Data to 7 Oct 2026

Hypercorrelation Squeeze for Few-Shot Segmentation

arXiv 2104.01538 ↗First posted 4 Apr 2021cs.CVcited by 193

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Abstract (arXiv)

Few-shot semantic segmentation aims at learning to segment a target object from a query image using only a few annotated support images of the target class. This challenging task requires to understand diverse levels of visual cues and analyze fine-grained correspondence relations between the query and the support images. To address the problem, we propose Hypercorrelation Squeeze Networks (HSNet) that leverages multi-level feature correlation and efficient 4D convolutions. It extracts diverse features from different levels of intermediate convolutional layers and constructs a collection of 4D correlation tensors, i.e., hypercorrelations. Using efficient center-pivot 4D convolutions in a pyramidal architecture, the method gradually squeezes high-level semantic and low-level geometric cues of the hypercorrelation into precise segmentation masks in coarse-to-fine manner. The significant performance improvements on standard few-shot segmentation benchmarks of PASCAL-5i, COCO-20i, and FSS-1000 verify the efficacy of the proposed method.

Key facts. Cited by 193 papers in the map (data through 7 Oct 2026). Builds on 69 of its 89 references that are in the map. First posted on arXiv 4 Apr 2021.

Builds on

69 of 89 references.

References · 69

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38 more

2016Matching Networks for One Shot Learningcited by 2,246
2018Group Normalizationcited by 1,514
2017Learning to Compare: Relation Network for Few-Shot Learningcited by 1,139
2019PANet: Few-Shot Image Semantic Segmentation with Prototype Alignmentcited by 449
2018SiamRPN++: Evolution of Siamese Visual Tracking with Very Deep Networkscited by 445
2018TADAM: Task dependent adaptive metric for improved few-shot learningcited by 408
2015Learning Deconvolution Network for Semantic Segmentationcited by 365
2017One-Shot Learning for Semantic Segmentationcited by 337
2017Few-Shot Learning with Graph Neural Networkscited by 324
2020Self-supervised Equivariant Attention Mechanism for Weakly Supervised Semantic Segmentationcited by 299
2018Few-Shot Learning via Embedding Adaptation with Set-to-Set Functionscited by 293
2018Fast Online Object Tracking and Segmentation: A Unifying Approachcited by 272
2020Prior Guided Feature Enrichment Network for Few-Shot Segmentationcited by 271
2017HPatches: A benchmark and evaluation of handcrafted and learned local descriptorscited by 261
2019Cross Attention Network for Few-shot Classificationcited by 218
2019CANet: Class-Agnostic Segmentation Networks with Iterative Refinement and Attentive Few-Shot Learningcited by 217
2015Learning Multi-Domain Convolutional Neural Networks for Visual Trackingcited by 209
2017Large Kernel Matters -- Improve Semantic Segmentation by Global Convolutional Networkcited by 208
2020Prototype Mixture Models for Few-shot Semantic Segmentationcited by 190
2019Feature Weighting and Boosting for Few-Shot Segmentationcited by 189
2020Modeling the Background for Incremental Learning in Semantic Segmentationcited by 189
2018Neighbourhood Consensus Networkscited by 173
2018SG-One: Similarity Guidance Network for One-Shot Semantic Segmentationcited by 169
2020Part-aware Prototype Network for Few-shot Semantic Segmentationcited by 162
2019FSS-1000: A 1000-Class Dataset for Few-Shot Segmentationcited by 160
2014Simultaneous Detection and Segmentationcited by 158
2020Weakly-Supervised Semantic Segmentation via Sub-category Explorationcited by 145
2020Mining Cross-Image Semantics for Weakly Supervised Semantic Segmentationcited by 144
2019SPair-71k: A Large-scale Benchmark for Semantic Correspondencecited by 140
2019GLU-Net: Global-Local Universal Network for Dense Flow and Correspondencescited by 139
2017Convolutional neural network architecture for geometric matchingcited by 138
2020Few-Shot Segmentation Without Meta-Learning: A Good Transductive Inference Is All You Need?cited by 135
2019Revisiting Local Descriptor based Image-to-Class Measure for Few-shot Learningcited by 121
2020CRNet: Cross-Reference Networks for Few-Shot Segmentationcited by 101
2019Infinite Mixture Prototypes for Few-Shot Learningcited by 99
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2020Dual-Resolution Correspondence Networkscited by 90
2019Transductive Episodic-Wise Adaptive Metric for Few-Shot Learningcited by 67

50 most cited of 69 All 69 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 17; 2022: 33; 2023: 50; 2024: 48; 2025: 27; 2026: 18.

