3 research outputs found
Reducing Class Collapse in Metric Learning with Easy Positive Sampling
Metric learning seeks perceptual embeddings where visually similar instances
are close and dissimilar instances are apart, but learn representation can be
sub-optimal when the distribution of intra-class samples is diverse and
distinct sub-clusters are present. We theoretically prove and empirically show
that under reasonable noise assumptions, prevalent embedding losses in metric
learning, e.g., triplet loss, tend to project all samples of a class with
various modes onto a single point in the embedding space, resulting in class
collapse that usually renders the space ill-sorted for classification or
retrieval. To address this problem, we propose a simple modification to the
embedding losses such that each sample selects its nearest same-class
counterpart in a batch as the positive element in the tuple. This allows for
the presence of multiple sub-clusters within each class. The adaptation can be
integrated into a wide range of metric learning losses. Our method demonstrates
clear benefits on various fine-grained image retrieval datasets over a variety
of existing losses; qualitative retrieval results show that samples with
similar visual patterns are indeed closer in the embedding space
Color Variants Identification in Fashion e-commerce via Contrastive Self-Supervised Representation Learning
In this paper, we utilize deep visual Representation Learning to address an
important problem in fashion e-commerce: color variants identification, i.e.,
identifying fashion products that match exactly in their design (or style), but
only to differ in their color. At first we attempt to tackle the problem by
obtaining manual annotations (depicting whether two products are color
variants), and train a supervised triplet loss based neural network model to
learn representations of fashion products. However, for large scale real-world
industrial datasets such as addressed in our paper, it is infeasible to obtain
annotations for the entire dataset, while capturing all the difficult corner
cases. Interestingly, we observed that color variants are essentially
manifestations of color jitter based augmentations. Thus, we instead explore
Self-Supervised Learning (SSL) to solve this problem. We observed that existing
state-of-the-art SSL methods perform poor, for our problem. To address this, we
propose a novel SSL based color variants model that simultaneously focuses on
different parts of an apparel. Quantitative and qualitative evaluation shows
that our method outperforms existing SSL methods, and at times, the supervised
model.Comment: Accepted In IJCAI-21 Weakly Supervised Representation Learning (WSRL)
worksho
Hierarchical Proxy-based Loss for Deep Metric Learning
Proxy-based metric learning losses are superior to pair-based losses due to
their fast convergence and low training complexity. However, existing
proxy-based losses focus on learning class-discriminative features while
overlooking the commonalities shared across classes which are potentially
useful in describing and matching samples. Moreover, they ignore the implicit
hierarchy of categories in real-world datasets, where similar subordinate
classes can be grouped together. In this paper, we present a framework that
leverages this implicit hierarchy by imposing a hierarchical structure on the
proxies and can be used with any existing proxy-based loss. This allows our
model to capture both class-discriminative features and class-shared
characteristics without breaking the implicit data hierarchy. We evaluate our
method on five established image retrieval datasets such as In-Shop and SOP.
Results demonstrate that our hierarchical proxy-based loss framework improves
the performance of existing proxy-based losses, especially on large datasets
which exhibit strong hierarchical structure.Comment: Accepted to WACV202