Advancing Image Retrieval with Few-Shot Learning and Relevance Feedback
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Abstract (arXiv)
With such a massive growth in the number of images stored, efficient search in a database has become a crucial endeavor managed by image retrieval systems. Image Retrieval with Relevance Feedback (IRRF) involves iterative human interaction during the retrieval process, yielding more meaningful outcomes. This process can be generally cast as a binary classification problem with only it few labeled samples derived from user feedback. The IRRF task frames a unique few-shot learning characteristics including binary classification of imbalanced and asymmetric classes, all in an open-set regime. In this paper, we study this task through the lens of few-shot learning methods. We propose a new scheme based on a hyper-network, that is tailored to the task and facilitates swift adjustment to user feedback. Our approach's efficacy is validated through comprehensive evaluations on multiple benchmarks and two supplementary tasks, supported by theoretical analysis. We demonstrate the advantage of our model over strong baselines on 4 different datasets in IRRF, addressing also retrieval of images with multiple objects. Furthermore, 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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 24 of its 47 references that are in the map. First posted on arXiv 18 Dec 2023.
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24 of 47 references.
References · 24
- 2015Deep Residual Learning for Image Recognitioncited by 52,096
- 2014Adam: A Method for Stochastic Optimizationcited by 37,240
- 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,897
- 2017Prototypical Networks for Few-shot Learningcited by 3,015
- 2016Matching Networks for One Shot Learningcited by 2,255
- 2019A Closer Look at Few-shot Classificationcited by 684
- 2021ImageNet-21K Pretraining for the Massescited by 613
- 2019Meta-Learning with Differentiable Convex Optimizationcited by 461
- 2020Rethinking Few-Shot Image Classification: a Good Embedding Is All You Need?cited by 437
- 2018How to train your MAMLcited by 309
- 2019A Baseline for Few-Shot Image Classificationcited by 298
- 2018Few-Shot Learning via Embedding Adaptation with Set-to-Set Functionscited by 294
12 more
- 2019Few-Shot Object Detection with Attention-RPN and Multi-Relation Detectorcited by 206
- 2018Revisiting Oxford and Paris: Large-Scale Image Retrieval Benchmarkingcited by 191
- 2020Meta-Baseline: Exploring Simple Meta-Learning for Few-Shot Learningcited by 179
- 2022Pushing the Limits of Simple Pipelines for Few-Shot Learning: External Data and Fine-Tuning Make a Differencecited by 126
- 2020Embedding Propagation: Smoother Manifold for Few-Shot Classificationcited by 80
- 2020learn2learn: A Library for Meta-Learning Researchcited by 69
- 2020Few-Shot Open-Set Recognition using Meta-Learningcited by 50
- 2021Few-shot Image Classification: Just Use a Library of Pre-trained Feature Extractors and a Simple Classifiercited by 32
- 2021Few-shot Open-set Recognition by Transformation Consistencycited by 29
- 2020Few-Shot One-Class Classification via Meta-Learningcited by 17
- 2018Information-Theoretic Active Learning for Content-Based Image Retrievalcited by 5
- 2018Fast Interactive Image Retrieval using large-scale unlabeled datacited by 2
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