Course Format and Requirements
You will be assigned a paper(s) to read and present to the class. The first 4 presenters each week present on Monday, and the next 4 present on Wednesday.
The presentation should last approximately 20 minutes, and will be followed by 10 minutes of Q&A and discussion. We will be timing presentations to maintain the reading schedule, and may cut off presenters who exceed the allotted time. Similarly, if we find one partner presenting more than half of the time, we may cut off and move on to the next partner.
At a minimum, your presentation should cover:
Required Background: for example, if your paper explores a novel CNN architecture, perhaps you might take a few slides to explain what a convolutional neural network is before you share details about the paper's unique network architecture. Your classmates will appreciate the lesson (or review), and you can continue to build your understanding of fundamental ideas as you explore your assigned paper.
Analysis and results
Advantages & disadvantages of approach
Key contribution(s) to the field
Some papers have readily-available repositories for readers to replicate and explore the models. If applicable to your paper, feel free to do so (and share your findings) to enhance your presentation.
You are additionally required to make a private post on Piazza with two questions & answers related to your paper. These questions should be at a level that a classmate could answer following your presentation.
If you have questions while reading your paper and developing your presentation, before asking in Office Hours, you should first ask your question publicly on Piazza to initiate class discussion. TAs will also be attentive to this channel to assist.
Presentation Overview Quizzes (30%)
Quizzes will be given at the end of each unit. Quizzes will draw on concepts and ideas from presented papers, and may include questions created by your classmates.
Class & Piazza Participation (20%)
You can earn your participation grade by:
Coming to class ready to discuss reading assignments,
Engaging in questions or discussion with presenters and peers during class,
Engaging in questions or discussion with presenters and peers on Piazza.
This list is non-exhaustive.
Kevan Yuen and Mohan M. Trivedi, "Looking at Hands in Autonomous Vehicles:A ConvNet Approach using Part Affinity Fields," IEEE Transactions on Intelligent Vehicles, 2019 [Jason Isa]
Akshay Rangesh, Bowen Zhang and Mohan M. Trivedi, "Gaze Preserving CycleGANs for Eyeglass Removal & Persistent Gaze Estimation," 2020. [Evan Smith]
Martin, Manuel, et al. "Drive&act: A multi-modal dataset for fine-grained driver behavior recognition in autonomous vehicles." IEEE/CVF ICCV. 2019. [Tritai Nguyen]
Ortega, Juan Diego, et al. "Dmd: A large-scale multi-modal driver monitoring dataset for attention and alertness analysis." European Conference on Computer Vision. Springer, Cham, 2020. [Yichen Jia]
Akshay Rangesh, Nachiket Deo, Ross Greer, Pujitha Gunaratne and Mohan M. Trivedi, "Autonomous Vehicles that Alert Humans to Take-Over Controls: Modeling with Real-World Data," 2021 IEEE International Intelligent Transportation Systems Conference (ITSC). IEEE, 2021 [Jia Qiu]
Palazzi, Andrea, et al. "Predicting the Driver's Focus of Attention: the DR (eye) VE Project." IEEE transactions on pattern analysis and machine intelligence (2018) [Ashwin Rao]
Abati, Davide, et al. "Latent space autoregression for novelty detection." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2019 [Muqing Li]
M. Gao, A. Tawari and S. Martin, "Goal-oriented Object Importance Estimation in On-road Driving Videos," 2019 International Conference on Robotics and Automation (ICRA), 2019 [Tian Qiu]
Quiz 3 [Following Presentations, during our 7-10 PM final time]
Benjamin Ranft and Christoph Stiller: The Role of Machine Vision for Intelligent Vehicles