# Feedforward semantic segmentation with zoom-out features

Source: [Feedforward semantic segmentation with zoom-out features](<https://www.sota2.com/research/paper/68fa619e1e209131ce9f52ae>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T20:03:28.249Z

Authors: Mohammadreza Mostajabi, Payman Yadollahpour, Gregory Shakhnarovich

Published: 2014-12-02T03:31:51.000Z

## Abstract

We introduce a purely feed-forward architecture for semantic segmentation. We map small image elements (superpixels) to rich feature representations extracted from a sequence of nested regions of increasing extent. These regions are obtained by "zooming out" from the superpixel all the way to scene-level resolution. This approach exploits statistical structure in the image and in the label space without setting up explicit structured prediction mechanisms, and thus avoids complex and expensive inference. Instead superpixels are classified by a feedforward multilayer network. Our architecture achieves new state of the art performance in semantic segmentation, obtaining 64.4% average accuracy on the PASCAL VOC 2012 test set.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Semantic Segmentation on PASCAL VOC 2012 (test)](<https://www.sota2.com/research/sota/semantic-segmentation-on-pascal-voc-2012-test.md>): Semantic segmentation; PASCAL VOC 2012 (test); mIoU: 69.6.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1412.0774>)

[PDF](<https://arxiv.org/pdf/1412.0774.pdf>)