[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

Zhang et al., 2024 - Google Patents

FSCIL-EACA: Few-Shot Class-Incremental Learning Network Based on Embedding Augmentation and Classifier Adaptation for Image Classification

Zhang et al., 2024

View PDF
Document ID
4328998969094465540
Author
Zhang R
Haihong E
Song M
Publication year
Publication venue
Chinese Journal of Electronics

External Links

Snippet

The ability to learn incrementally is critical to the long-term operation of AI systems. Benefiting from the power of few-shot class-incremental learning (FSCIL), deep learning models can continuously recognize new classes with only a few samples. The difficulty is …
Continue reading at ieeexplore.ieee.org (PDF) (other versions)

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6267—Classification techniques
    • G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
    • G06K9/46—Extraction of features or characteristics of the image
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • G06K9/6232—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods
    • G06K9/6247—Extracting features by transforming the feature space, e.g. multidimensional scaling; Mappings, e.g. subspace methods based on an approximation criterion, e.g. principal component analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • G06K9/6256—Obtaining sets of training patterns; Bootstrap methods, e.g. bagging, boosting
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6201—Matching; Proximity measures
    • G06K9/6202—Comparing pixel values or logical combinations thereof, or feature values having positional relevance, e.g. template matching
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/02—Computer systems based on biological models using neural network models
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass
    • G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computer systems utilising knowledge based models

Similar Documents

Publication Publication Date Title
Gao et al. A mutually supervised graph attention network for few-shot segmentation: The perspective of fully utilizing limited samples
Han et al. A survey on visual transformer
Han et al. A survey on vision transformer
Yang et al. Bi-directional generation for unsupervised domain adaptation
Liang et al. Clustseg: Clustering for universal segmentation
Mehralian et al. RDCGAN: Unsupervised representation learning with regularized deep convolutional generative adversarial networks
Zhang et al. Spatial context-aware object-attentional network for multi-label image classification
Zhang et al. FSCIL-EACA: Few-Shot Class-Incremental Learning Network Based on Embedding Augmentation and Classifier Adaptation for Image Classification
Cao et al. Adversarial dual-student with differentiable spatial warping for semi-supervised semantic segmentation
Zhao et al. Background-insensitive scene text recognition with text semantic segmentation
Liu et al. Multi-scale promoted self-adjusting correlation learning for facial action unit detection
Wang et al. Adversarial dense contrastive learning for semi-supervised semantic segmentation
Yang et al. A multi-domain and multi-modal representation disentangler for cross-domain image manipulation and classification
Sun et al. Appearance and geometry transformer for facial expression recognition in the wild
Li et al. Spatial-then-temporal self-supervised learning for video correspondence
Li et al. Lite-FENet: Lightweight multi-scale feature enrichment network for few-shot segmentation
Sebastian et al. Tied: A cycle consistent encoder-decoder model for text-to-image retrieval
Zhang et al. Cross-modal identity correlation mining for visible-thermal person re-identification
Zheng et al. BLAN: Bi-directional ladder attentive network for facial attribute prediction
Liu et al. Facial landmark detection using generative adversarial network combined with autoencoder for occlusion
Zhao et al. Large scale instance segmentation of outdoor environment based on improved YOLACT
Giakoumoglou et al. A review on discriminative self-supervised learning methods
Cheng et al. Stacked learning to search for scene labeling
Yu et al. UnifiedTT: Visual tracking with unified transformer
Lin et al. Decoupling foreground and background with Siamese ViT networks for weakly-supervised semantic segmentation