Data to 14 Oct 2026

CANet: Class-Agnostic Segmentation Networks with Iterative Refinement and Attentive Few-Shot Learning

arXiv 1903.02351 ↗First posted 6 Mar 2019cs.CVcited by 217

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Abstract (arXiv)

Recent progress in semantic segmentation is driven by deep Convolutional Neural Networks and large-scale labeled image datasets. However, data labeling for pixel-wise segmentation is tedious and costly. Moreover, a trained model can only make predictions within a set of pre-defined classes. In this paper, we present CANet, a class-agnostic segmentation network that performs few-shot segmentation on new classes with only a few annotated images available. Our network consists of a two-branch dense comparison module which performs multi-level feature comparison between the support image and the query image, and an iterative optimization module which iteratively refines the predicted results. Furthermore, we introduce an attention mechanism to effectively fuse information from multiple support examples under the setting of k-shot learning. Experiments on PASCAL VOC 2012 show that our method achieves a mean Intersection-over-Union score of 55.4% for 1-shot segmentation and 57.1% for 5-shot segmentation, outperforming state-of-the-art methods by a large margin of 14.6% and 13.2%, respectively.

Key facts. Cited by 217 papers in the map (data through 14 Oct 2026). First posted on arXiv 6 Mar 2019.

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Citing papers per year: 2021: 59; 2022: 54; 2023: 47; 2024: 35; 2025: 16; 2026: 6.

217 papers.

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2021Image Segmentation Using Text and Image Promptscited by 512
2021ReStyle: A Residual-Based StyleGAN Encoder via Iterative Refinementcited by 248
2021Adaptive Prototype Learning and Allocation for Few-Shot Segmentationcited by 207
2021Hypercorrelation Squeeze for Few-Shot Segmentationcited by 193
2021Few-Shot Incremental Learning with Continually Evolved Classifierscited by 187
2022A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunitiescited by 163
2021Few-Shot Segmentation via Cycle-Consistent Transformercited by 133
2022Learning What Not to Segment: A New Perspective on Few-Shot Segmentationcited by 120
2021Simpler is Better: Few-shot Semantic Segmentation with Classifier Weight Transformercited by 116
2022Cost Aggregation with 4D Convolutional Swin Transformer for Few-Shot Segmentationcited by 114
2022Self-Support Few-Shot Semantic Segmentationcited by 96
2023Vision-Language Models in Remote Sensing: Current Progress and Future Trendscited by 93

38 more

2021Self-Guided and Cross-Guided Learning for Few-Shot Segmentationcited by 92
2023UniverSeg: Universal Medical Image Segmentationcited by 91
2023Hierarchical Dense Correlation Distillation for Few-Shot Segmentationcited by 84
2022FS6D: Few-Shot 6D Pose Estimation of Novel Objectscited by 81
2021Mining Latent Classes for Few-shot Segmentationcited by 69
2022Dense Cross-Query-and-Support Attention Weighted Mask Aggregation for Few-Shot Segmentationcited by 65
2022Few-shot Object Counting with Similarity-Aware Feature Enhancementcited by 62
2022Feature-Proxy Transformer for Few-Shot Segmentationcited by 58
2022Learning Non-target Knowledge for Few-shot Semantic Segmentationcited by 57
2021Learning Meta-class Memory for Few-Shot Semantic Segmentationcited by 56
2023FaceStudio: Put Your Face Everywhere in Secondscited by 53
2022Hierarchical Normalization for Robust Monocular Depth Estimationcited by 52
2022Singular Value Fine-tuning: Few-shot Segmentation requires Few-parameters Fine-tuningcited by 50
2023LLaFS: When Large Language Models Meet Few-Shot Segmentationcited by 48
2021Prototypical Cross-Attention Networks for Multiple Object Tracking and Segmentationcited by 43
2022Dynamic Prototype Convolution Network for Few-Shot Semantic Segmentationcited by 43
2022Anomaly Detection-Inspired Few-Shot Medical Image Segmentation Through Self-Supervision With Supervoxelscited by 42
2021One-Shot Object Affordance Detection in the Wildcited by 40
2022Intermediate Prototype Mining Transformer for Few-Shot Semantic Segmentationcited by 37
2021HCRF-Flow: Scene Flow from Point Clouds with Continuous High-order CRFs and Position-aware Flow Embeddingcited by 36
2021Few-shot Segmentation with Optimal Transport Matching and Message Flowcited by 36
2022Pose for Everything: Towards Category-Agnostic Pose Estimationcited by 36
2024Bridge the Points: Graph-based Few-shot Segment Anything Semanticallycited by 35
2023Self-Calibrated Cross Attention Network for Few-Shot Segmentationcited by 32
2021PFENet++: Boosting Few-shot Semantic Segmentation with the Noise-filtered Context-aware Prior Maskcited by 31
2024Cross-Domain Few-Shot Segmentation via Iterative Support-Query Correspondence Miningcited by 30
2022Beyond the Prototype: Divide-and-conquer Proxies for Few-shot Segmentationcited by 28
2023Prototype Adaption and Projection for Few- and Zero-shot 3D Point Cloud Semantic Segmentationcited by 26
2024APSeg: Auto-Prompt Network for Cross-Domain Few-Shot Semantic Segmentationcited by 26
2022Integrative Few-Shot Learning for Classification and Segmentationcited by 25
2022Interclass Prototype Relation for Few-Shot Segmentationcited by 25
2023MIANet: Aggregating Unbiased Instance and General Information for Few-Shot Semantic Segmentationcited by 25
2023Neuralizer: General Neuroimage Analysis without Re-Trainingcited by 24
2023Distilling Self-Supervised Vision Transformers for Weakly-Supervised Few-Shot Classification & Segmentationcited by 24
2021Anti-aliasing Semantic Reconstruction for Few-Shot Semantic Segmentationcited by 23
2022Bidirectional Feature Globalization for Few-shot Semantic Segmentation of 3D Point Cloud Scenescited by 23
2022A Strong Baseline for Generalized Few-Shot Semantic Segmentationcited by 22
2022Mask Matching Transformer for Few-Shot Segmentationcited by 22

50 most cited of 217 All 217 (JSON)