Tagging Like Humans: Diverse and Distinct Image Annotation

Baoyuan Wu, Weidong Chen, Peng Sun, Wei Liu, Bernard Ghanem, Siwei Lyu; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018, pp. 7967-7975


In this work we propose a new automatic image annotation model, dubbed diverse and distinct image annotation (D2IA). The generative model D2IA is inspired by the ensemble of human annotations, which create semantically relevant, yet distinct and diverse tags. In D2IA, we generate a relevant and distinct tag subset, in which the tags are relevant to the image contents and semantically distinct to each other, using sequential sampling from a determinantal point process (DPP) model. Multiple such tag subsets that cover diverse semantic aspects or diverse semantic levels of the image contents are generated by randomly perturbing the DPP sampling process. We leverage a generative adversarial network (GAN) model to train D2IA. We perform extensive experiments including quantitative and qualitative comparisons, as well as human subject studies, on two benchmark datasets to demonstrate that the proposed model can produce more diverse and distinct tags than the state-of-the-arts.

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[pdf] [Supp] [arXiv]
author = {Wu, Baoyuan and Chen, Weidong and Sun, Peng and Liu, Wei and Ghanem, Bernard and Lyu, Siwei},
title = {Tagging Like Humans: Diverse and Distinct Image Annotation},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2018}