Course Overview

Description: Introduces the growing interdisciplinary intersection of computer vision and planetary health, with a focus on introducing open challenges in CV, and AI more broadly, that limit the deployability of automated approaches for global environmental challenges. Topics include representation learning for imbalanced, fine-grained, and open-set categories, distributional robustness and adaptation, efficiency in training, evaluation, and inference, human-AI collaboration via, e.g., active learning, selective prediction, or active inference, and heterogeneously sampled multimodal learning. Lecture material covers fundamentals and SOTA methods from recent papers. Includes in-class discussion and participation, presentation of papers, and a group final project.

Pre-requisites: 6.8300 or 6.7960 or permission of instructor

Course Information

Class Schedule

** Class schedule is subject to change **

Final project assignment details

The final project in this course will be to propose a new CV-based research project for an ecological application. We are very open to broad interpretations of both of these. You can work in groups of up to 3. We would like to see proposals that are clearly motivated from the ecological side, as well as clearly grounded in the existing AI literature. We take the stance that novelty can take many forms, and are excited to see your proposed versions of “Application-driven innovation”.

Proposals should be 6-8 pages long and include the following sections:

1. Introduction:
• Motivates the problem that your research project aims to solve
• Include at least one “hero” figure that captures the project goals at a high level
2. Related work
• Clearly outlines the past work that relates to the proposed research and characterizes gaps that the method will hopefully fill
3. Datasets
• Clearly define the data that you will work with for this project. If working from existing public datasets, justify why they are reasonably representative for the task or propose extensions/adaptations of those datasets. You can also propose the collection of new data here. However if you will be proposing relabeling of additional data or collection of new data we would like to see an estimate of the cost associated.
4. Method and Experiment Design
• Describe your research project methodology and design at least one figure that captures the method
• Describe if and how you will build on prior work
• Propose how you will test your method, and what ablations you plan to do, as well as possibly including mockup figures of the trends you expect to see. Be concrete.
5. Broader Impacts
• Finish by describing the potential impact the proposal could have on your application area, and describe what additional steps would need to be taken to translate the research to a user community

Proposals are due end of day May 9th on GradeScope.

AI assistants policy (honor code)

• You are not allowed to ask AI to generate your project ideas or the content for the project, we hope that by participating in this class you have lots of ideas and context to be able to formulate a proposed project well.
• You are allowed to with with AI to discover relevant literature, and to co-edit prose (with the strong request that you don’t generate a bulleted list, ask AI to expand on it, and submit it without iteration! Because this usually reads really badly and we don’t want to read annoying AI-generated overhyped text 😃 )