Local feature detection and description play an important role in many
computer vision tasks, which are designed to detect and describe keypoints in
"any scene" and "any downstream task". Data-driven local feature learning
methods need to rely on pixel-level correspondence for training, which is
challenging to acquire at scale, thus hindering further improvements in
performance. In this paper, we propose SAMFeat to introduce SAM (segment
anything model), a fundamental model trained on 11 million images, as a teacher
to guide local feature learning and thus inspire higher performance on limited
datasets. To do so, first, we construct an auxiliary task of Pixel Semantic
Relational Distillation (PSRD), which distillates feature relations with
category-agnostic semantic information learned by the SAM encoder into a local
feature learning network, to improve local feature description using semantic
discrimination. Second, we develop a technique called Weakly Supervised
Contrastive Learning Based on Semantic Grouping (WSC), which utilizes semantic
groupings derived from SAM as weakly supervised signals, to optimize the metric
space of local descriptors. Third, we design an Edge Attention Guidance (EAG)
to further improve the accuracy of local feature detection and description by
prompting the network to pay more attention to the edge region guided by SAM.
SAMFeat's performance on various tasks such as image matching on HPatches, and
long-term visual localization on Aachen Day-Night showcases its superiority
over previous local features. The release code is available at
this https URL

SAMFeat 通过引入 SAM（segment anything model）作为教师模型，利用像素级对齐关系和语义信息，改进本地特征描述和检测的性能，展现了在不同任务中的优越性能。

Segment Anything Model 是学习局部特征的好教师

Segment Anything Model is a Good Teacher for Local Feature Learning

In this work, we present a novel learning-based framework that combines the
local accuracy of contrastive learning with the global consistency of geometric
approaches, for robust non-rigid matching. We first observe that while
contrastive learning can lead to powerful point-wise features, the learned
correspondences commonly lack smoothness and consistency, owing to the purely
combinatorial nature of the standard contrastive losses. To overcome this
limitation we propose to boost contrastive feature learning with two types of
smoothness regularization that inject geometric information into correspondence
learning. With this novel combination in hand, the resulting features are both
highly discriminative across individual points, and, at the same time, lead to
robust and consistent correspondences, through simple proximity queries. Our
framework is general and is applicable to local feature learning in both the 3D
and 2D domains. We demonstrate the superiority of our approach through
extensive experiments on a wide range of challenging matching benchmarks,
including 3D non-rigid shape correspondence and 2D image keypoint matching.

本研究提出了一个新的基于学习的框架，将对比学习的局部准确性与几何方法的全局一致性相结合，用于强健性非刚性匹配。

SRFeat：学习局部精确和全局一致的非刚性形状对应

SRFeat: Learning Locally Accurate and Globally Consistent Non-Rigid  Shape Correspondence

Recently, self-supervised vision transformers have attracted unprecedented
attention for their impressive representation learning ability. However, the
dominant method, contrastive learning, mainly relies on an instance
discrimination pretext task, which learns a global understanding of the image.
This paper incorporates local feature learning into self-supervised vision
transformers via Reconstructive Pre-training (RePre). Our RePre extends
contrastive frameworks by adding a branch for reconstructing raw image pixels
in parallel with the existing contrastive objective. RePre is equipped with a
lightweight convolution-based decoder that fuses the multi-hierarchy features
from the transformer encoder. The multi-hierarchy features provide rich
supervisions from low to high semantic information, which are crucial for our
RePre. Our RePre brings decent improvements on various contrastive frameworks
with different vision transformer architectures. Transfer performance in
downstream tasks outperforms supervised pre-training and state-of-the-art
(SOTA) self-supervised counterparts.

本文提出了一种基于重构预训练技术 (RePre) 的自监督视觉变压器模型，使用本地特征学习来替代对图像的全局理解，其中重构预训练通过向现有对比性目标添加像素重构分支，使用变压器编码器的多层次特征进行处理，并提供了从低到高语义信息的丰富监督信息；在各种对比学习框架和不同的视觉变换器架构上，该方法带来的转移性能超过了受监督的预训练和最先进的自监督方法。

RePre：利用重构预训练提高自监督视觉 Transformer 的性能

RePre: Improving Self-Supervised Vision Transformer with Reconstructive Pre-training

In this paper, we propose a novel method called AlignedReID that extracts a
global feature which is jointly learned with local features. Global feature
learning benefits greatly from local feature learning, which performs an
alignment/matching by calculating the shortest path between two sets of local
features, without requiring extra supervision. After the joint learning, we
only keep the global feature to compute the similarities between images. Our
method achieves rank-1 accuracy of 94.4% on Market1501 and 97.8% on CUHK03,
outperforming state-of-the-art methods by a large margin. We also evaluate
human-level performance and demonstrate that our method is the first to surpass
human-level performance on Market1501 and CUHK03, two widely used Person ReID
datasets.

本文提出了一种名为 AlignedReID 的新方法，通过局部特征的对齐匹配和全局特征的共同学习，提高了全局特征的学习效果并且超越了现有的 Person ReID 方法。在 Market1501 和 CUHK03 这两个广泛应用的人员再识别数据集方面，相对于现有的最优方法，本方法的排名 1 准确度分别提高了 94.4% 和 97.8%，超过了人类的水平。