MetaCloth: Learning Unseen Tasks of Dense Fashion Landmark Detection from a Few Samples

TL;DR

MetaCloth introduces a meta-learning framework for few-shot dense fashion landmark detection, capable of generalizing to unseen clothing categories with varying numbers of landmarks from limited labeled samples.

Contribution

It presents a novel meta-learning approach that dynamically handles varying landmark counts across clothing categories, improving few-shot detection performance.

Findings

MetaCloth outperforms existing methods significantly.

It effectively generalizes to unseen clothing categories.

The framework handles varying numbers of landmarks per task.

Abstract

Recent advanced methods for fashion landmark detection are mainly driven by training convolutional neural networks on large-scale fashion datasets, which has a large number of annotated landmarks. However, such large-scale annotations are difficult and expensive to obtain in real-world applications, thus models that can generalize well from a small amount of labelled data are desired. We investigate this problem of few-shot fashion landmark detection, where only a few labelled samples are available for an unseen task. This work proposes a novel framework named MetaCloth via meta-learning, which is able to learn unseen tasks of dense fashion landmark detection with only a few annotated samples. Unlike previous meta-learning work that focus on solving "N-way K-shot" tasks, where each task predicts N number of classes by training with K annotated samples for each class (N is fixed for all seen and unseen tasks), a task in MetaCloth detects N different landmarks for different clothing categories using K samples, where N varies across tasks, because different clothing categories usually have various number of landmarks. Therefore, numbers of parameters are various for different seen and unseen tasks in MetaCloth. MetaCloth is carefully designed to dynamically generate different numbers of parameters for different tasks, and learn a generalizable feature extraction network from a few annotated samples with a set of good initialization parameters. Extensive experiments show that MetaCloth outperforms its counterparts by a large margin.