AMEND: Adaptive Margin and Expanded Neighborhood for Efficient Generalized Category Discovery

Banerjee, A and Kallooriyakath, LS and Biswas, S (2024) AMEND: Adaptive Margin and Expanded Neighborhood for Efficient Generalized Category Discovery. In: UNSPECIFIED, pp. 2090-2099.

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Abstract

Generalized Category Discovery aims to discover and cluster images from previously unseen classes, in addition to classifying images from seen classes correctly. In this work, we propose a simple, yet effective framework for this task, which not only performs on-par or better with the current approaches but is also significantly more efficient in terms of computational requirements. Our first contribution is to use expanded neighborhood information in contrastive learning to generate robust and generalizable features. To generate more discriminative feature representations, especially for fine-grained datasets and confusing classes, we propose a class-wise adaptive margin regularizer that aims at increasing the angular separation among the prototypes of all classes. Extensive experiments on three generic as well as four fine-grained benchmark datasets show the usefulness of the proposed Adaptive Margin and Expanded Neighborhood (AMEND) framework. © 2024 IEEE.

Item Type:	Conference Paper
Publication:	Proceedings - 2024 IEEE Winter Conference on Applications of Computer Vision, WACV 2024
Publisher:	Institute of Electrical and Electronics Engineers Inc.
Additional Information:	The copyright for this article belongs to Institute of Electrical and Electronics Engineers Inc.
Department/Centre:	Division of Electrical Sciences > Electrical Engineering
Date Deposited:	24 May 2024 05:39
Last Modified:	24 May 2024 05:39
URI:	https://eprints.iisc.ac.in/id/eprint/85042

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