Here you can find the code used for the training and experimental evaluation of the approach described in the paper "GMNet: Graph Matching Network for Large Scale Part Semantic Segmentation in the Wild".

Abstract

The semantic segmentation of parts of objects in the wild is a challenging task in which multiple instances of objects and multiple parts within those objects must be detected in the scene. This problem remains nowadays very marginally explored, despite its fundamental importance towards detailed object understanding. In this work, we propose a novel framework combining higher object-level context conditioning and part-level spatial relationships to address the task. To tackle object-level ambiguity, a class-conditioning module is introduced to retain class-level semantics when learning parts-level semantics. In this way, mid-level features carry also this information prior to the decoding stage. To tackle part-level ambiguity and localization we propose a novel adjacency graph-based module that aims at matching the relative spatial relationships between ground truth and predicted parts. The experimental evaluation on the Pascal-Part dataset shows that we achieve state-of-the-art results on this task.

 

The full paper can be downloaded from here

 

Code and Dataset

The code for the training and the evaluation of the proposed method is available on GitHub here.

The data and some checkpoints of interest can be found here.

Method

The overall architecture of the proposed approach is illustrated below.

 

The graph matching module is shown more in detail below.

 

Results

The main quantitative and qualitative results are reported in the following.

 

Contacts

For any information on the method you can contact lttm@dei.unipd.it

Have a look at our website http://lttm.dei.unipd.it for other works on this topic.

 

 

References

[1]   U. Michieli, E. Borsato, L. Rossi, P. Zanuttigh, "GMNet: Graph Matching Network for Large Scale Part Semantic Segmentation in the Wild", European Conference on Computer Vision (ECCV), 2020

 

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