84 citations · 223 across the 6 of their papers we have counts for
6 papers
EgoPolice: A Benchmark for Egocentric Video Understanding in High-Stakes Police Body-Worn Camera Footage
Max Gonzalez Saez-Diez, Jihoon Chung, Adam D. Wolsky +5
We introduce EgoPolice, a carefully curated dataset of real, egocentric police-civilian interactions, sourced from publicly available body-worn camera videos. We select police-civi…
Learning to Learn from Noisy Web Videos
Serena Yeung, Vignesh Ramanathan, Olga Russakovsky +3
Understanding the simultaneously very diverse and intricately fine-grained set of possible human actions is a critical open problem in computer vision. Manually labeling training v…
Predictive-Corrective Networks for Action Detection
Achal Dave, Olga Russakovsky, Deva Ramanan
While deep feature learning has revolutionized techniques for static-image understanding, the same does not quite hold for video processing. Architectures and optimization techniqu…
Crowdsourcing in Computer Vision
Adriana Kovashka, Olga Russakovsky, Li Fei-Fei +1
Computer vision systems require large amounts of manually annotated data to properly learn challenging visual concepts. Crowdsourcing platforms offer an inexpensive method to captu…
End-to-end Learning of Action Detection from Frame Glimpses in Videos
Serena Yeung, Olga Russakovsky, Greg Mori +1
In this work we introduce a fully end-to-end approach for action detection in videos that learns to directly predict the temporal bounds of actions. Our intuition is that the proce…
Every Moment Counts: Dense Detailed Labeling of Actions in Complex Videos
Serena Yeung, Olga Russakovsky, Ning Jin +3
Every moment counts in action recognition. A comprehensive understanding of human activity in video requires labeling every frame according to the actions occurring, placing multip…