Unsupervised object discovery (UOD) refers to the task of discriminating the
whole region of objects from the background within a scene without relying on
labeled datasets, which benefits the task of bounding-box-level localization
and pixel-level segmentation. This task is promising due to its ability to
discover objects in a generic manner. We roughly categorise existing techniques
into two main directions, namely the generative solutions based on image
resynthesis, and the clustering methods based on self-supervised models. We
have observed that the former heavily relies on the quality of image
reconstruction, while the latter shows limitations in effectively modeling
semantic correlations. To directly target at object discovery, we focus on the
latter approach and propose a novel solution by incorporating weakly-supervised
contrastive learning (WCL) to enhance semantic information exploration. We
design a semantic-guided self-supervised learning model to extract high-level
semantic features from images, which is achieved by fine-tuning the feature
encoder of a self-supervised model, namely DINO, via WCL. Subsequently, we
introduce Principal Component Analysis (PCA) to localize object regions. The
principal projection direction, corresponding to the maximal eigenvalue, serves
as an indicator of the object region(s). Extensive experiments on benchmark
unsupervised object discovery datasets demonstrate the effectiveness of our
proposed solution. The source code and experimental results are publicly
available via our project page at this https URL

利用弱监督对比学习的语义引导自监督学习模型，在基于自我监督模型 DINO 的特征编码器上微调，采用主成分分析（PCA）来定位物体区域，实验证明了该解决方案的有效性。

弱监督对比学习用于无监督物体发现

Weakly-supervised Contrastive Learning for Unsupervised Object Discovery

In step with the digitalization of transportation, we are witnessing a
growing range of path-based smart-city applications, e.g., travel-time
estimation and travel path ranking. A temporal path(TP) that includes temporal
information, e.g., departure time, into the path is fundamental to enable such
applications. In this setting, it is essential to learn generic temporal path
representations(TPRs) that consider spatial and temporal correlations
simultaneously and that can be used in different applications, i.e., downstream
tasks. Existing methods fail to achieve the goal since (i) supervised methods
require large amounts of task-specific labels when training and thus fail to
generalize the obtained TPRs to other tasks; (ii) through unsupervised methods
can learn generic representations, they disregard the temporal aspect, leading
to sub-optimal results. To contend with the limitations of existing solutions,
we propose a Weakly-Supervised Contrastive (WSC) learning model. We first
propose a temporal path encoder that encodes both the spatial and temporal
information of a temporal path into a TPR. To train the encoder, we introduce
weak labels that are easy and inexpensive to obtain and are relevant to
different tasks, e.g., temporal labels indicating peak vs. off-peak hours from
departure times. Based on the weak labels, we construct meaningful positive and
negative temporal path samples by considering both spatial and temporal
information, which facilities training the encoder using contrastive learning
by pulling closer to the positive samples' representations while pushing away
the negative samples' representations. To better guide contrastive learning, we
propose a learning strategy based on Curriculum Learning such that the learning
performs from easy to hard training instances. Experiments studies verify the
effectiveness of the proposed method.

本研究提出一种弱监督对比学习模型，通过构造有意义的正负时空路径样本来训练具有空间和时间信息的时间路径编码器，以获得通用的时间路径表示，用于智能城市应用的不同任务，同时采用课程学习策略来指导对比学习

弱监督时间路径表示学习与对比课程学习 — 扩展版

Weakly-supervised Temporal Path Representation Learning with Contrastive Curriculum Learning -- Extended Version

We argue that a form of the valuable information provided by the auxiliary
information is its implied data clustering information. For instance,
considering hashtags as auxiliary information, we can hypothesize that an
Instagram image will be semantically more similar with the same hashtags. With
this intuition, we present a two-stage weakly-supervised contrastive learning
approach. The first stage is to cluster data according to its auxiliary
information. The second stage is to learn similar representations within the
same cluster and dissimilar representations for data from different clusters.
Our empirical experiments suggest the following three contributions. First,
compared to conventional self-supervised representations, the
auxiliary-information-infused representations bring the performance closer to
the supervised representations, which use direct downstream labels as
supervision signals. Second, our approach performs the best in most cases, when
comparing our approach with other baseline representation learning methods that
also leverage auxiliary data information. Third, we show that our approach also
works well with unsupervised constructed clusters (e.g., no auxiliary
information), resulting in a strong unsupervised representation learning
approach.

本文提出了一种二阶段的弱监督对比学习方法，第一阶段根据辅助信息对数据进行聚类，第二阶段在同一簇中学习相似的表征和不同簇的数据的不相似的表征。通过实验表明，与传统的自监督表示相比，辅助信息注入表示使性能更接近于监督表示。同时，本方法在与其他基线表示学习方法进行比较时表现最佳，并且其也适用于无监督构建簇，展示了强大的无监督表示学习方法。