24 citations · 77 across the 11 of their papers we have counts for
5 papers · 1 filter
A-OKVQA: A Benchmark for Visual Question Answering using World Knowledge
Dustin Schwenk, Apoorv Khandelwal, Christopher Clark +2
The Visual Question Answering (VQA) task aspires to provide a meaningful testbed for the development of AI models that can jointly reason over visual and natural language inputs. D…
KRISP: Integrating Implicit and Symbolic Knowledge for Open-Domain Knowledge-Based VQA
Kenneth Marino, Xinlei Chen, Devi Parikh +2
One of the most challenging question types in VQA is when answering the question requires outside knowledge not present in the image. In this work we study open-domain knowledge, t…
OK-VQA: A Visual Question Answering Benchmark Requiring External Knowledge
Kenneth Marino, Mohammad Rastegari, Ali Farhadi +1
Visual Question Answering (VQA) in its ideal form lets us study reasoning in the joint space of vision and language and serves as a proxy for the AI task of scene understanding. Ho…
The Pose Knows: Video Forecasting by Generating Pose Futures
Jacob Walker, Kenneth Marino, Abhinav Gupta +1
Current approaches in video forecasting attempt to generate videos directly in pixel space using Generative Adversarial Networks (GANs) or Variational Autoencoders (VAEs). However,…
The More You Know: Using Knowledge Graphs for Image Classification
Kenneth Marino, Ruslan Salakhutdinov, Abhinav Gupta
One characteristic that sets humans apart from modern learning-based computer vision algorithms is the ability to acquire knowledge about the world and use that knowledge to reason…