Chaotic Contrastive Learning for Robust Texture Classification

• URL: http://arxiv.org/abs/2605.05012v1
• Date: Wed, 06 May 2026 15:11:28 GMT
• Title: Chaotic Contrastive Learning for Robust Texture Classification
• Abstract summary: This paper introduces a novel framework that synergizes Self-Supervised Learning with deterministic chaotic dynamics.<n>We propose a chaotic contrastive pre-training strategy, where pixel-wise chaotic maps act as non-linear data augmentation techniques.<n> Experimental results on six texture benchmarks demonstrate the superiority of the proposed method.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Texture classification is a pivotal task in computer vision, presenting unique challenges due to high inter-class similarity and the sensitivity of structural patterns to scale and illumination changes. While Convolutional Neural Networks (CNNs) and recent Vision Transformers have set performance benchmarks, they often require extensive labeled datasets or struggle to generalize across domains due to an over-reliance on color and shape features. This paper introduces a novel framework that synergizes Self-Supervised Learning (SSL) with deterministic chaotic dynamics. We propose a chaotic contrastive pre-training strategy, where pixel-wise chaotic maps, specifically Logistic, Tent, and Sine maps, act as non-linear data augmentation techniques. These chaotic perturbations, grounded in ergodic theory, force the network to learn topologically robust features by mimicking complex environmental noise and reflectance variations. Furthermore, we introduce an attention-based feature ensemble that fuses high-level semantic representations from a supervised large backbone with low-frequency structural features from a chaos-pretrained tiny encoder. Experimental results on six texture benchmarks (FMD, UMD, KTH-TIPS2-b, DTD, GTOS, and 1200Tex) demonstrate the superiority of the proposed method, outperforming state-of-the-art approaches and achieving promising accuracies on all the analyzed datasets.

Related papers

• Generalized Synthetic Image Detection with Enhanced RGB-Noise Representation Learning [33.97020349697987]
  We propose RNSIDNet, a novel forensic framework that achieves robust detection through enhanced RGB-Noise representation learning.<n>Specifically, our method employs a dual-branch architecture where global RGB semantics, extracted by an attention-refined CLIP backbone, dynamically modulate high-frequency noise artifacts captured by Bayar convolutions via a Feature-wise Linear Modulation (FiLM) module.<n>To further enhance the learned representations, we design a Hard Sample-aware Contrastive Learning (HSCL) strategy.
  arXiv  Detail & Related papers  (2026-07-07T14:52:22Z)
• Hierarchical Consistency Learning for Test-time Adaptation in Camouflage Perception [50.278200968044665]
  Camouflaged object detection (COD) aims to localize targets that exhibit minimal perceptual differences from backgrounds through physical attributes.<n>Existing methods, constrained by the static train-then-freeze paradigm, suffer from domain rigidity and dependency annotation.<n>We propose the hierarchical consistency learning framework, which integrates test-time adaptation for dynamic representation recalibration.
  arXiv  Detail & Related papers  (2026-05-25T09:57:46Z)
• Synergizing Multigrid Algorithms with Vision Transformer: A Novel Approach to Enhance the Seismic Foundation Model [8.86328796040398]
  Existing vision transformers (ViTs) with sequential tokenization ignore the intrinsic pattern and fail to grasp both the high- and low-frequency seismic information efficiently and effectively.<n>This work introduces a novel adaptive two-grid foundation model training strategy (ADATG) with Hilbert encoding specifically tailored for seismogram data.
  arXiv  Detail & Related papers  (2025-11-17T08:37:28Z)
• The Geometry of Cortical Computation: Manifold Disentanglement and Predictive Dynamics in VCNet [0.0]
  This paper introduces Visual Cortex Network (VCNet), a novel neural network architecture.<n> VCNet is framed as a geometric framework that emulates key biological mechanisms.<n>We show that VCNet achieves state-of-the-art accuracy of 92.1% on Spots-10 and 74.4% on the light field dataset.
  arXiv  Detail & Related papers  (2025-08-05T01:52:42Z)
• DGIQA: Depth-guided Feature Attention and Refinement for Generalizable Image Quality Assessment [9.851063768646847]
  A long-held challenge in no-reference image quality assessment is the lack of objective generalization to unseen natural distortions.<n>We integrate a novel Depth-Guided cross-attention and refinement mechanism, which distills scene depth and spatial features into a structure-aware representation.<n>We implement TCB and Depth-CAR as multimodal attention-based projection functions to select the most informative features.<n> Experimental results demonstrate that our proposed DGIQA model achieves state-of-the-art (SOTA) performance on both synthetic and authentic benchmark datasets.
  arXiv  Detail & Related papers  (2025-05-29T20:52:56Z)
• LayerMix: Enhanced Data Augmentation through Fractal Integration for Robust Deep Learning [1.786053901581251]
  Deep learning models often struggle to maintain consistent performance when confronted with Out-of-Distribution (OOD) samples.<n>We introduce LayerMix, an innovative data augmentation approach that systematically enhances model robustness.<n>Our method generates semantically consistent synthetic samples that significantly improve neural network generalization capabilities.
  arXiv  Detail & Related papers  (2025-01-08T22:22:44Z)
• Task-Oriented Real-time Visual Inference for IoVT Systems: A Co-design Framework of Neural Networks and Edge Deployment [61.20689382879937]
  Task-oriented edge computing addresses this by shifting data analysis to the edge. Existing methods struggle to balance high model performance with low resource consumption. We propose a novel co-design framework to optimize neural network architecture.
  arXiv  Detail & Related papers  (2024-10-29T19:02:54Z)
• Towards Evaluating the Robustness of Visual State Space Models [63.14954591606638]
  Vision State Space Models (VSSMs) have demonstrated remarkable performance in visual perception tasks. However, their robustness under natural and adversarial perturbations remains a critical concern. We present a comprehensive evaluation of VSSMs' robustness under various perturbation scenarios.
  arXiv  Detail & Related papers  (2024-06-13T17:59:44Z)
• Bilevel Fast Scene Adaptation for Low-Light Image Enhancement [50.639332885989255]
  Enhancing images in low-light scenes is a challenging but widely concerned task in the computer vision. Main obstacle lies in the modeling conundrum from distribution discrepancy across different scenes. We introduce the bilevel paradigm to model the above latent correspondence. A bilevel learning framework is constructed to endow the scene-irrelevant generality of the encoder towards diverse scenes.
  arXiv  Detail & Related papers  (2023-06-02T08:16:21Z)
• Intriguing Properties of Vision Transformers [114.28522466830374]
  Vision transformers (ViT) have demonstrated impressive performance across various machine vision problems. We systematically study this question via an extensive set of experiments and comparisons with a high-performing convolutional neural network (CNN) We show effective features of ViTs are due to flexible receptive and dynamic fields possible via the self-attention mechanism.
  arXiv  Detail & Related papers  (2021-05-21T17:59:18Z)
• PredRNN: A Recurrent Neural Network for Spatiotemporal Predictive Learning [109.84770951839289]
  We present PredRNN, a new recurrent network for learning visual dynamics from historical context. We show that our approach obtains highly competitive results on three standard datasets.
  arXiv  Detail & Related papers  (2021-03-17T08:28:30Z)

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.