580 citations · 3.8k across the 56 of their papers we have counts for
57 papers · 1 filter
Improving Selective Visual Question Answering by Learning from Your Peers
Corentin Dancette, Spencer Whitehead, Rishabh Maheshwary +5
Despite advances in Visual Question Answering (VQA), the ability of models to assess their own correctness remains underexplored. Recent work has shown that VQA models, out-of-the-…
Simple Token-Level Confidence Improves Caption Correctness
Suzanne Petryk, Spencer Whitehead, Joseph E. Gonzalez +3
The ability to judge whether a caption correctly describes an image is a critical part of vision-language understanding. However, state-of-the-art models often misinterpret the cor…
Learn2Augment: Learning to Composite Videos for Data Augmentation in Action Recognition
Shreyank N Gowda, Marcus Rohrbach, Frank Keller +1
We address the problem of data augmentation for video action recognition. Standard augmentation strategies in video are hand-designed and sample the space of possible augmented dat…
Reliable Visual Question Answering: Abstain Rather Than Answer Incorrectly
Spencer Whitehead, Suzanne Petryk, Vedaad Shakib +4
Machine learning has advanced dramatically, narrowing the accuracy gap to humans in multimodal tasks like visual question answering (VQA). However, while humans can say "I don't kn…
Learning To Recognize Procedural Activities with Distant Supervision
Xudong Lin, Fabio Petroni, Gedas Bertasius +3
In this paper we consider the problem of classifying fine-grained, multi-step activities (e.g., cooking different recipes, making disparate home improvements, creating various form…
FLAVA: A Foundational Language And Vision Alignment Model
Amanpreet Singh, Ronghang Hu, Vedanuj Goswami +4
State-of-the-art vision and vision-and-language models rely on large-scale visio-linguistic pretraining for obtaining good performance on a variety of downstream tasks. Generally,…