AutoNovel: Automatically Discovering and Learning Novel Visual Categories

• URL: http://arxiv.org/abs/2106.15252v1
• Date: Tue, 29 Jun 2021 11:12:16 GMT
• Title: AutoNovel: Automatically Discovering and Learning Novel Visual Categories
• Authors: Kai Han and Sylvestre-Alvise Rebuffi and S\'ebastien Ehrhardt and Andrea Vedaldi and Andrew Zisserman
• Abstract summary: We present a new approach called AutoNovel to tackle the problem of discovering novel classes in an image collection given labelled examples of other classes. We evaluate AutoNovel on standard classification benchmarks and substantially outperform current methods for novel category discovery.
• Score: 138.80332861066287
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We tackle the problem of discovering novel classes in an image collection given labelled examples of other classes. We present a new approach called AutoNovel to address this problem by combining three ideas: (1) we suggest that the common approach of bootstrapping an image representation using the labelled data only introduces an unwanted bias, and that this can be avoided by using self-supervised learning to train the representation from scratch on the union of labelled and unlabelled data; (2) we use ranking statistics to transfer the model's knowledge of the labelled classes to the problem of clustering the unlabelled images; and, (3) we train the data representation by optimizing a joint objective function on the labelled and unlabelled subsets of the data, improving both the supervised classification of the labelled data, and the clustering of the unlabelled data. Moreover, we propose a method to estimate the number of classes for the case where the number of new categories is not known a priori. We evaluate AutoNovel on standard classification benchmarks and substantially outperform current methods for novel category discovery. In addition, we also show that AutoNovel can be used for fully unsupervised image clustering, achieving promising results.

Related papers

• Generalized Category Discovery with Clustering Assignment Consistency [56.92546133591019]
  Generalized category discovery (GCD) is a recently proposed open-world task. We propose a co-training-based framework that encourages clustering consistency. Our method achieves state-of-the-art performance on three generic benchmarks and three fine-grained visual recognition datasets.
  arXiv  Detail & Related papers  (2023-10-30T00:32:47Z)
• Learning Semi-supervised Gaussian Mixture Models for Generalized Category Discovery [36.01459228175808]
  We propose an EM-like framework that alternates between representation learning and class number estimation. We evaluate our framework on both generic image classification datasets and challenging fine-grained object recognition datasets.
  arXiv  Detail & Related papers  (2023-05-10T13:47:38Z)
• CiPR: An Efficient Framework with Cross-instance Positive Relations for Generalized Category Discovery [21.380021266251426]
  generalized category discovery (GCD) considers the open-world problem of automatically clustering a partially labelled dataset. In this paper, we address the GCD problem with an unknown category number for the unlabelled data. We propose a framework, named CiPR, to bootstrap the representation by exploiting Cross-instance Positive Relations.
  arXiv  Detail & Related papers  (2023-04-14T05:25:52Z)
• XCon: Learning with Experts for Fine-grained Category Discovery [4.787507865427207]
  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.
  arXiv  Detail & Related papers  (2022-08-03T08:03:12Z)
• Novel Class Discovery without Forgetting [72.52222295216062]
  We identify and formulate a new, pragmatic problem setting of NCDwF: Novel Class Discovery without Forgetting. We propose a machine learning model to incrementally discover novel categories of instances from unlabeled data. We introduce experimental protocols based on CIFAR-10, CIFAR-100 and ImageNet-1000 to measure the trade-off between knowledge retention and novel class discovery.
  arXiv  Detail & Related papers  (2022-07-21T17:54:36Z)
• Generalized Category Discovery [148.32255950504182]
  We consider a highly general image recognition setting wherein, given a labelled and unlabelled set of images, the task is to categorize all images in the unlabelled set. Here, the unlabelled images may come from labelled classes or from novel ones. We first establish strong baselines by taking state-of-the-art algorithms from novel category discovery and adapting them for this task. We then introduce a simple yet effective semi-supervised $k$-means method to cluster the unlabelled data into seen and unseen classes.
  arXiv  Detail & Related papers  (2022-01-07T18:58:35Z)
• Unsupervised Person Re-identification via Softened Similarity Learning [122.70472387837542]
  Person re-identification (re-ID) is an important topic in computer vision. This paper studies the unsupervised setting of re-ID, which does not require any labeled information. Experiments on two image-based and video-based datasets demonstrate state-of-the-art performance.
  arXiv  Detail & Related papers  (2020-04-07T17:16:41Z)
• Automatically Discovering and Learning New Visual Categories with Ranking Statistics [145.89790963544314]
  We tackle the problem of discovering novel classes in an image collection given labelled examples of other classes. We learn a general-purpose clustering model and use the latter to identify the new classes in the unlabelled data. We evaluate our approach on standard classification benchmarks and outperform current methods for novel category discovery by a significant margin.
  arXiv  Detail & Related papers  (2020-02-13T18:53:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.