Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Positive-First Most Ambiguous: A Simple Active Learning Criterion for Interactive Retrieval of Rare Categories

Kawtar Zaher, Olivier Buisson, Alexis Joly

Abstract

Real-world fine-grained visual retrieval often requires discovering a rare concept from large unlabeled collections with minimal supervision. This is especially critical in biodiversity monitoring, ecological studies, and long-tailed visual domains, where the target may represent only a tiny fraction of the data, creating highly imbalanced binary problems. Interactive retrieval with relevance feedback offers a practical solution: starting from a small query, the system selects candidates for binary user annotation and iteratively refines a lightweight classifier. While Active Learning (AL) is commonly used to guide selection, conventional AL assumes symmetric class priors and large annotation budgets, limiting effectiveness in imbalanced, low-budget, low-latency settings. We introduce Positive-First Most Ambiguous (PF-MA), a simple yet effective AL criterion that explicitly addresses the class imbalance asymmetry: it prioritizes near-boundary samples while favoring likely positives, enabling rapid discovery of subtle visual categories while maintaining informativeness. Unlike standard methods that oversample negatives, PF-MA consistently returns small batches with a high proportion of relevant samples, improving early retrieval and user satisfaction. To capture retrieval diversity, we also propose a class coverage metric that measures how well selected positives span the visual variability of the target class. Experiments on long-tailed datasets, including fine-grained botanical data, demonstrate that PF-MA consistently outperforms strong baselines in both coverage and classifier performance, across varying class sizes and descriptors. Our results highlight that aligning AL with the asymmetric and user-centric objectives of interactive fine-grained retrieval enables simple yet powerful solutions for retrieving rare and visually subtle categories in realistic human-in-the-loop settings.

Positive-First Most Ambiguous: A Simple Active Learning Criterion for Interactive Retrieval of Rare Categories

Abstract

Real-world fine-grained visual retrieval often requires discovering a rare concept from large unlabeled collections with minimal supervision. This is especially critical in biodiversity monitoring, ecological studies, and long-tailed visual domains, where the target may represent only a tiny fraction of the data, creating highly imbalanced binary problems. Interactive retrieval with relevance feedback offers a practical solution: starting from a small query, the system selects candidates for binary user annotation and iteratively refines a lightweight classifier. While Active Learning (AL) is commonly used to guide selection, conventional AL assumes symmetric class priors and large annotation budgets, limiting effectiveness in imbalanced, low-budget, low-latency settings. We introduce Positive-First Most Ambiguous (PF-MA), a simple yet effective AL criterion that explicitly addresses the class imbalance asymmetry: it prioritizes near-boundary samples while favoring likely positives, enabling rapid discovery of subtle visual categories while maintaining informativeness. Unlike standard methods that oversample negatives, PF-MA consistently returns small batches with a high proportion of relevant samples, improving early retrieval and user satisfaction. To capture retrieval diversity, we also propose a class coverage metric that measures how well selected positives span the visual variability of the target class. Experiments on long-tailed datasets, including fine-grained botanical data, demonstrate that PF-MA consistently outperforms strong baselines in both coverage and classifier performance, across varying class sizes and descriptors. Our results highlight that aligning AL with the asymmetric and user-centric objectives of interactive fine-grained retrieval enables simple yet powerful solutions for retrieving rare and visually subtle categories in realistic human-in-the-loop settings.

Paper Structure

This paper contains 37 sections, 2 equations, 10 figures, 8 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Interactive Retrieval and Relevance Feedback
  4. Human-in-the-Loop, Interactive Learning and Active Learning
  5. Class Imbalance in Active Learning
  6. Fine-Grained Recognition and Visual Similarity
  7. Summary
  8. Methodology
  9. Problem Formulation
  10. Active Sample Selection and Limitations
  11. Positive-First Most Ambiguous AL Strategy
  12. Assessing the diversity: Class Coverage Metric
  13. Experimental Framework
  14. Datasets
  15. Descriptor Models
  16. ...and 22 more sections

Figures (10)

  • Figure 1: Comparison of selected samples. MA (left): near-boundary negatives are oversampled. MP (middle): only positives far from the boundary are selected. PF-MA (right): balance between relevant positives and negatives around the boundary.
  • Figure 2: Interactive retrieval process.
  • Figure 3: Retrieval results for a bird species at iteration $5$ ($50$ total annotations). The initial query image is in blue, the positive images in green and the negative ones in red. Top: MA. Middle: MP. Bottom: PF-MA
  • Figure 4: Class coverage results per iteration.
  • Figure 5: Class coverage results at iteration $15$ per range of class size.
  • ...and 5 more figures