In this paper, we propose a novel concept of path consistency to learn robust
object matching without using manual object identity supervision. Our key idea
is that, to track a object through frames, we can obtain multiple different
association results from a model by varying the frames it can observe, i.e.,
skipping frames in observation. As the differences in observations do not alter
the identities of objects, the obtained association results should be
consistent. Based on this rationale, we generate multiple observation paths,
each specifying a different set of frames to be skipped, and formulate the Path
Consistency Loss that enforces the association results are consistent across
different observation paths. We use the proposed loss to train our object
matching model with only self-supervision. By extensive experiments on three
tracking datasets (MOT17, PersonPath22, KITTI), we demonstrate that our method
outperforms existing unsupervised methods with consistent margins on various
evaluation metrics, and even achieves performance close to supervised methods.

本文提出了一种新颖的路径一致性概念，用于学习强健的物体匹配，无需使用手动物体标识监督。通过改变模型可以观察的帧的方式，即跳过观察中的帧，我们可以得到多个不同的关联结果来追踪一个物体。由于观察的差异不会改变物体的身份，所得到的关联结果应该是一致的。基于这一理论，我们生成多个观察路径，每个路径都指定了一个不同的要跳过的帧的集合，并制定了路径一致性损失，以强制要求不同观察路径下的关联结果是一致的。通过在三个跟踪数据集（MOT17、PersonPath22、KITTI）上进行大量实验证明，我们的方法在各种评估指标上优于现有的无监督方法，并且甚至接近监督方法的性能水平。

自监督多目标跟踪与路径一致性

Self-Supervised Multi-Object Tracking with Path Consistency

Semantic understanding of 3D objects is crucial in many applications such as
object manipulation. However, it is hard to give a universal definition of
point-level semantics that everyone would agree on. We observe that people have
a consensus on semantic correspondences between two areas from different
objects, but are less certain about the exact semantic meaning of each area.
Therefore, we argue that by providing human labeled correspondences between
different objects from the same category instead of explicit semantic labels,
one can recover rich semantic information of an object. In this paper, we
introduce a new dataset named CorresPondenceNet. Based on this dataset, we are
able to learn dense semantic embeddings with a novel geodesic consistency loss.
Accordingly, several state-of-the-art networks are evaluated on this
correspondence benchmark. We further show that CorresPondenceNet could not only
boost fine-grained understanding of heterogeneous objects but also cross-object
registration and partial object matching.

本文提出了一种基于同一类别不同对象之间的语义对应关系来恢复物体丰富语义信息的方法，同时介绍了 CorresPondenceNet 数据集，通过该数据集训练得到的密集语义嵌入可以促进异构对象的细粒度理解以及跨对象注册和部分对象匹配。