580 citations · 3.8k across the 56 of their papers we have counts for
13 papers · 1 filter
12-in-1: Multi-Task Vision and Language Representation Learning
Jiasen Lu, Vedanuj Goswami, Marcus Rohrbach +2
Much of vision-and-language research focuses on a small but diverse set of independent tasks and supporting datasets often studied in isolation; however, the visually-grounded lang…
Iterative Answer Prediction with Pointer-Augmented Multimodal Transformers for TextVQA
Ronghang Hu, Amanpreet Singh, Trevor Darrell +1
Many visual scenes contain text that carries crucial information, and it is thus essential to understand text in images for downstream reasoning tasks. For example, a deep water la…
Decoupling Representation and Classifier for Long-Tailed Recognition
Bingyi Kang, Saining Xie, Marcus Rohrbach +4
The long-tail distribution of the visual world poses great challenges for deep learning based classification models on how to handle the class imbalance problem. Existing solutions…
Uncertainty-guided Continual Learning with Bayesian Neural Networks
Sayna Ebrahimi, Mohamed Elhoseiny, Trevor Darrell +1
Continual learning aims to learn new tasks without forgetting previously learned ones. This is especially challenging when one cannot access data from previous tasks and when the m…
Learning to Generate Grounded Visual Captions without Localization Supervision
Chih-Yao Ma, Yannis Kalantidis, Ghassan AlRegib +3
When automatically generating a sentence description for an image or video, it often remains unclear how well the generated caption is grounded, that is whether the model uses the…
Towards VQA Models That Can Read
Amanpreet Singh, Vivek Natarajan, Meet Shah +5
Studies have shown that a dominant class of questions asked by visually impaired users on images of their surroundings involves reading text in the image. But today's VQA models ca…