HiLo: A Learning Framework for Generalized Category Discovery Robust to Domain Shifts

• URL: http://arxiv.org/abs/2408.04591v1
• Date: Thu, 8 Aug 2024 17:04:06 GMT
• Title: HiLo: A Learning Framework for Generalized Category Discovery Robust to Domain Shifts
• Authors: Hongjun Wang, Sagar Vaze, Kai Han,
• Abstract summary: We introduce a new task and method to handle GCD when the unlabelled data also contains images from different domains to the labelled set. Our proposed HiLo' networks extract High-level semantic and Low-level domain features. We extend our method with a specialized domain augmentation tailored for the GCD task, as well as a curriculum learning approach.
• Score: 17.520137576423593
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Generalized Category Discovery (GCD) is a challenging task in which, given a partially labelled dataset, models must categorize all unlabelled instances, regardless of whether they come from labelled categories or from new ones. In this paper, we challenge a remaining assumption in this task: that all images share the same domain. Specifically, we introduce a new task and method to handle GCD when the unlabelled data also contains images from different domains to the labelled set. Our proposed `HiLo' networks extract High-level semantic and Low-level domain features, before minimizing the mutual information between the representations. Our intuition is that the clusterings based on domain information and semantic information should be independent. We further extend our method with a specialized domain augmentation tailored for the GCD task, as well as a curriculum learning approach. Finally, we construct a benchmark from corrupted fine-grained datasets as well as a large-scale evaluation on DomainNet with real-world domain shifts, reimplementing a number of GCD baselines in this setting. We demonstrate that HiLo outperforms SoTA category discovery models by a large margin on all evaluations.

Related papers

• CDAD-Net: Bridging Domain Gaps in Generalized Category Discovery [9.505699498746976]
  Generalized Category Discovery (GCD) is a tool to cluster unlabeled samples of known and novel classes. We present Across Domain Generalized Category Discovery (AD-GCD) and bring forth CDAD-NET as a remedy. CDAD-NET is architected to synchronize potential known class samples across both the labeled (source) and unlabeled (target) datasets. Experimentally, CDAD-NET eclipses existing literature with a performance increment of 8-15% on three AD-GCD benchmarks we present.
  arXiv  Detail & Related papers  (2024-04-08T10:05:24Z)
• 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)
• Upcycling Models under Domain and Category Shift [95.22147885947732]
  We introduce an innovative global and local clustering learning technique (GLC) We design a novel, adaptive one-vs-all global clustering algorithm to achieve the distinction across different target classes. Remarkably, in the most challenging open-partial-set DA scenario, GLC outperforms UMAD by 14.8% on the VisDA benchmark.
  arXiv  Detail & Related papers  (2023-03-13T13:44:04Z)
• Few-Shot Object Detection in Unseen Domains [4.36080478413575]
  Few-shot object detection (FSOD) has thrived in recent years to learn novel object classes with limited data. We propose various data augmentations techniques on the few shots of novel classes to account for all possible domain-specific information. Our experiments on the T-LESS dataset show that the proposed approach succeeds in alleviating the domain gap considerably.
  arXiv  Detail & Related papers  (2022-04-11T13:16:41Z)
• Context-Conditional Adaptation for Recognizing Unseen Classes in Unseen Domains [48.17225008334873]
  We propose a feature generative framework integrated with a COntext COnditional Adaptive (COCOA) Batch-Normalization. The generated visual features better capture the underlying data distribution enabling us to generalize to unseen classes and domains at test-time. We thoroughly evaluate and analyse our approach on established large-scale benchmark - DomainNet.
  arXiv  Detail & Related papers  (2021-07-15T17:51:16Z)
• Cross-domain Contrastive Learning for Unsupervised Domain Adaptation [108.63914324182984]
  Unsupervised domain adaptation (UDA) aims to transfer knowledge learned from a fully-labeled source domain to a different unlabeled target domain. We build upon contrastive self-supervised learning to align features so as to reduce the domain discrepancy between training and testing sets.
  arXiv  Detail & Related papers  (2021-06-10T06:32:30Z)
• Inferring Latent Domains for Unsupervised Deep Domain Adaptation [54.963823285456925]
  Unsupervised Domain Adaptation (UDA) refers to the problem of learning a model in a target domain where labeled data are not available. This paper introduces a novel deep architecture which addresses the problem of UDA by automatically discovering latent domains in visual datasets. We evaluate our approach on publicly available benchmarks, showing that it outperforms state-of-the-art domain adaptation methods.
  arXiv  Detail & Related papers  (2021-03-25T14:33:33Z)
• Robust Domain-Free Domain Generalization with Class-aware Alignment [4.442096198968069]
  Domain-Free Domain Generalization (DFDG) is a model-agnostic method to achieve better generalization performance on the unseen test domain. DFDG uses novel strategies to learn domain-invariant class-discriminative features. It obtains competitive performance on both time series sensor and image classification public datasets.
  arXiv  Detail & Related papers  (2021-02-17T17:46:06Z)
• Exploring Category-Agnostic Clusters for Open-Set Domain Adaptation [138.29273453811945]
  We present Self-Ensembling with Category-agnostic Clusters (SE-CC) -- a novel architecture that steers domain adaptation with category-agnostic clusters in target domain. clustering is performed over all the unlabeled target samples to obtain the category-agnostic clusters, which reveal the underlying data space structure peculiar to target domain.
  arXiv  Detail & Related papers  (2020-06-11T16:19:02Z)
• Universal-RCNN: Universal Object Detector via Transferable Graph R-CNN [117.80737222754306]
  We present a novel universal object detector called Universal-RCNN. We first generate a global semantic pool by integrating all high-level semantic representation of all the categories. An Intra-Domain Reasoning Module learns and propagates the sparse graph representation within one dataset guided by a spatial-aware GCN.
  arXiv  Detail & Related papers  (2020-02-18T07:57:45Z)

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.