Smart Mining for Deep Metric Learning

Harwood, Ben; G, Vijay Kumar B; Carneiro, Gustavo; Reid, Ian; Drummond, Tom

doi:10.48550/arxiv.1704.01285

Cited by 10 publications

(1 citation statement)

References 9 publications

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“…To further improve the triplet loss some authors either extends the number of tuples in the loss [1], [28], [34] or/and propose novel sampling methods [8], [36]. However, the reported improvements are not high, thus, we did not use them in our experiments.…”

Section: Loss Functionsmentioning

confidence: 99%

A Strong Baseline for Fashion Retrieval with Person Re-Identification Models

Wieczorek¹,

Michałowski²,

Wróblewska³

et al. 2020

Preprint

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Fashion retrieval is the challenging task of finding an exact match for fashion items contained within an image. Difficulties arise from the fine-grained nature of clothing items, very large intra-class and inter-class variance. Additionally, query and source images for the task usually come from different domains -street photos and catalogue photos respectively. Due to these differences, a significant gap in quality, lighting, contrast, background clutter and item presentation exists between domains. As a result, fashion retrieval is an active field of research both in academia and the industry. Inspired by recent advancements in Person Re-Identification research, we adapt leading ReID models to be used in fashion retrieval tasks. We introduce a simple baseline model for fashion retrieval, significantly outperforming previous state-of-the-art results despite a much simpler architecture. We conduct in-depth experiments on Street2Shop and DeepFashion datasets and validate our results. Finally, we propose a cross-domain (cross-dataset) evaluation method to test the robustness of fashion retrieval models.

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Section: Loss Functionsmentioning

confidence: 99%

A Strong Baseline for Fashion Retrieval with Person Re-Identification Models

Wieczorek¹,

Michałowski²,

Wróblewska³

et al. 2020

Preprint

View full text Add to dashboard Cite

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A metric learning-based method for biomedical entity linking

Le,

Nguyen

2023

Front. Res. Metr. Anal.

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Biomedical entity linking task is the task of mapping mention(s) that occur in a particular textual context to a unique concept or entity in a knowledge base, e.g., the Unified Medical Language System (UMLS). One of the most challenging aspects of the entity linking task is the ambiguity of mentions, i.e., (1) mentions whose surface forms are very similar, but which map to different entities in different contexts, and (2) entities that can be expressed using diverse types of mentions. Recent studies have used BERT-based encoders to encode mentions and entities into distinguishable representations such that their similarity can be measured using distance metrics. However, most real-world biomedical datasets suffer from severe imbalance, i.e., some classes have many instances while others appear only once or are completely absent from the training data. A common way to address this issue is to down-sample the dataset, i.e., to reduce the number instances of the majority classes to make the dataset more balanced. In the context of entity linking, down-sampling reduces the ability of the model to comprehensively learn the representations of mentions in different contexts, which is very important. To tackle this issue, we propose a metric-based learning method that treats a given entity and its mentions as a whole, regardless of the number of mentions in the training set. Specifically, our method uses a triplet loss-based function in conjunction with a clustering technique to learn the representation of mentions and entities. Through evaluations on two challenging biomedical datasets, i.e., MedMentions and BC5CDR, we show that our proposed method is able to address the issue of imbalanced data and to perform competitively with other state-of-the-art models. Moreover, our method significantly reduces computational cost in both training and inference steps. Our source code is publicly available here.

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Generalization in Metric Learning: Should the Embedding Layer be the Embedding Layer?

Vo¹,

Hays²

2018

Preprint

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Smart Mining for Deep Metric Learning

Cited by 10 publications

References 9 publications

A Strong Baseline for Fashion Retrieval with Person Re-Identification Models

A Strong Baseline for Fashion Retrieval with Person Re-Identification Models

A metric learning-based method for biomedical entity linking

Generalization in Metric Learning: Should the Embedding Layer be the Embedding Layer?

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