3 citations · 5 across the 5 of their papers we have counts for
6 papers
D2D: Detector-to-Differentiable Critic for Improved Numeracy in Text-to-Image Generation
Nobline Yoo, Olga Russakovsky, Ye Zhu
Text-to-image (T2I) diffusion models have achieved strong performance in semantic alignment, yet they still struggle with generating the correct number of objects specified in prom…
Efficient, Self-Supervised Human Pose Estimation with Inductive Prior Tuning
Nobline Yoo, Olga Russakovsky
The goal of 2D human pose estimation (HPE) is to localize anatomical landmarks, given an image of a person in a pose. SOTA techniques make use of thousands of labeled figures (fine…
Point and Ask: Incorporating Pointing into Visual Question Answering
Arjun Mani, Nobline Yoo, Will Hinthorn +1
Visual Question Answering (VQA) has become one of the key benchmarks of visual recognition progress. Multiple VQA extensions have been explored to better simulate real-world settin…
What Actions are Needed for Understanding Human Actions in Videos?
Gunnar A. Sigurdsson, Olga Russakovsky, Abhinav Gupta
What is the right way to reason about human activities? What directions forward are most promising? In this work, we analyze the current state of human activity understanding in vi…
What's in a Question: Using Visual Questions as a Form of Supervision
Siddha Ganju, Olga Russakovsky, Abhinav Gupta
Collecting fully annotated image datasets is challenging and expensive. Many types of weak supervision have been explored: weak manual annotations, web search results, temporal con…
Much Ado About Time: Exhaustive Annotation of Temporal Data
Gunnar A. Sigurdsson, Olga Russakovsky, Ali Farhadi +2
Large-scale annotated datasets allow AI systems to learn from and build upon the knowledge of the crowd. Many crowdsourcing techniques have been developed for collecting image anno…