Two-Stage Category-Guided Frequency Modulation for Few-Shot Semantic Segmentation
Source abstract
Abstract
Semantic segmentation of novel object categories with limited labeled data remains a challenging problem in computer vision. Few-shot segmentation methods aim to address this problem by recognizing objects from specific targe... | Find, read and cite all the research you need on Tech Science Press
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Geophysical Methods and Applications · Ultrasonics and Acoustic Wave Propagation · Optical Systems and Laser Technology
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (58)
Show source-provided references
1. Scene parsing through ADE20K dataset
2. Ren · Advances in Neural Information Processing Systems 28 (NIPS 2015) · 2015 · 91
3. Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, et al. An image is worth 16 x 16 words: transformers for image recognition at scale. arXiv: 2010.11929. 2020.
4. Xie · Advances in Neural Information Processing Systems 34 (NeurIPS 2021) · 2021 · 34 · 12077
5. SegGPT: towards segmenting everything in context
6. Semantic segmentation and YOLO detector over aerial vehicle images
7. Few-shot 3D point cloud semantic segmentation
8. Improving few-shot part segmentation using coarse supervision
9. Adaptive dynamic inference for few-shot left atrium segmentation10.1016/j.media.2024.103321 ↗
10. Hierarchical relational inference for few-shot learning in 3D left atrial segmentation10.1109/TETCI.2024.3377267 ↗
11. Shaban A, Bansal S, Liu Z, Essa I, Boots B. One-shot learning for semantic segmentation. arXiv:1709.03410. 2017.10.5244/C.31.167 ↗
12. PaNet: few-shot image semantic segmentation with prototype alignment
13. Prior guided feature enrichment network for few-shot segmentation10.1109/TPAMI.2020.3013717 ↗
14. Hierarchical dense correlation distillation for few-shot segmentation
15. Addressing background context bias in few-shot segmentation through iterative modulation
16. Part-aware prototype network for few-shot semantic segmentation
17. Prototype mixture models for few-shot semantic segmentation
18. Adaptive prototype learning and allocation for few-shot segmentation
19. Rethinking the correlation in few-shot segmentation: a buoys view
20. Few-shot semantic segmentation with democratic attention networks
21. Hypercorrelation squeeze for few-shot segmentation
22. Shi · Computer Vision—ECCV 2022 · 2022 · 15110.1007/978-3-031-20044-1_9 ↗
23. Few-shot segmentation via cycle-consistent transformer
24. FcaNet: frequency channel attention networks
25. SG-One: similarity guidance network for one-shot semantic segmentation10.1109/TCYB.2020.2992433 ↗
26. Self-guided and cross-guided learning for few-shot segmentation
27. Ouyang · Computer Vision—ECCV 2020 · 2020 · 76210.1007/978-3-030-58526-6_45 ↗
28. Zhu K, Zhai W, Zha ZJ, Cao Y. Self-supervised tuning for few-shot segmentation. arXiv:2004.05538. 2020.10.24963/ijcai.2020/142 ↗
29. Masksplit: self-supervised meta-learning for few-shot semantic segmentation
30. Self-supervised few-shot learning for semantic segmentation: an annotation-free approach
31. Baxes · Digital image processing: principles and applications · 1994
32. Pitas · Digital image processing algorithms and applications · 2000 · 2 · 133
33. FALCON: a fourier transform based approach for fast and secure convolutional neural network predictions
34. Yu · Computer Vision—ECCV 2022 · 2022 · 181
35. Spatial-frequency mutual learning for face super-resolution
36. Deep frequency filtering for domain generalization
37. Transformative noise reduction: leveraging a transformer-based deep network for medical image denoising10.3390/math12152313 ↗
38. FsaNet: frequency self-attention for semantic segmentation10.1109/TIP.2023.3305090 ↗
39. Head-free lightweight semantic segmentation with linear transformer
40. Learning transferable visual models from natural language supervision
41. Gonzalez · Digital image processing · 2009
42. FSS-1000: a 1000-class dataset for few-shot segmentation
43. The pascal visual object classes (VOC) challenge10.1007/s11263-009-0275-4 ↗
44. Hariharan · Computer Vision—ECCV 2014 · 2014 · 29710.1007/978-3-319-10584-0_20 ↗
45. Lin · Computer Vision—ECCV 2014 · 2014 · 74010.1007/978-3-319-10602-1_48 ↗
46. Feature weighting and boosting for few-shot segmentation
47. Dynamic prototype convolution network for few-shot semantic segmentation
48. Conditional networks for few-shot semantic segmentation
49. CaNet: class-agnostic segmentation networks with iterative refinement and attentive few-shot learning
50. Pyramid graph networks with connection attentions for region-based one-shot semantic segmentation
51. Relevant intrinsic feature enhancement network for few-shot semantic segmentation
52. NTRENet++: unleashing the power of non-target knowledge for few-shot semantic segmentation
53. Few-shot segmentation via divide-and-conquer proxies10.1007/s11263-023-01886-8 ↗
54. Adaptive agent transformer for few-shot segmentation
55. Learning what not to segment: a new perspective on few-shot segmentation
56. Few-shot semantic segmentation via perceptual attention and spatial control
57. Self-support few-shot semantic segmentation
58. Intermediate prototype mining transformer for few-shot semantic segmentation