Generalized Category Discovery in Hyperspectral Images via Prototype Subspace Modeling

• URL: http://arxiv.org/abs/2509.24017v1
• Date: Sun, 28 Sep 2025 18:16:59 GMT
• Title: Generalized Category Discovery in Hyperspectral Images via Prototype Subspace Modeling
• Abstract summary: Generalized category discovery(GCD) seeks to jointly identify both known and novel categories in unlabeled data.<n>Prior works have mainly focused on RGB images, their assumptions and modeling strategies do not generalize well to hyperspectral images(HSI), which are inherently high-dimensional and exhibit complex spectral structures.<n>We propose the first GCD framework tailored for HSI, introducing a prototype subspace modeling model to better capture class structure.
• Score: 42.598744707088535
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generalized category discovery~(GCD) seeks to jointly identify both known and novel categories in unlabeled data. While prior works have mainly focused on RGB images, their assumptions and modeling strategies do not generalize well to hyperspectral images~(HSI), which are inherently high-dimensional and exhibit complex spectral structures. In this paper, we propose the first GCD framework tailored for HSI, introducing a prototype subspace modeling model to better capture class structure. Instead of learning a single prototype vector for each category as in existing methods such as SimGCD, we model each category using a set of basis vectors, forming a subspace representation that enables greater expressiveness and discrimination in a high-dimensional feature space. To guide the learning of such bases, we enforce two key constraints: (1) a basis orthogonality constraint that promotes inter-class separability, and (2) a reconstruction constraint that ensures each prototype basis can effectively reconstruct its corresponding class samples. Experimental results on real-world HSI demonstrate that our method significantly outperforms state-of-the-art GCD methods, establishing a strong foundation for generalized category discovery in hyperspectral settings.

Related papers

• Dual Prototype-Conditioned Diffusion Model for Scalable Multi-Class Unsupervised Anomaly Detection in Large Category Spaces [21.943236094911892]
  We propose DPDiff-AD, a Dual Prototype-conditioned Diffusion model for large-scale multi-class Anomaly Detection.<n> DPDiff-AD models heterogeneous normal distributions through complementary local and global prototypes.<n>It achieves precise normality modeling, preserves structured separability as category cardinality grows, and enables scalable anomaly discrimination.
  arXiv  Detail & Related papers  (2026-05-23T05:10:18Z)
• Hyperbolic Enhanced Representation Learning for Incomplete Multi-view Clustering [57.38215918201251]
  We propose HERL, a Hyperbolic Enhanced Representation Learning framework for Incomplete Multi-View Clustering.<n> operating within the Poincaré ball, HERL constructs a structure-aware latent space to enhance representation learning.<n>We show that HERL consistently outperforms state-of-the-art approaches.
  arXiv  Detail & Related papers  (2026-04-18T10:50:46Z)
• Hyperbolic Category Discovery [12.654574453480635]
  Generalized Category Discovery (GCD) aims to categorize all images in the unlabelled subset, regardless of whether they belong to known or unknown classes.<n>Hyperbolic framework for learning hierarchy-aware representations and classifiers for GCD is presented.
  arXiv  Detail & Related papers  (2025-04-08T15:12:33Z)
• GCC: Generative Calibration Clustering [55.44944397168619]
  We propose a novel Generative Clustering (GCC) method to incorporate feature learning and augmentation into clustering procedure. First, we develop a discrimirative feature alignment mechanism to discover intrinsic relationship across real and generated samples. Second, we design a self-supervised metric learning to generate more reliable cluster assignment.
  arXiv  Detail & Related papers  (2024-04-14T01:51:11Z)
• Unfolding ADMM for Enhanced Subspace Clustering of Hyperspectral Images [43.152314090830174]
  We introduce an innovative clustering architecture for hyperspectral images (HSI) by unfolding an iterative solver based on the Alternating Direction Method of Multipliers (ADMM) for sparse subspace clustering. Our approach captures well the structural characteristics of HSI data by employing the K nearest neighbors algorithm as part of a structure preservation module.
  arXiv  Detail & Related papers  (2024-04-10T15:51:46Z)
• Automatically Discovering Novel Visual Categories with Self-supervised Prototype Learning [68.63910949916209]
  This paper tackles the problem of novel category discovery (NCD), which aims to discriminate unknown categories in large-scale image collections. We propose a novel adaptive prototype learning method consisting of two main stages: prototypical representation learning and prototypical self-training. We conduct extensive experiments on four benchmark datasets and demonstrate the effectiveness and robustness of the proposed method with state-of-the-art performance.
  arXiv  Detail & Related papers  (2022-08-01T16:34:33Z)
• On Hyperbolic Embeddings in 2D Object Detection [76.12912000278322]
  We study whether a hyperbolic geometry better matches the underlying structure of the object classification space. We incorporate a hyperbolic classifier in two-stage, keypoint-based, and transformer-based object detection architectures. We observe categorical class hierarchies emerging in the structure of the classification space, resulting in lower classification errors and boosting the overall object detection performance.
  arXiv  Detail & Related papers  (2022-03-15T16:43:40Z)
• Structure-Aware Feature Generation for Zero-Shot Learning [108.76968151682621]
  We introduce a novel structure-aware feature generation scheme, termed as SA-GAN, to account for the topological structure in learning both the latent space and the generative networks. Our method significantly enhances the generalization capability on unseen-classes and consequently improve the classification performance.
  arXiv  Detail & Related papers  (2021-08-16T11:52:08Z)
• Joint and Progressive Subspace Analysis (JPSA) with Spatial-Spectral Manifold Alignment for Semi-Supervised Hyperspectral Dimensionality Reduction [48.73525876467408]
  We propose a novel technique for hyperspectral subspace analysis. The technique is called joint and progressive subspace analysis (JPSA) Experiments are conducted to demonstrate the superiority and effectiveness of the proposed JPSA on two widely-used hyperspectral datasets.
  arXiv  Detail & Related papers  (2020-09-21T16:29:59Z)
• Graph Convolutional Subspace Clustering: A Robust Subspace Clustering Framework for Hyperspectral Image [6.332208511335129]
  We present a novel subspace clustering framework called Graph Convolutional Subspace Clustering (GCSC) for robust HSI clustering. Specifically, the framework recasts the self-expressiveness property of the data into the non-Euclidean domain. We show that traditional subspace clustering models are the special forms of our framework with the Euclidean data.
  arXiv  Detail & Related papers  (2020-04-22T10:09:19Z)

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.