Data-Free Class-Incremental Gesture Recognition with Prototype-Guided Pseudo Feature Replay

• URL: http://arxiv.org/abs/2505.20049v1
• Date: Mon, 26 May 2025 14:37:35 GMT
• Title: Data-Free Class-Incremental Gesture Recognition with Prototype-Guided Pseudo Feature Replay
• Authors: Hongsong Wang, Ao Sun, Jie Gui, Liang Wang,
• Abstract summary: We introduce a Prototype-Guided Pseudo Feature Replay (PGPFR) framework for data-free class-incremental gesture recognition.<n>This framework comprises four components: Pseudo Feature Generation with Batch Prototypes (PFGBP), Variational Prototype Replay (VPR) for old classes, and Continual Re-Training ( CCRT)
• Score: 17.780573542052434
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Gesture recognition is an important research area in the field of computer vision. Most gesture recognition efforts focus on close-set scenarios, thereby limiting the capacity to effectively handle unseen or novel gestures. We aim to address class-incremental gesture recognition, which entails the ability to accommodate new and previously unseen gestures over time. Specifically, we introduce a Prototype-Guided Pseudo Feature Replay (PGPFR) framework for data-free class-incremental gesture recognition. This framework comprises four components: Pseudo Feature Generation with Batch Prototypes (PFGBP), Variational Prototype Replay (VPR) for old classes, Truncated Cross-Entropy (TCE) for new classes, and Continual Classifier Re-Training (CCRT). To tackle the issue of catastrophic forgetting, the PFGBP dynamically generates a diversity of pseudo features in an online manner, leveraging class prototypes of old classes along with batch class prototypes of new classes. Furthermore, the VPR enforces consistency between the classifier's weights and the prototypes of old classes, leveraging class prototypes and covariance matrices to enhance robustness and generalization capabilities. The TCE mitigates the impact of domain differences of the classifier caused by pseudo features. Finally, the CCRT training strategy is designed to prevent overfitting to new classes and ensure the stability of features extracted from old classes. Extensive experiments conducted on two widely used gesture recognition datasets, namely SHREC 2017 3D and EgoGesture 3D, demonstrate that our approach outperforms existing state-of-the-art methods by 11.8\% and 12.8\% in terms of mean global accuracy, respectively. The code is available on https://github.com/sunao-101/PGPFR-3/.

