Learning Relationship-Aware Visual Features
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Relational reasoning in Computer Vision has recently shown impressive results on visual question answering tasks. On the challenging dataset called CLEVR, the recently proposed Relation Network (RN), a simple plug-and-play module and one of the state-of-the-art approaches, has obtained a very good accuracy (95.5%) answering relational questions. In this paper, we define a sub-field of Content-Based Image Retrieval (CBIR) called Relational-CBIR (R-CBIR), in which we are interested in retrieving images with given relationships among objects. To this aim, we employ the RN architecture in order to extract relation-aware features from CLEVR images. To prove the effectiveness of these features, we extended both CLEVR and Sort-of-CLEVR datasets generating a ground-truth for R-CBIR by exploiting relational data embedded into scene-graphs. Furthermore, we propose a modification of the RN module – a two-stage Relation Network (2S-RN) – that enabled us to extract relation-aware features by using a preprocessing stage able to focus on the image content, leaving the question apart. Experiments show that our RN features, especially the 2S-RN ones, outperform the RMAC state-of-the-art features on this new challenging task.
KeywordsCLEVR Content-based image retrieval Deep learning Relational reasoning Relation networks Deep features
This work was partially supported by Smart News, Social sensing for breaking news, co-founded by the Tuscany region under the FAR-FAS 2014 program, CUP CIPE D58C15000270008, and Automatic Data and documents Analysis to enhance human-based processes (ADA), CUP CIPE D55F17000290009.
We are very grateful to the DeepMind team (Santoro et al.), that kindly assisted us during the replication of their work on Relation Networks.
We also gratefully acknowledge the support of NVIDIA Corporation with the donation of the Tesla K40 GPU used for this research.
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