243 citations · 409 across the 8 of their papers we have counts for
7 papers · 1 filter
Measuring Progress in Fine-grained Vision-and-Language Understanding
Emanuele Bugliarello, Laurent Sartran, Aishwarya Agrawal +2
While pretraining on large-scale image-text data from the Web has facilitated rapid progress on many vision-and-language (V&L) tasks, recent work has demonstrated that pretrained m…
Weakly-Supervised Learning of Visual Relations in Multimodal Pretraining
Emanuele Bugliarello, Aida Nematzadeh, Lisa Anne Hendricks
Recent work in vision-and-language pretraining has investigated supervised signals from object detection data to learn better, fine-grained multimodal representations. In this work…
Scaling Language Models: Methods, Analysis & Insights from Training Gopher
Jack W. Rae, Sebastian Borgeaud, Trevor Cai +77
Language modelling provides a step towards intelligent communication systems by harnessing large repositories of written human knowledge to better predict and understand the world.…
Challenges in Detoxifying Language Models
Johannes Welbl, Amelia Glaese, Jonathan Uesato +7
Large language models (LM) generate remarkably fluent text and can be efficiently adapted across NLP tasks. Measuring and guaranteeing the quality of generated text in terms of saf…
Probing Image-Language Transformers for Verb Understanding
Lisa Anne Hendricks, Aida Nematzadeh
Multimodal image-language transformers have achieved impressive results on a variety of tasks that rely on fine-tuning (e.g., visual question answering and image retrieval). We are…
Decoupling the Role of Data, Attention, and Losses in Multimodal Transformers
Lisa Anne Hendricks, John Mellor, Rosalia Schneider +2
Recently multimodal transformer models have gained popularity because their performance on language and vision tasks suggest they learn rich visual-linguistic representations. Focu…