153 citations · 601 across the 67 of their papers we have counts for
14 papers · 1 filter
Mutual Exclusivity Training and Primitive Augmentation to Induce Compositionality
Yichen Jiang, Xiang Zhou, Mohit Bansal
Recent datasets expose the lack of the systematic generalization ability in standard sequence-to-sequence models. In this work, we analyze this behavior of seq2seq models and ident…
Perceiver-VL: Efficient Vision-and-Language Modeling with Iterative Latent Attention
Zineng Tang, Jaemin Cho, Jie Lei +1
We present Perceiver-VL, a vision-and-language framework that efficiently handles high-dimensional multimodal inputs such as long videos and text. Powered by the iterative latent c…
Are Hard Examples also Harder to Explain? A Study with Human and Model-Generated Explanations
Swarnadeep Saha, Peter Hase, Nazneen Rajani +1
Recent work on explainable NLP has shown that few-shot prompting can enable large pretrained language models (LLMs) to generate grammatical and factual natural language explanation…
Evaluating and Improving Factuality in Multimodal Abstractive Summarization
David Wan, Mohit Bansal
Current metrics for evaluating factuality for abstractive document summarization have achieved high correlations with human judgment, but they do not account for the vision modalit…
TVLT: Textless Vision-Language Transformer
Zineng Tang, Jaemin Cho, Yixin Nie +1
In this work, we present the Textless Vision-Language Transformer (TVLT), where homogeneous transformer blocks take raw visual and audio inputs for vision-and-language representati…
StoryDALL-E: Adapting Pretrained Text-to-Image Transformers for Story Continuation
Adyasha Maharana, Darryl Hannan, Mohit Bansal
Recent advances in text-to-image synthesis have led to large pretrained transformers with excellent capabilities to generate visualizations from a given text. However, these models…