# Incorporating Intra-Class Variance to Fine-Grained Visual Recognition

arXiv: https://arxiv.org/abs/1703.00196 · HTML: https://mlsanity.com/paper/1703.00196/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Mar 2017.

## Built on by (most cited first, 11 of 11)

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- [Tackling Inter-Class Similarity and Intra-Class Variance for Microscopic Image-based Classification](https://mlsanity.com/paper/2109.11891/) (2021)
- [Inter-intra Variant Dual Representations forSelf-supervised Video Recognition](https://mlsanity.com/paper/2107.01194/) (2021)
- [Improving Deep Metric Learning by Divide and Conquer](https://mlsanity.com/paper/2109.04003/) (2021)
- [FedCCL: Federated Dual-Clustered Feature Contrast Under Domain Heterogeneity](https://mlsanity.com/paper/2404.09259/) (2024)
- [DeepSSN: a deep convolutional neural network to assess spatial scene similarity](https://mlsanity.com/paper/2202.04755/) (2022)
- [Intra-class Adaptive Augmentation with Neighbor Correction for Deep Metric Learning](https://mlsanity.com/paper/2211.16264/) (2022)
- [TransformMix: Learning Transformation and Mixing Strategies from Data](https://mlsanity.com/paper/2403.12429/) (2024)
- [Enhancing Face Recognition with Latent Space Data Augmentation and Facial Posture Reconstruction](https://mlsanity.com/paper/2301.11986/) (2023)

## Abstract (arXiv)

Fine-grained visual recognition aims to capture discriminative characteristics amongst visually similar categories. The state-of-the-art research work has significantly improved the fine-grained recognition performance by deep metric learning using triplet network. However, the impact of intra-category variance on the performance of recognition and robust feature representation has not been well studied. In this paper, we propose to leverage intra-class variance in metric learning of triplet network to improve the performance of fine-grained recognition. Through partitioning training images within each category into a few groups, we form the triplet samples across different categories as well as different groups, which is called Group Sensitive TRiplet Sampling (GS-TRS). Accordingly, the triplet loss function is strengthened by incorporating intra-class variance with GS-TRS, which may contribute to the optimization objective of triplet network. Extensive experiments over benchmark datasets CompCar and VehicleID show that the proposed GS-TRS has significantly outperformed state-of-the-art approaches in both classification and retrieval tasks.