Related papers

• Proto-FG3D: Prototype-based Interpretable Fine-Grained 3D Shape Classification [59.68055837500357]
  We propose the first prototype-based framework named Proto-FG3D for fine-grained 3D shape classification.<n>Proto-FG3D establishes joint multi-view and multi-category representation learning via Prototype Association.<n>Proto-FG3D surpasses state-of-the-art methods in accuracy, transparent predictions, and ad-hoc interpretability with visualizations.
  arXiv  Detail & Related papers  (2025-05-23T09:31:02Z)
• Efficient Non-Exemplar Class-Incremental Learning with Retrospective Feature Synthesis [21.348252135252412]
  Current Non-Exemplar Class-Incremental Learning (NECIL) methods mitigate forgetting by storing a single prototype per class. We propose a more efficient NECIL method that replaces prototypes with synthesized retrospective features for old classes. Our method significantly improves the efficiency of non-exemplar class-incremental learning and achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2024-11-03T07:19:11Z)
• Queryable Prototype Multiple Instance Learning with Vision-Language Models for Incremental Whole Slide Image Classification [10.667645628712542]
  Whole Slide Image (WSI) classification has very significant applications in clinical pathology.<n>This paper proposes the first Vision-Language-based framework with Queryable Prototype Multiple Instance Learning (QPMIL-VL) specially designed for incremental WSI classification.
  arXiv  Detail & Related papers  (2024-10-14T14:49:34Z)
• Continual Gesture Learning without Data via Synthetic Feature Sampling [10.598646625077025]
  DFCIL aims to enable models to continuously learn new classes while retraining knowledge of old classes, even when the training data for old classes is unavailable.<n>We developed Synthetic Feature Replay (SFR) that can sample synthetic features from class prototypes to replay for old classes and augment for new classes.<n>Our proposed method showcases significant advancements over the state-of-the-art, achieving up to 15% enhancements in mean accuracy across all steps.
  arXiv  Detail & Related papers  (2024-08-21T18:44:15Z)
• Learning Prompt with Distribution-Based Feature Replay for Few-Shot Class-Incremental Learning [56.29097276129473]
  We propose a simple yet effective framework, named Learning Prompt with Distribution-based Feature Replay (LP-DiF)<n>To prevent the learnable prompt from forgetting old knowledge in the new session, we propose a pseudo-feature replay approach.<n>When progressing to a new session, pseudo-features are sampled from old-class distributions combined with training images of the current session to optimize the prompt.
  arXiv  Detail & Related papers  (2024-01-03T07:59:17Z)
• Few-Shot Class-Incremental Learning via Training-Free Prototype Calibration [67.69532794049445]
  We find a tendency for existing methods to misclassify the samples of new classes into base classes, which leads to the poor performance of new classes. We propose a simple yet effective Training-frEE calibratioN (TEEN) strategy to enhance the discriminability of new classes.
  arXiv  Detail & Related papers  (2023-12-08T18:24:08Z)
• Rethinking Person Re-identification from a Projection-on-Prototypes Perspective [84.24742313520811]
  Person Re-IDentification (Re-ID) as a retrieval task, has achieved tremendous development over the past decade. We propose a new baseline ProNet, which innovatively reserves the function of the classifier at the inference stage. Experiments on four benchmarks demonstrate that our proposed ProNet is simple yet effective, and significantly beats previous baselines.
  arXiv  Detail & Related papers  (2023-08-21T13:38:10Z)
• Class Incremental Learning with Self-Supervised Pre-Training and Prototype Learning [21.901331484173944]
  We analyze the causes of catastrophic forgetting in class incremental learning. We propose a two-stage learning framework with a fixed encoder and an incrementally updated prototype classifier. Our method does not rely on preserved samples of old classes, is thus a non-exemplar based CIL method.
  arXiv  Detail & Related papers  (2023-08-04T14:20:42Z)
• Neural Collapse Inspired Feature-Classifier Alignment for Few-Shot Class Incremental Learning [120.53458753007851]
  Few-shot class-incremental learning (FSCIL) has been a challenging problem as only a few training samples are accessible for each novel class in the new sessions. We deal with this misalignment dilemma in FSCIL inspired by the recently discovered phenomenon named neural collapse. We propose a neural collapse inspired framework for FSCIL. Experiments on the miniImageNet, CUB-200, and CIFAR-100 datasets demonstrate that our proposed framework outperforms the state-of-the-art performances.
  arXiv  Detail & Related papers  (2023-02-06T18:39:40Z)
• Dual Prototypical Contrastive Learning for Few-shot Semantic Segmentation [55.339405417090084]
  We propose a dual prototypical contrastive learning approach tailored to the few-shot semantic segmentation (FSS) task. The main idea is to encourage the prototypes more discriminative by increasing inter-class distance while reducing intra-class distance in prototype feature space. We demonstrate that the proposed dual contrastive learning approach outperforms state-of-the-art FSS methods on PASCAL-5i and COCO-20i datasets.
  arXiv  Detail & Related papers  (2021-11-09T08:14:50Z)
• Few Shot Activity Recognition Using Variational Inference [9.371378627575883]
  We propose a novel variational inference based architectural framework (HF-AR) for few shot activity recognition. Our framework leverages volume-preserving Householder Flow to learn a flexible posterior distribution of the novel classes. This results in better performance as compared to state-of-the-art few shot approaches for human activity recognition.
  arXiv  Detail & Related papers  (2021-08-20T03:57:58Z)
• Continual Semantic Segmentation via Repulsion-Attraction of Sparse and Disentangled Latent Representations [18.655840060559168]
  This paper focuses on class incremental continual learning in semantic segmentation. New categories are made available over time while previous training data is not retained. The proposed continual learning scheme shapes the latent space to reduce forgetting whilst improving the recognition of novel classes.
  arXiv  Detail & Related papers  (2021-03-10T21:02:05Z)

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.