Papers › Fully Connected Deep Structured Networks

Fully Connected Deep Structured Networks

9 Mar 2015arXiv:1503.02351archive 2025-07-28

Alexander G. Schwing, Raquel Urtasun

Convolutional neural networks with many layers have recently been shown to achieve excellent results on many high-level tasks such as image classification, object detection and more recently also semantic segmentation. Particularly for semantic segmentation, a two-stage procedure is often employed. Hereby, convolutional networks are trained to provide good local pixel-wise features for the second step being traditionally a more global graphical model. In this work we unify this two-stage process into a single joint training algorithm. We demonstrate our method on the semantic image segmentation task and show encouraging results on the challenging PASCAL VOC 2012 dataset.

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Tasks

General Classification Image Classification Image Segmentation Lesion Segmentation Object Detection Segmentation Semantic Segmentation image-classification object-detection

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Lesion Segmentation	University of Waterloo skin cancer database	FCN-8s	Dice score	0.870 ±0.063	#3 of 5	Archive leaderboard	report

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