Active Learning via Classifier Impact and Greedy Selection for Interactive Image Retrieval

• URL: http://arxiv.org/abs/2412.02310v1
• Date: Tue, 03 Dec 2024 09:27:46 GMT
• Title: Active Learning via Classifier Impact and Greedy Selection for Interactive Image Retrieval
• Authors: Leah Bar, Boaz Lerner, Nir Darshan, Rami Ben-Ari,
• Abstract summary: Active Learning (AL) is a user-interactive approach aimed at reducing annotation costs by selecting the most crucial examples to label. We introduce a novel batch-mode Active Learning framework named GAL (Greedy Active Learning) that better copes with this application.
• Score: 4.699825956909531
• License:
• Abstract: Active Learning (AL) is a user-interactive approach aimed at reducing annotation costs by selecting the most crucial examples to label. Although AL has been extensively studied for image classification tasks, the specific scenario of interactive image retrieval has received relatively little attention. This scenario presents unique characteristics, including an open-set and class-imbalanced binary classification, starting with very few labeled samples. We introduce a novel batch-mode Active Learning framework named GAL (Greedy Active Learning) that better copes with this application. It incorporates a new acquisition function for sample selection that measures the impact of each unlabeled sample on the classifier. We further embed this strategy in a greedy selection approach, better exploiting the samples within each batch. We evaluate our framework with both linear (SVM) and non-linear MLP/Gaussian Process classifiers. For the Gaussian Process case, we show a theoretical guarantee on the greedy approximation. Finally, we assess our performance for the interactive content-based image retrieval task on several benchmarks and demonstrate its superiority over existing approaches and common baselines. Code is available at https://github.com/barleah/GreedyAL.

Related papers

• Class Balance Matters to Active Class-Incremental Learning [61.11786214164405]
  We aim to start from a pool of large-scale unlabeled data and then annotate the most informative samples for incremental learning. We propose Class-Balanced Selection (CBS) strategy to achieve both class balance and informativeness in chosen samples. Our CBS can be plugged and played into those CIL methods which are based on pretrained models with prompts tunning technique.
  arXiv  Detail & Related papers  (2024-12-09T16:37:27Z)
• Advancing Image Retrieval with Few-Shot Learning and Relevance Feedback [5.770351255180495]
  Image Retrieval with Relevance Feedback (IRRF) involves iterative human interaction during the retrieval process. We propose a new scheme based on a hyper-network, that is tailored to the task and facilitates swift adjustment to user feedback. We show that our method can attain SoTA results in few-shot one-class classification and reach comparable results in binary classification task of few-shot open-set recognition.
  arXiv  Detail & Related papers  (2023-12-18T10:20:28Z)
• Two-Step Active Learning for Instance Segmentation with Uncertainty and Diversity Sampling [20.982992381790034]
  We propose a post-hoc active learning algorithm that integrates uncertainty-based sampling with diversity-based sampling. Our proposed algorithm is not only simple and easy to implement, but it also delivers superior performance on various datasets.
  arXiv  Detail & Related papers  (2023-09-28T03:40:30Z)
• Active Learning Principles for In-Context Learning with Large Language Models [65.09970281795769]
  This paper investigates how Active Learning algorithms can serve as effective demonstration selection methods for in-context learning. We show that in-context example selection through AL prioritizes high-quality examples that exhibit low uncertainty and bear similarity to the test examples.
  arXiv  Detail & Related papers  (2023-05-23T17:16:04Z)
• MoBYv2AL: Self-supervised Active Learning for Image Classification [57.4372176671293]
  We present MoBYv2AL, a novel self-supervised active learning framework for image classification. Our contribution lies in lifting MoBY, one of the most successful self-supervised learning algorithms, to the AL pipeline. We achieve state-of-the-art results when compared to recent AL methods.
  arXiv  Detail & Related papers  (2023-01-04T10:52:02Z)
• CAD: Co-Adapting Discriminative Features for Improved Few-Shot Classification [11.894289991529496]
  Few-shot classification is a challenging problem that aims to learn a model that can adapt to unseen classes given a few labeled samples. Recent approaches pre-train a feature extractor, and then fine-tune for episodic meta-learning. We propose a strategy to cross-attend and re-weight discriminative features for few-shot classification.
  arXiv  Detail & Related papers  (2022-03-25T06:14:51Z)
• A Lagrangian Duality Approach to Active Learning [119.36233726867992]
  We consider the batch active learning problem, where only a subset of the training data is labeled. We formulate the learning problem using constrained optimization, where each constraint bounds the performance of the model on labeled samples. We show, via numerical experiments, that our proposed approach performs similarly to or better than state-of-the-art active learning methods.
  arXiv  Detail & Related papers  (2022-02-08T19:18:49Z)
• Generative Multi-Label Zero-Shot Learning [136.17594611722285]
  Multi-label zero-shot learning strives to classify images into multiple unseen categories for which no data is available during training. Our work is the first to tackle the problem of multi-label feature in the (generalized) zero-shot setting. Our cross-level fusion-based generative approach outperforms the state-of-the-art on all three datasets.
  arXiv  Detail & Related papers  (2021-01-27T18:56:46Z)
• Deep Active Learning for Joint Classification & Segmentation with Weak Annotator [22.271760669551817]
  CNN visualization and interpretation methods, like class-activation maps (CAMs), are typically used to highlight the image regions linked to class predictions. We propose an active learning framework, which progressively integrates pixel-level annotations during training. Our results indicate that, by simply using random sample selection, the proposed approach can significantly outperform state-of-the-art CAMs and AL methods.
  arXiv  Detail & Related papers  (2020-10-10T03:25:54Z)
• SCAN: Learning to Classify Images without Labels [73.69513783788622]
  We advocate a two-step approach where feature learning and clustering are decoupled. A self-supervised task from representation learning is employed to obtain semantically meaningful features. We obtain promising results on ImageNet, and outperform several semi-supervised learning methods in the low-data regime.
  arXiv  Detail & Related papers  (2020-05-25T18:12:33Z)

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.