XCon: Learning with Experts for Fine-grained Category Discovery
- URL: http://arxiv.org/abs/2208.01898v1
- Date: Wed, 3 Aug 2022 08:03:12 GMT
- Title: XCon: Learning with Experts for Fine-grained Category Discovery
- Authors: Yixin Fei, Zhongkai Zhao, Siwei Yang, Bingchen Zhao
- Abstract summary: We present a novel method called Expert-Contrastive Learning (XCon) to help the model to mine useful information from the images.
Experiments on fine-grained datasets show a clear improved performance over the previous best methods, indicating the effectiveness of our method.
- Score: 4.787507865427207
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We address the problem of generalized category discovery (GCD) in this paper,
i.e. clustering the unlabeled images leveraging the information from a set of
seen classes, where the unlabeled images could contain both seen classes and
unseen classes. The seen classes can be seen as an implicit criterion of
classes, which makes this setting different from unsupervised clustering where
the cluster criteria may be ambiguous. We mainly concern the problem of
discovering categories within a fine-grained dataset since it is one of the
most direct applications of category discovery, i.e. helping experts discover
novel concepts within an unlabeled dataset using the implicit criterion set
forth by the seen classes. State-of-the-art methods for generalized category
discovery leverage contrastive learning to learn the representations, but the
large inter-class similarity and intra-class variance pose a challenge for the
methods because the negative examples may contain irrelevant cues for
recognizing a category so the algorithms may converge to a local-minima. We
present a novel method called Expert-Contrastive Learning (XCon) to help the
model to mine useful information from the images by first partitioning the
dataset into sub-datasets using k-means clustering and then performing
contrastive learning on each of the sub-datasets to learn fine-grained
discriminative features. Experiments on fine-grained datasets show a clear
improved performance over the previous best methods, indicating the
effectiveness of our method.
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