensive experiments
on PASCAL-5i and COCO-20i datasets demonstrate that our method not only
outperforms the state-of-the-art weakly supervised approaches by a significant
margin, but also achieves comparable or better results to recent supervised
methods. Moreover, our method owns an excellent generalization ability for the
images in the wild and uncommon classes. Code will be available at
this https URL

本文提出了一种利用视觉 - 语言模型 CLIP 生成粗略掩模并迭代互相调整支持和查询图片的掩模预测的框架，实验结果表明该方法不仅在 PASCAL-5i 和 COCO-20i 数据集上优于最先进的弱监督方法，还能够取得与最近的有监督方法相当甚至更好的结果，并且具有出色的野外图像和非常规类别的泛化能力。