Papers › ELoPE: Fine-Grained Visual Classification with Efficient Localization, Pooling and Embedding

ELoPE: Fine-Grained Visual Classification with Efficient Localization, Pooling and Embedding

17 Nov 2019arXiv:1911.07344archive 2025-07-28

Harald Hanselmann, Hermann Ney

The task of fine-grained visual classification (FGVC) deals with classification problems that display a small inter-class variance such as distinguishing between different bird species or car models. State-of-the-art approaches typically tackle this problem by integrating an elaborate attention mechanism or (part-) localization method into a standard convolutional neural network (CNN). Also in this work the aim is to enhance the performance of a backbone CNN such as ResNet by including three efficient and lightweight components specifically designed for FGVC. This is achieved by using global k-max pooling, a discriminative embedding layer trained by optimizing class means and an efficient bounding box estimator that only needs class labels for training. The resulting model achieves new best state-of-the-art recognition accuracies on the Stanford cars and FGVC-Aircraft datasets.

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Code

rwth-i6/fgvc pytorch report

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Tasks

Fine-Grained Image Classification General Classification

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Fine-Grained Image Classification	FGVC Aircraft	ELoPE	Accuracy	93.5%	#22 of 57	Archive leaderboard	report
Fine-Grained Image Classification	Stanford Cars	ELoPE	Accuracy	95.0%	#24 of 83	Archive leaderboard	report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

1x1 Convolution Average Pooling Batch Normalization Bottleneck Residual Block Convolution Global Average Pooling Kaiming Initialization Max Pooling ReLU Residual Block Residual Connection

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