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A Closer Look at Few-shot Classification

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arxiv 1904.04232 v2 pith:OCZBWQUU submitted 2019-04-08 cs.CV

Wei-Yu Chen , Yen-Cheng Liu , Zsolt Kira , Yu-Chiang Frank Wang , Jia-Bin Huang This is my paper
classification cs.CV
keywords few-shotalgorithmsclassificationdifferenceswhenbackbonesbaselinecross-domain
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Few-shot classification aims to learn a classifier to recognize unseen classes during training with limited labeled examples. While significant progress has been made, the growing complexity of network designs, meta-learning algorithms, and differences in implementation details make a fair comparison difficult. In this paper, we present 1) a consistent comparative analysis of several representative few-shot classification algorithms, with results showing that deeper backbones significantly reduce the performance differences among methods on datasets with limited domain differences, 2) a modified baseline method that surprisingly achieves competitive performance when compared with the state-of-the-art on both the \miniI and the CUB datasets, and 3) a new experimental setting for evaluating the cross-domain generalization ability for few-shot classification algorithms. Our results reveal that reducing intra-class variation is an important factor when the feature backbone is shallow, but not as critical when using deeper backbones. In a realistic cross-domain evaluation setting, we show that a baseline method with a standard fine-tuning practice compares favorably against other state-of-the-art few-shot learning algorithms.

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Cited by 9 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. SpurAudio: A Benchmark for Studying Shortcut Learning in Few-Shot Audio Classification

    cs.CV 2026-05 unverdicted novelty 7.0 of 10

    SpurAudio benchmark shows state-of-the-art few-shot audio classifiers suffer large performance drops when background correlations are disrupted, even in large pretrained models.

  2. Rethinking Few Shot CLIP Benchmarks: A Critical Analysis in the Inductive Setting

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    Unlearning benchmark classes from CLIP creates a fairer few-shot test, on which most existing CLIP methods lose over half their accuracy, but the new method SEPRES remains strong.

  3. RelPrism: A Multi-Faceted Pre-training Framework with Self-Generated Tasks for Relational Databases

    cs.LG 2026-05 unverdicted novelty 6.0 of 10

    RelPrism generates self-supervised pseudo-tasks from three attribute perspectives via multi-granularity clustering to improve representation learning for relational database prediction tasks.

  4. Decomposing the Generalization Gap in PROTAC Activity Prediction: Variance Attribution and the Inter-Laboratory Ceiling

    cs.LG 2026-05 accept novelty 6.0 of 10

    Inter-laboratory measurement variance dominates the generalization gap in PROTAC activity prediction, capping LOTO AUROC near 0.67 across models and architectures.

  5. TAROT: Task-Adaptive Refinement of LLM-prior Graphs for Few-shot Tabular Learning

    cs.LG 2026-06 unverdicted novelty 5.0 of 10

    TAROT constructs and refines LLM-derived task-adaptive semantic graphs then applies GNN message passing to improve few-shot tabular prediction.

  6. Enhancing Few-Shot Classification of Benchmark and Disaster Imagery with ABHFA-Net

    cs.CV 2025-10 conditional novelty 5.0 of 10

    ABHFA-Net is a novel few-shot classification framework that models prototypes as distributions, applies spatial-channel attention, and uses Bhattacharyya-based contrastive loss, achieving state-of-the-art accuracies o...

  7. Language-Aware Information Maximization for Transductive Few-Shot CLIP

    cs.CV 2025-08 conditional novelty 5.0 of 10

    LIMO, a transductive loss combining mutual information, zero-shot KL regularization, and LoRA, sets new state-of-the-art few-shot accuracy for CLIP on 11 datasets.

  8. A Discrepancy-Based Perspective on Dataset Condensation

    cs.LG 2025-09 conditional novelty 4.0 of 10

    Dataset condensation is reframed as minimizing distribution discrepancies, and existing methods are sorted into a taxonomy; no new algorithm or experiments are provided.

  9. Object-Centric Cropping for Visual Few-Shot Classification

    cs.CV 2025-07 unverdicted novelty 4.0 of 10

    The supplied full text (arXiv:2508.00225) is a different paper from the claimed metadata (arXiv:2508.00218), so no claim about few-shot classification is backed by the manuscript.

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