55 citations · 138 across the 31 of their papers we have counts for
6 papers · 1 filter
A Study on the Autoregressive and non-Autoregressive Multi-label Learning
Elham J. Barezi, Iacer Calixto, Kyunghyun Cho +1
Extreme classification tasks are multi-label tasks with an extremely large number of labels (tags). These tasks are hard because the label space is usually (i) very large, e.g. tho…
Seeing past words: Testing the cross-modal capabilities of pretrained V&L models on counting tasks
Letitia Parcalabescu, Albert Gatt, Anette Frank +1
We investigate the reasoning ability of pretrained vision and language (V&L) models in two tasks that require multimodal integration: (1) discriminating a correct image-sentence pa…
Are scene graphs good enough to improve Image Captioning?
Victor Milewski, Marie-Francine Moens, Iacer Calixto
Many top-performing image captioning models rely solely on object features computed with an object detection model to generate image descriptions. However, recent studies propose t…
ImagiFilter: A resource to enable the semi-automatic mining of images at scale
Houda Alberts, Iacer Calixto
Datasets (semi-)automatically collected from the web can easily scale to millions of entries, but a dataset's usefulness is directly related to how clean and high-quality its examp…
VisualSem: A High-quality Knowledge Graph for Vision and Language
Houda Alberts, Teresa Huang, Yash Deshpande +4
An exciting frontier in natural language understanding (NLU) and generation (NLG) calls for (vision-and-) language models that can efficiently access external structured knowledge…
English Intermediate-Task Training Improves Zero-Shot Cross-Lingual Transfer Too
Jason Phang, Iacer Calixto, Phu Mon Htut +5
Intermediate-task training---fine-tuning a pretrained model on an intermediate task before fine-tuning again on the target task---often improves model performance substantially on…