193 papers.

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2022Language-driven Semantic Segmentationcited by 711
2021Image Segmentation Using Text and Image Promptscited by 509
2023Personalize Segment Anything Model with One Shotcited by 250
2023SegGPT: Segmenting Everything In Contextcited by 193
2022A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunitiescited by 162
2023Matcher: Segment Anything with One Shot Using All-Purpose Feature Matchingcited by 142
2021Few-Shot Segmentation via Cycle-Consistent Transformercited by 133
2022Learning What Not to Segment: A New Perspective on Few-Shot Segmentationcited by 120
2022Cost Aggregation with 4D Convolutional Swin Transformer for Few-Shot Segmentationcited by 114
2022Self-Support Few-Shot Semantic Segmentationcited by 96
2023Hierarchical Dense Correlation Distillation for Few-Shot Segmentationcited by 84
2021CATs: Cost Aggregation Transformers for Visual Correspondencecited by 77

38 more

2024Local All-Pair Correspondence for Point Trackingcited by 77
2021Relational Embedding for Few-Shot Classificationcited by 74
2022Dense Cross-Query-and-Support Attention Weighted Mask Aggregation for Few-Shot Segmentationcited by 65
2022Feature-Proxy Transformer for Few-Shot Segmentationcited by 58
2023Learning Mask-aware CLIP Representations for Zero-Shot Segmentationcited by 57
2022CATs++: Boosting Cost Aggregation with Convolutions and Transformerscited by 53
2022Singular Value Fine-tuning: Few-shot Segmentation requires Few-parameters Fine-tuningcited by 50
2022Correlation Verification for Image Retrievalcited by 47
2022Dynamic Prototype Convolution Network for Few-Shot Semantic Segmentationcited by 43
2023Exploring Effective Factors for Improving Visual In-Context Learningcited by 43
2024Multi-modal Instruction Tuned LLMs with Fine-grained Visual Perceptioncited by 39
2022Intermediate Prototype Mining Transformer for Few-Shot Semantic Segmentationcited by 37
2024Bridge the Points: Graph-based Few-shot Segment Anything Semanticallycited by 35
2022Neural Matching Fields: Implicit Representation of Matching Fields for Visual Correspondencecited by 32
2023Self-Calibrated Cross Attention Network for Few-Shot Segmentationcited by 32
2024Data-Efficient Operator Learning via Unsupervised Pretraining and In-Context Learningcited by 32
2021PFENet++: Boosting Few-shot Semantic Segmentation with the Noise-filtered Context-aware Prior Maskcited by 31
2021Novel Class Discovery in Semantic Segmentationcited by 31
2023Universal Few-shot Learning of Dense Prediction Tasks with Visual Token Matchingcited by 29
2022Peripheral Vision Transformercited by 27
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
2023Distilling Self-Supervised Vision Transformers for Weakly-Supervised Few-Shot Classification & Segmentationcited by 24
2022Doubly Deformable Aggregation of Covariance Matrices for Few-shot Segmentationcited by 23
2022Mask Matching Transformer for Few-Shot Segmentationcited by 22
2023FECANet: Boosting Few-Shot Semantic Segmentation with Feature-Enhanced Context-Aware Networkcited by 22
2023DifFSS: Diffusion Model for Few-Shot Semantic Segmentationcited by 22
2023Multi-Modal Prototypes for Open-World Semantic Segmentationcited by 22
2024Adapt Before Comparison: A New Perspective on Cross-Domain Few-Shot Segmentationcited by 22
2022MSANet: Multi-Similarity and Attention Guidance for Boosting Few-Shot Segmentationcited by 21
2023Learning Expressive Prompting With Residuals for Vision Transformerscited by 21
2024Multimodality Helps Few-shot 3D Point Cloud Semantic Segmentationcited by 20
2023Visual and Textual Prior Guided Mask Assemble for Few-Shot Segmentation and Beyondcited by 19
2023SEGIC: Unleashing the Emergent Correspondence for In-Context Segmentationcited by 19
2024A Simple Image Segmentation Framework via In-Context Examplescited by 19
2022MSI: Maximize Support-Set Information for Few-Shot Segmentationcited by 18
2021Dense Gaussian Processes for Few-Shot Segmentationcited by 17

50 most cited of 193 All 193 (JSON)