# Cooperative Bi-path Metric for Few-shot Learning

arXiv: https://arxiv.org/abs/2008.04031 · HTML: https://mlsanity.com/paper/2008.04031/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Aug 2020.

## Built on by (most cited first, 15 of 15)

- [MoLo: Motion-augmented Long-short Contrastive Learning for Few-shot Action Recognition](https://mlsanity.com/paper/2304.00946/) (2023)
- [SEGA: Semantic Guided Attention on Visual Prototype for Few-Shot Learning](https://mlsanity.com/paper/2111.04316/) (2021)
- [Federated Few-Shot Learning with Adversarial Learning](https://mlsanity.com/paper/2104.00365/) (2021)
- [Few-Shot Fine-Grained Action Recognition via Bidirectional Attention and Contrastive Meta-Learning](https://mlsanity.com/paper/2108.06647/) (2021)
- [Learning Unknowns from Unknowns: Diversified Negative Prototypes Generator for Few-Shot Open-Set Recognition](https://mlsanity.com/paper/2408.13373/) (2024)
- [Decoupling Template Bias in CLIP: Harnessing Empty Prompts for Enhanced Few-Shot Learning](https://mlsanity.com/paper/2512.08606/) (2025)
- [Few-shot Partial Multi-view Learning](https://mlsanity.com/paper/2105.02046/) (2021)
- [Rethinking the Metric in Few-shot Learning: From an Adaptive Multi-Distance Perspective](https://mlsanity.com/paper/2211.00890/) (2022)
- [Few-shot Classification via Ensemble Learning with Multi-Order Statistics](https://mlsanity.com/paper/2305.00454/) (2023)
- [Instant Response Few-shot Object Detection with Meta Strategy and Explicit Localization Inference](https://mlsanity.com/paper/2110.13377/) (2021)
- [Joint Image-Instance Spatial-Temporal Attention for Few-shot Action Recognition](https://mlsanity.com/paper/2503.14430/) (2025)
- [Controllable Relation Disentanglement for Few-Shot Class-Incremental Learning](https://mlsanity.com/paper/2403.11070/) (2024)
- [MICM: Rethinking Unsupervised Pretraining for Enhanced Few-shot Learning](https://mlsanity.com/paper/2408.13385/) (2024)
- [Few-shot Learning with Global Relatedness Decoupled-Distillation](https://mlsanity.com/paper/2107.05583/) (2021)
- [Otter: Mitigating Background Distractions of Wide-Angle Few-Shot Action Recognition with Enhanced RWKV](https://mlsanity.com/paper/2511.06741/) (2025)

## Abstract (arXiv)

Given base classes with sufficient labeled samples, the target of few-shot classification is to recognize unlabeled samples of novel classes with only a few labeled samples. Most existing methods only pay attention to the relationship between labeled and unlabeled samples of novel classes, which do not make full use of information within base classes. In this paper, we make two contributions to investigate the few-shot classification problem. First, we report a simple and effective baseline trained on base classes in the way of traditional supervised learning, which can achieve comparable results to the state of the art. Second, based on the baseline, we propose a cooperative bi-path metric for classification, which leverages the correlations between base classes and novel classes to further improve the accuracy. Experiments on two widely used benchmarks show that our method is a simple and effective framework, and a new state of the art is established in the few-shot classification field.