Prototype-Based Support Example Miner and Triplet Loss for Deep Metric Learning

Abstract

Deep metric learning aims to learn a mapping function that projects input data into a high-dimensional embedding space, facilitating the clustering of similar data points while ensuring dissimilar ones are far apart. The most recent studies focus on designing a batch sampler and mining online triplets to achieve this purpose. Conventionally, hard negative mining schemes serve as the preferred batch sampler. However, most hard negative mining schemes search for hard examples in randomly selected mini-batches at each epoch, which often results in less-optimal hard examples and thus sub-optimal performances. Furthermore, Triplet Loss is commonly adopted to perform online triplet mining by pulling the hard positives close to and pushing the negatives away from the anchor. However, when the anchor in a triplet is an outlier, the positive example will be pulled away from the centroid of the cluster, thus resulting in a loose cluster and inferior performance. To address the above challenges, we propose the Prototype-based Support Example Miner (pSEM) and Triplet Loss (pTriplet Loss). First, we present a support example miner designed to mine the support classes on the prototype-based nearest neighbor graph of classes. Following this, we locate the support examples by searching for instances at the intersection between clusters of these support classes. Second, we develop a variant of Triplet Loss, referred to as a Prototype-based Triplet Loss. In our approach, a dynamically updated prototype is used to rectify outlier anchors, thus reducing their detrimental effects and facilitating a more robust formulation for Triplet Loss. Extensive experiments on typical Computer Vision (CV) and Natural Language Processing (NLP) tasks, namely person re-identification and few-shot relation extraction, demonstrated the effectiveness and generalizability of the proposed scheme, which consistently outperforms the state-of-the-art models.

Dual-Stage Attribute Embedding and Modality Consistency Learning-Based Visible–Infrared Person Re-Identification

“…In 2022, Zhang et al [3] proposed the N-tuple loss to uniformly optimize the distances between multiple samples from different classes in the feature distribution. In 2023, Yang et al [4] designed a Support Example Miner (SEM) and a variant of triplet loss to correct outlier samples that cause adverse effects. For unsupervised supervised ReID, in 2020, Yu et al [5] embedded the asymmetric metric into an unsupervised ReID network that applies transformations for features captured from different camera view to address the distortions.…”

“…Specifically, intra-class discrepancies indicate the variation of the same pedestrian across multiple images, while inter-class discrepancies indicate the variation between different pedestrians. For single-modality ReID, researchers have proposed supervised ReID approaches [1][2][3][4], unsupervised ReID approaches [5,6], and lifelong ReID approaches [7,8], which can substantially improve identification efficiency. The above approaches show that significant breakthroughs have been made in the research area of single-modality person re-identification.…”