Recently, self-supervised metric learning has raised attention for the
potential to learn a generic distance function. It overcomes the limitations of
conventional supervised one, e.g., scalability and label biases. Despite
progress in this domain, current benchmarks, incorporating a narrow scope of
classes, stop the nuanced evaluation of semantic representations. To bridge
this gap, we introduce a large-scale benchmark with diversity and granularity
of classes, Statistical Metric Learning Benchmark (SMLB) built upon
ImageNet-21K and WordNet. SMLB is designed to rigorously evaluate the
discriminative discernment and generalizability across more than 14M images,
20K classes, and 16K taxonomic nodes. Alongside, we propose novel evaluation
metrics -- `overlap' for separability and `aSTD' for consistency -- to measure
distance statistical information, which are efficient and robust to the change
of class number. Our benchmark offers a novel perspective of evaluating the
quality of representations beyond accuracy. Our findings reveal the limitations
of supervised learning and the class bias inherent in SSL models, offering
insights into potential areas for future model enhancement.

自我监督度量学习通过引入大规模基准测试 SMLB (Statistical Metric Learning Benchmark) 在超过 14M 张图像、20K 个类别和 16K 个分类节点中严格评估鉴别性和泛化性，同时提供了衡量距离统计信息的新型评估指标 overlap 和 aSTD，揭示了监督学习的局限性和 SSL 模型中固有的类别偏差，为未来模型的改进提供了洞察。

超越准确性：自监督学习表示评估的统计度量和基准

Beyond Accuracy: Statistical Measures and Benchmark for Evaluation of  Representation from Self-Supervised Learning

3D object trackers usually require training on large amounts of annotated
data that is expensive and time-consuming to collect. Instead, we propose
leveraging vast unlabeled datasets by self-supervised metric learning of 3D
object trackers, with a focus on data association. Large scale annotations for
unlabeled data are cheaply obtained by automatic object detection and
association across frames. We show how these self-supervised annotations can be
used in a principled manner to learn point-cloud embeddings that are effective
for 3D tracking. We estimate and incorporate uncertainty in self-supervised
tracking to learn more robust embeddings, without needing any labeled data. We
design embeddings to differentiate objects across frames, and learn them using
uncertainty-aware self-supervised training. Finally, we demonstrate their
ability to perform accurate data association across frames, towards effective
and accurate 3D tracking. Project videos and code are at
this https URL

本研究提出利用自我监督度量学习的方式，以数据关联为重点，从大量未标注数据集中学习 3D 物体跟踪器。自我监督注释可以被用于有原则的学习点云嵌入，实现数据关联和 3D 跟踪。估计并结合跟踪的不确定度，设计嵌入以识别不同帧间的物体，通过未标注数据的训练，实现了更强大的跟踪效果。最终得到的跟踪器能够在帧间执行准确的数据关联，在 3D 跟踪中表现出色。