Calibrating Higher-Order Statistics for Few-Shot Class-Incremental Learning with Pre-trained Vision Transformers

• URL: http://arxiv.org/abs/2404.06622v1
• Date: Tue, 9 Apr 2024 21:12:31 GMT
• Title: Calibrating Higher-Order Statistics for Few-Shot Class-Incremental Learning with Pre-trained Vision Transformers
• Authors: Dipam Goswami, Bartłomiej Twardowski, Joost van de Weijer,
• Abstract summary: Few-shot class-incremental learning (FSCIL) aims to adapt the model to new classes from very few data (5 samples) without forgetting the previously learned classes. Recent works in many-shot CIL (MSCIL) exploited pre-trained models to reduce forgetting and achieve better plasticity. We use ViT models pre-trained on large-scale datasets for few-shot settings, which face the critical issue of low plasticity.
• Score: 12.590571371294729
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Few-shot class-incremental learning (FSCIL) aims to adapt the model to new classes from very few data (5 samples) without forgetting the previously learned classes. Recent works in many-shot CIL (MSCIL) (using all available training data) exploited pre-trained models to reduce forgetting and achieve better plasticity. In a similar fashion, we use ViT models pre-trained on large-scale datasets for few-shot settings, which face the critical issue of low plasticity. FSCIL methods start with a many-shot first task to learn a very good feature extractor and then move to the few-shot setting from the second task onwards. While the focus of most recent studies is on how to learn the many-shot first task so that the model generalizes to all future few-shot tasks, we explore in this work how to better model the few-shot data using pre-trained models, irrespective of how the first task is trained. Inspired by recent works in MSCIL, we explore how using higher-order feature statistics can influence the classification of few-shot classes. We identify the main challenge of obtaining a good covariance matrix from few-shot data and propose to calibrate the covariance matrix for new classes based on semantic similarity to the many-shot base classes. Using the calibrated feature statistics in combination with existing methods significantly improves few-shot continual classification on several FSCIL benchmarks. Code is available at https://github.com/dipamgoswami/FSCIL-Calibration.

Related papers

• Lightweight Conditional Model Extrapolation for Streaming Data under Class-Prior Shift [27.806085423595334]
  We introduce LIMES, a new method for learning with non-stationary streaming data. We learn a single set of model parameters from which a specific classifier for any specific data distribution is derived. Experiments on a set of exemplary tasks using Twitter data show that LIMES achieves higher accuracy than alternative approaches.
  arXiv  Detail & Related papers  (2022-06-10T15:19:52Z)
• Revisiting the Updates of a Pre-trained Model for Few-shot Learning [11.871523410051527]
  We compare the two popular updating methods, fine-tuning and linear probing. We find that fine-tuning is better than linear probing as the number of samples increases.
  arXiv  Detail & Related papers  (2022-05-13T08:47:06Z)
• Class-Incremental Learning with Strong Pre-trained Models [97.84755144148535]
  Class-incremental learning (CIL) has been widely studied under the setting of starting from a small number of classes (base classes) We explore an understudied real-world setting of CIL that starts with a strong model pre-trained on a large number of base classes. Our proposed method is robust and generalizes to all analyzed CIL settings.
  arXiv  Detail & Related papers  (2022-04-07T17:58:07Z)
• Few-Shot Class-Incremental Learning by Sampling Multi-Phase Tasks [59.12108527904171]
  A model should recognize new classes and maintain discriminability over old classes. The task of recognizing few-shot new classes without forgetting old classes is called few-shot class-incremental learning (FSCIL) We propose a new paradigm for FSCIL based on meta-learning by LearnIng Multi-phase Incremental Tasks (LIMIT)
  arXiv  Detail & Related papers  (2022-03-31T13:46:41Z)
• Model-Agnostic Multitask Fine-tuning for Few-shot Vision-Language Transfer Learning [59.38343286807997]
  We propose Model-Agnostic Multitask Fine-tuning (MAMF) for vision-language models on unseen tasks. Compared with model-agnostic meta-learning (MAML), MAMF discards the bi-level optimization and uses only first-order gradients. We show that MAMF consistently outperforms the classical fine-tuning method for few-shot transfer learning on five benchmark datasets.
  arXiv  Detail & Related papers  (2022-03-09T17:26:53Z)
• Improving Calibration for Long-Tailed Recognition [68.32848696795519]
  We propose two methods to improve calibration and performance in such scenarios. For dataset bias due to different samplers, we propose shifted batch normalization. Our proposed methods set new records on multiple popular long-tailed recognition benchmark datasets.
  arXiv  Detail & Related papers  (2021-04-01T13:55:21Z)
• Few-shot Weakly-Supervised Object Detection via Directional Statistics [55.97230224399744]
  We propose a probabilistic multiple instance learning approach for few-shot Common Object Localization (COL) and few-shot Weakly Supervised Object Detection (WSOD) Our model simultaneously learns the distribution of the novel objects and localizes them via expectation-maximization steps. Our experiments show that the proposed method, despite being simple, outperforms strong baselines in few-shot COL and WSOD, as well as large-scale WSOD tasks.
  arXiv  Detail & Related papers  (2021-03-25T22:34:16Z)
• Few-Shot Lifelong Learning [35.05196800623617]
  Few-Shot Lifelong Learning enables deep learning models to perform lifelong/continual learning on few-shot data. Our method selects very few parameters from the model for training every new set of classes instead of training the full model. We experimentally show that our method significantly outperforms existing methods on the miniImageNet, CIFAR-100, and CUB-200 datasets.
  arXiv  Detail & Related papers  (2021-03-01T13:26:57Z)
• A Primal-Dual Subgradient Approachfor Fair Meta Learning [23.65344558042896]
  Few shot meta-learning is well-known with its fast-adapted capability and accuracy generalization onto unseen tasks. We propose a Primal-Dual Fair Meta-learning framework, namely PDFM, which learns to train fair machine learning models using only a few examples.
  arXiv  Detail & Related papers  (2020-09-26T19:47:38Z)
• Few-Shot Image Classification via Contrastive Self-Supervised Learning [5.878021051195956]
  We propose a new paradigm of unsupervised few-shot learning to repair the deficiencies. We solve the few-shot tasks in two phases: meta-training a transferable feature extractor via contrastive self-supervised learning. Our method achieves state of-the-art performance in a variety of established few-shot tasks on the standard few-shot visual classification datasets.
  arXiv  Detail & Related papers  (2020-08-23T02:24:31Z)
• Few-Shot Learning with Intra-Class Knowledge Transfer [100.87659529592223]
  We consider the few-shot classification task with an unbalanced dataset. Recent works have proposed to solve this task by augmenting the training data of the few-shot classes using generative models. We propose to leverage the intra-class knowledge from the neighbor many-shot classes with the intuition that neighbor classes share similar statistical information.
  arXiv  Detail & Related papers  (2020-08-22T18:15:38Z)

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.