2016 Poster Sessions : End-to-end Learning of Action Detection from Frame Glimpses in Videos

Student Name : Serena Yeung
Advisor : Fei-Fei Li
Research Areas: Artificial Intelligence
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 process of detecting actions is naturally one of observation and refinement: observing moments in video, and refining hypotheses about when an action is occurring. Based on this insight, we formulate our model as a recurrent neural network-based agent that interacts with a video over time. The agent observes video frames and decides both where to look next and when to emit a prediction. Since backpropagation is not adequate in this non-differentiable setting, we use REINFORCE to learn the agent's decision policy. Our model achieves state-of-the-art results on the THUMOS'14 and ActivityNet datasets while observing only a fraction (2% or less) of the video frames.

Not sure what you want (let me know if different), but:
Serena Yeung is a PhD candidate in the Stanford Vision Lab, advised by Fei-Fei Li. Her research interests are in computer vision, machine learning, and deep learning. She's particularly interested in the areas of video understanding, human action recognition, and healthcare application. Serena received a B.S. in Electrical Engineering in 2010, and an M.S. in Electrical Engineering in 2013, both from Stanford.