%Aigaion2 BibTeX export from Idiap Publications %Sunday 13 October 2024 09:31:32 PM @INPROCEEDINGS{Lebret_ICML_2015, author = {Lebret, R{\'{e}}mi and Pinheiro, Pedro H. O. and Collobert, Ronan}, projects = {Idiap}, title = {Phrase-based Image Captioning}, booktitle = {International Conference on Machine Learning (ICML)}, volume = {37}, year = {2015}, pages = {2085–2094}, publisher = {JMLR}, location = {Lille, France}, url = {http://jmlr.org/proceedings/papers/v37/lebret15.html}, crossref = {Lebret_Idiap-RR-08-2015}, abstract = {Generating a novel textual description of an image is an interesting problem that connects computer vision and natural language processing. In this paper, we present a simple model that is able to generate descriptive sentences given a sample image. This model has a strong focus on the syntax of the descriptions. We train a purely bilinear model that learns a metric between an image representation (generated from a previously trained Convolutional Neural Network) and phrases that are used to described them. The system is then able to infer phrases from a given image sample. Based on caption syntax statistics, we propose a simple language model that can produce relevant descriptions for a given test image using the phrases inferred. Our approach, which is considerably simpler than state-of-the-art models, achieves comparable results in two popular datasets for the task: Flickr30k and the recently proposed Microsoft COCO.}, pdf = {https://publications.idiap.ch/attachments/papers/2015/Lebret_ICML_2015.pdf} } crossreferenced publications: @TECHREPORT{Lebret_Idiap-RR-08-2015, author = {Lebret, R{\'{e}}mi and Pinheiro, Pedro H. O. and Collobert, Ronan}, projects = {Idiap}, month = {5}, title = {Phrase-based Image Captioning}, type = {Idiap-RR}, number = {Idiap-RR-08-2015}, year = {2015}, institution = {Idiap}, note = {Under review by the International Conference on Machine Learning (ICML).}, abstract = {Generating a novel textual description of an image is an interesting problem that connects computer vision and natural language processing. In this paper, we present a simple model that is able to generate descriptive sentences given a sample image. This model has a strong focus on the syntax of the descriptions. We train a purely bilinear model that learns a metric between an image representation (generated from a previously trained Convolutional Neural Network) and phrases that are used to described them. The system is then able to infer phrases from a given image sample. Based on caption syntax statistics, we propose a simple language model that can produce relevant descriptions for a given test image using the phrases inferred. Our approach, which is considerably simpler than state-of-the-art models, achieves comparable results in two popular datasets for the task: Flickr30k and the recently proposed Microsoft COCO.}, pdf = {https://publications.idiap.ch/attachments/reports/2015/Lebret_Idiap-RR-08-2015.pdf} }