Iacer Calixto

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NYU college ∙ University of Amsterdam ∙ ADAPT Centre

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Seeing past words: Testing the cross-modal capabilities of pretrained V L models

We investigate the ability of general-purpose pretrained vision and lang...
12/22/2020 ∙ by Letitia Parcalabescu, et al. ∙ 11 ∙ share

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A Study on the Autoregressive and non-Autoregressive Multi-label Learning

Extreme classification tasks are multi-label tasks with an extremely lar...
12/03/2020 ∙ by Elham J. Barezi, et al. ∙ 16 ∙ share

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Are scene graphs good enough to improve Image Captioning?

Many top-performing image captioning models rely solely on object featur...
09/25/2020 ∙ by Victor Milewski, et al. ∙ 0 ∙ share

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ImagiFilter: A resource to enable the semi-automatic mining of images at scale

Datasets (semi-)automatically collected from the web can easily scale to...
08/20/2020 ∙ by Houda Alberts, et al. ∙ 0 ∙ share

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VisualSem: a high-quality knowledge graph for vision and language

We argue that the next frontier in natural language understanding (NLU) ...
08/20/2020 ∙ by Houda Alberts, et al. ∙ 30 ∙ share

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English Intermediate-Task Training Improves Zero-Shot Cross-Lingual Transfer Too

Intermediate-task training has been shown to substantially improve pretr...
05/26/2020 ∙ by Jason Phang, et al. ∙ 0 ∙ share

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Latent Visual Cues for Neural Machine Translation

In this work, we propose to model the interaction between visual and tex...
11/01/2018 ∙ by Iacer Calixto, et al. ∙ 0 ∙ share

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Doubly-Attentive Decoder for Multi-modal Neural Machine Translation

We introduce a Multi-modal Neural Machine Translation model in which a d...
02/04/2017 ∙ by Iacer Calixto, et al. ∙ 0 ∙ share

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Multilingual Multi-modal Embeddings for Natural Language Processing

We propose a novel discriminative model that learns embeddings from mult...
02/03/2017 ∙ by Iacer Calixto, et al. ∙ 0 ∙ share

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Incorporating Global Visual Features into Attention-Based Neural Machine Translation

We introduce multi-modal, attention-based neural machine translation (NM...
01/23/2017 ∙ by Iacer Calixto, et al. ∙ 0 ∙ share

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