# Combining the Best of Graphical Models and ConvNets for Semantic Segmentation Source: [Combining the Best of Graphical Models and ConvNets for Semantic Segmentation]() Collection index: [llms.txt]() Data updated: 2025-10-25T20:03:28.249Z Authors: Michael Cogswell, Xiao Lin, Senthil Purushwalkam, Dhruv Batra Published: 2014-12-14T05:23:39.000Z ## Abstract We present a two-module approach to semantic segmentation that incorporates Convolutional Networks (CNNs) and Graphical Models. Graphical models are used to generate a small (5-30) set of diverse segmentations proposals, such that this set has high recall. Since the number of required proposals is so low, we can extract fairly complex features to rank them. Our complex feature of choice is a novel CNN called SegNet, which directly outputs a (coarse) semantic segmentation. Importantly, SegNet is specifically trained to optimize the corpus-level PASCAL IOU loss function. To the best of our knowledge, this is the first CNN specifically designed for semantic segmentation. This two-module approach achieves $52.5\\%$ on the PASCAL 2012 segmentation challenge. ## 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. ## Original sources and implementation [Original paper]() [PDF]()