243 citations · 409 across the 7 of their papers we have counts for
15 papers
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…
Contrastive Examples for Addressing the Tyranny of the Majority
Viktoriia Sharmanska, Lisa Anne Hendricks, Trevor Darrell +1
Computer vision algorithms, e.g. for face recognition, favour groups of individuals that are better represented in the training data. This happens because of the generalization tha…
Localizing Moments in Video with Temporal Language
Lisa Anne Hendricks, Oliver Wang, Eli Shechtman +3
Localizing moments in a longer video via natural language queries is a new, challenging task at the intersection of language and video understanding. Though moment localization wit…
Object Hallucination in Image Captioning
Anna Rohrbach, Lisa Anne Hendricks, Kaylee Burns +2
Despite continuously improving performance, contemporary image captioning models are prone to "hallucinating" objects that are not actually in a scene. One problem is that standard…