Visual place recognition (VPR) is a fundamental task of computer vision for
visual localization. Existing methods are trained using image pairs that either
depict the same place or not. Such a binary indication does not consider
continuous relations of similarity between images of the same place taken from
different positions, determined by the continuous nature of camera pose. The
binary similarity induces a noisy supervision signal into the training of VPR
methods, which stall in local minima and require expensive hard mining
algorithms to guarantee convergence. Motivated by the fact that two images of
the same place only partially share visual cues due to camera pose differences,
we deploy an automatic re-annotation strategy to re-label VPR datasets. We
compute graded similarity labels for image pairs based on available
localization metadata. Furthermore, we propose a new Generalized Contrastive
Loss (GCL) that uses graded similarity labels for training contrastive
networks. We demonstrate that the use of the new labels and GCL allow to
dispense from hard-pair mining, and to train image descriptors that perform
better in VPR by nearest neighbor search, obtaining superior or comparable
results than methods that require expensive hard-pair mining and re-ranking
techniques. Code and models available at:
this https URL

本论文提出了一种改进的视觉定位方法，通过使用连续相似性标签和广义对比损失函数代替传统的二元相似性标签和硬对样本挖掘，使得视觉识别表现得到了明显提升。

带有分级相似度监督的高效大规模场所识别

Data-efficient Large Scale Place Recognition with Graded Similarity  Supervision

Visual place recognition is a challenging task in computer vision and a key
component of camera-based localization and navigation systems. Recently,
Convolutional Neural Networks (CNNs) achieved high results and good
generalization capabilities. They are usually trained using pairs or triplets
of images labeled as either similar or dissimilar, in a binary fashion. In
practice, the similarity between two images is not binary, but continuous.
Furthermore, training these CNNs is computationally complex and involves costly
pair and triplet mining strategies. We propose a Generalized Contrastive loss
(GCL) function that relies on image similarity as a continuous measure, and use
it to train a siamese CNN. Furthermore, we present three techniques for
automatic annotation of image pairs with labels indicating their degree of
similarity, and deploy them to re-annotate the MSLS, TB-Places, and 7Scenes
datasets. We demonstrate that siamese CNNs trained using the GCL function and
the improved annotations consistently outperform their binary counterparts. Our
models trained on MSLS outperform the state-of-the-art methods, including
NetVLAD, NetVLAD-SARE, AP-GeM and Patch-NetVLAD, and generalize well on the
Pittsburgh30k, Tokyo 24/7, RobotCar Seasons v2 and Extended CMU Seasons
datasets. Furthermore, training a siamese network using the GCL function does
not require complex pair mining. We release the source code at
this https URL

研究了 CNN 在视觉地点识别中的应用，提出了一种基于图像相似度的广义对比损失功能（GCL）函数，用于训练 Siamese CNN 并使用改进的注释来实现自动注释和视觉地点识别，实验证明 CNN 在这方面的效果优于目前主流的方法。

用共享权重网络的一般化对比学习进行地点识别的优化

Generalized Contrastive Optimization of Siamese Networks for Place  Recognition

This paper introduces a semi-supervised contrastive learning framework and
its application to text-independent speaker verification. The proposed
framework employs generalized contrastive loss (GCL). GCL unifies losses from
two different learning frameworks, supervised metric learning and unsupervised
contrastive learning, and thus it naturally determines the loss for
semi-supervised learning. In experiments, we applied the proposed framework to
text-independent speaker verification on the VoxCeleb dataset. We demonstrate
that GCL enables the learning of speaker embeddings in three manners,
supervised learning, semi-supervised learning, and unsupervised learning,
without any changes in the definition of the loss function.

介绍了一种半监督对比学习框架，应用于无文本说话人验证，通过广义对比损失，将监督度量学习和无监督对比学习统一，从而自然地确定了半监督学习的损失。在 VoxCeleb 数据集上进行实验，演示了 GCL 使得听众嵌入在三种方式下学习，监督学习、半监督学习和无监督学习，而不需要更改损失函数的定义。