Refining Context-Entangled Content Segmentation via Curriculum Selection and Anti-Curriculum Promotion

• URL: http://arxiv.org/abs/2602.01183v1
• Date: Sun, 01 Feb 2026 12:12:24 GMT
• Title: Refining Context-Entangled Content Segmentation via Curriculum Selection and Anti-Curriculum Promotion
• Authors: Chunming He, Rihan Zhang, Fengyang Xiao, Dingming Zhang, Zhiwen Cao, Sina Farsiu,
• Abstract summary: CurriSeg is a dual-phase learning framework that unifies curriculum and anti-curriculum principles to improve representation reliability.<n>In the Curriculum Selection phase, CurriSeg dynamically selects training data based on the temporal statistics of sample losses.<n>In the Anti-Curriculum Promotion phase, we design Spectral-Blindness Fine-Tuning, which suppresses high-frequency components to enforce dependence on low-frequency structural and contextual cues.
• Score: 14.803333807611414
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Biological learning proceeds from easy to difficult tasks, gradually reinforcing perception and robustness. Inspired by this principle, we address Context-Entangled Content Segmentation (CECS), a challenging setting where objects share intrinsic visual patterns with their surroundings, as in camouflaged object detection. Conventional segmentation networks predominantly rely on architectural enhancements but often ignore the learning dynamics that govern robustness under entangled data distributions. We introduce CurriSeg, a dual-phase learning framework that unifies curriculum and anti-curriculum principles to improve representation reliability. In the Curriculum Selection phase, CurriSeg dynamically selects training data based on the temporal statistics of sample losses, distinguishing hard-but-informative samples from noisy or ambiguous ones, thus enabling stable capability enhancement. In the Anti-Curriculum Promotion phase, we design Spectral-Blindness Fine-Tuning, which suppresses high-frequency components to enforce dependence on low-frequency structural and contextual cues and thus strengthens generalization. Extensive experiments demonstrate that CurriSeg achieves consistent improvements across diverse CECS benchmarks without adding parameters or increasing total training time, offering a principled view of how progression and challenge interplay to foster robust and context-aware segmentation. Code will be released.

Related papers

• Unsupervised Online 3D Instance Segmentation with Synthetic Sequences and Dynamic Loss [52.28880405119483]
  Unsupervised online 3D instance segmentation is a fundamental yet challenging task.<n>Existing methods, such as UNIT, have made progress in this direction but remain constrained by limited training diversity.<n>We propose a new framework that enriches the training distribution through synthetic point cloud sequence generation.
  arXiv  Detail & Related papers  (2025-09-27T08:53:27Z)
• AURORA: Augmented Understanding via Structured Reasoning and Reinforcement Learning for Reference Audio-Visual Segmentation [113.75682363364004]
  AURORA is a framework designed to enhance genuine reasoning and language comprehension in reference audio-visual segmentation.<n>AURORA achieves state-of-the-art performance on Ref-AVS benchmarks and generalizes effectively to unreferenced segmentation.
  arXiv  Detail & Related papers  (2025-08-04T07:47:38Z)
• Feature-Based vs. GAN-Based Learning from Demonstrations: When and Why [50.191655141020505]
  This survey provides a comparative analysis of feature-based and GAN-based approaches to learning from demonstrations.<n>We argue that the dichotomy between feature-based and GAN-based methods is increasingly nuanced.
  arXiv  Detail & Related papers  (2025-07-08T11:45:51Z)
• StPR: Spatiotemporal Preservation and Routing for Exemplar-Free Video Class-Incremental Learning [79.44594332189018]
  Class-Incremental Learning (CIL) seeks to develop models that continuously learn new action categories over time without previously acquired knowledge.<n>Existing approaches either rely on forgetting, raising concerns over memory and privacy, or adapt static image-based methods that neglect temporal modeling.<n>We propose a unified and exemplar-free VCIL framework that explicitly disentangles and preserves information.
  arXiv  Detail & Related papers  (2025-05-20T06:46:51Z)
• Semi-supervised Semantic Segmentation with Multi-Constraint Consistency Learning [81.02648336552421]
  We propose a Multi-Constraint Consistency Learning approach to facilitate the staged enhancement of the encoder and decoder.<n>Self-adaptive feature masking and noise injection are designed in an instance-specific manner to perturb the features for robust learning of the decoder.<n> Experimental results on Pascal VOC2012 and Cityscapes datasets demonstrate that our proposed MCCL achieves new state-of-the-art performance.
  arXiv  Detail & Related papers  (2025-03-23T03:21:33Z)
• Salience-Invariant Consistent Policy Learning for Generalization in Visual Reinforcement Learning [12.9372563969007]
  Generalizing policies to unseen scenarios remains a critical challenge in visual reinforcement learning.<n>In unseen environments, distracting pixels may lead agents to extract representations containing task-irrelevant information.<n>We propose the Salience-Invariant Consistent Policy Learning algorithm, an efficient framework for zero-shot generalization.
  arXiv  Detail & Related papers  (2025-02-12T12:00:16Z)
• Uncertainty-Participation Context Consistency Learning for Semi-supervised Semantic Segmentation [9.546065701435532]
  Semi-supervised semantic segmentation has attracted considerable attention for its ability to mitigate the reliance on extensive labeled data.<n>This paper proposes the Uncertainty-participation Context Consistency Learning (UCCL) method to explore richer supervisory signals.
  arXiv  Detail & Related papers  (2024-12-23T06:49:59Z)
• Intrinsic Dynamics-Driven Generalizable Scene Representations for Vision-Oriented Decision-Making Applications [0.21051221444478305]
  How to improve the ability of scene representation is a key issue in vision-oriented decision-making applications. We propose an intrinsic dynamics-driven representation learning method with sequence models in visual reinforcement learning.
  arXiv  Detail & Related papers  (2024-05-30T06:31:03Z)
• Relaxed Contrastive Learning for Federated Learning [48.96253206661268]
  We propose a novel contrastive learning framework to address the challenges of data heterogeneity in federated learning. Our framework outperforms all existing federated learning approaches by huge margins on the standard benchmarks.
  arXiv  Detail & Related papers  (2024-01-10T04:55:24Z)
• Learning to Generate Training Datasets for Robust Semantic Segmentation [37.9308918593436]
  We propose a novel approach to improve the robustness of semantic segmentation techniques. We design Robusta, a novel conditional generative adversarial network to generate realistic and plausible perturbed images. Our results suggest that this approach could be valuable in safety-critical applications.
  arXiv  Detail & Related papers  (2023-08-01T10:02:26Z)
• HiCLRE: A Hierarchical Contrastive Learning Framework for Distantly Supervised Relation Extraction [24.853265244512954]
  We propose a hierarchical contrastive learning Framework for DistantlySupervised relation extraction (HiCLRE) to reduce noisy sentences. Specifically, we propose a three-level hierarchical learning framework to interact with cross levels, generating the de-noising context-aware representations. Experiments demonstrate that HiCLRE significantly outperforms strong baselines in various mainstream DSRE datasets.
  arXiv  Detail & Related papers  (2022-02-27T12:48:26Z)
• Adaptive Discrete Communication Bottlenecks with Dynamic Vector Quantization [76.68866368409216]
  We propose learning to dynamically select discretization tightness conditioned on inputs. We show that dynamically varying tightness in communication bottlenecks can improve model performance on visual reasoning and reinforcement learning tasks.
  arXiv  Detail & Related papers  (2022-02-02T23:54:26Z)

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.