# Seam Carving as Feature Pooling in CNN

arXiv: https://arxiv.org/abs/2409.06311 · HTML: https://mlsanity.com/paper/2409.06311/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 13 references that are in the map.
- First posted on arXiv 10 Sep 2024.

## Builds on (most cited first, 4 of 4)

- [Multi-Scale Context Aggregation by Dilated Convolutions](https://mlsanity.com/paper/1511.07122/) (2015)
- [Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition](https://mlsanity.com/paper/1406.4729/) (2014)
- [Fractional Max-Pooling](https://mlsanity.com/paper/1412.6071/) (2014)
- [Deep Generalized Max Pooling](https://mlsanity.com/paper/1908.05040/) (2019)

## Abstract (arXiv)

This work investigates the potential of seam carving as a feature pooling technique within Convolutional Neural Networks (CNNs) for image classification tasks. We propose replacing the traditional max pooling layer with a seam carving operation. Our experiments on the Caltech-UCSD Birds 200-2011 dataset demonstrate that the seam carving-based CNN achieves better performance compared to the model utilizing max pooling, based on metrics such as accuracy, precision, recall, and F1-score. We further analyze the behavior of both approaches through feature map visualizations, suggesting that seam carving might preserve more structural information during the pooling process. Additionally, we discuss the limitations of our approach and propose potential future directions for research.