Visual place recognition tasks often encounter significant challenges in
landmark detection due to the presence of irrelevant objects such as humans,
cars, and trees, despite the remarkable progress achieved by previous models,
especially in the context of transformers. To address this issue, we propose a
novel method that effectively leverages the strengths of vision transformers.
By employing a meticulous selection process, our approach identifies and
isolates specific patches within the image that correspond to occluding
objects. To evaluate the efficacy of our method, we created augmented datasets
and conducted comprehensive testing. The results demonstrate the superior
accuracy achieved by our proposed approach. This research contributes to the
advancement of landmark detection in visual place recognition and shows the
potential of leveraging vision transformers to overcome challenges posed by
cluttered real-world scenarios.

本研究提出了一种能够有效利用视觉 Transformer 的方法，通过精选和隔离图像中与遮挡物相对应的特定区域，以改进视觉地点识别中的地标检测问题，在拥挤的实际情境中显示了视觉 Transformer 的潜力，并证明了该方法的卓越准确性。

在混乱的现实场景中使用视觉变换增强地标检测

Enhancing Landmark Detection in Cluttered Real-World Scenarios with  Vision Transformers

Existing face datasets often lack sufficient representation of occluding
objects, which can hinder recognition, but also supply meaningful information
to understand the visual context. In this work, we introduce Extended Labeled
Faces in-the-Wild (ELFW), a dataset supplementing with additional face-related
categories -- and also additional faces -- the originally released semantic
labels in the vastly used Labeled Faces in-the-Wild (LFW) dataset.
Additionally, two object-based data augmentation techniques are deployed to
synthetically enrich under-represented categories which, in benchmarking
experiments, reveal that not only segmenting the augmented categories improves,
but also the remaining ones benefit.

本文介绍 Extended Labeled Faces in-the-Wild (ELFW) 数据集，补充了原先使用的 Labeled Faces in-the-Wild (LFW) 数据集的语义标签，以及通过基于对象的数据增强技术来增加原有的人脸类别，该技术在基准测试中改善了已有的类别以及曾被低估的类别。

Labeled Faces in-the-Wild 扩展版 (ELFW): 增强人脸分割类别

Extended Labeled Faces in-the-Wild (ELFW): Augmenting Classes for Face  Segmentation

We present a new algorithm for multi-region segmentation of 2D images with
objects that may partially occlude each other. Our algorithm is based on the
observation hat human performance on this task is based both on prior knowledge
about plausible shapes and taking into account the presence of occluding
objects whose shape is already known - once an occluded region is identified,
the shape prior can be used to guess the shape of the missing part. We capture
the former aspect using a deep learning model of shape; for the latter, we
simultaneously minimize the energy of all regions and consider only unoccluded
pixels for data agreement.
Existing algorithms incorporating object shape priors consider every object
separately in turn and can't distinguish genuine deviation from the expected
shape from parts missing due to occlusion. We show that our method
significantly improves on the performance of a representative algorithm, as
evaluated on both preprocessed natural and synthetic images. Furthermore, on
the synthetic images, we recover the ground truth segmentation with good
accuracy.

该研究提出了一种新的算法，用于 2D 图像的多区域分割，其中的对象可能会部分遮挡，该算法基于观察到人类在此任务上的表现受先前关于可信形状的知识以及考虑到已知遮挡对象的形状的影响 - 一旦识别出遮挡区域，形状先验可以用于猜测缺失部分的形状。