COMP 648: Computer Vision Seminar | Fall 2022
Course Description: This seminar will explore and analyze the current literature in computer vision, especially focusing on computational methods for visual recognition. Our topics include image classification and understanding, object detection, image segmentation, and other high-level perceptual tasks. Particularly, we will explore this semester recent topics such as: Contrastive-learning (e.g SimCLR, CLIP, BLIP), Vision-language Transformers (e.g. ALBEF, UNITER, VisualBERT), Diffusion Models (e.g. DALL·E 2, Imagen), Learning with Synthetic Data (Hypersim, ThreeDWorld, etc), Biases in Computer Vision Models, Zero-shot Visual Recognition, Open Vocabulary Visual Recognition, Weakly Supervised Visual Grounding Models, Computer Vision for Image Generation (e.g. Stable Diffusion), among other topics.
Recommended Prerrequisites: COMP 547 (Computer Vision) or COMP 646 (Deep Learning for Vision and Language) or COMP 546/ELEC 546 (Intro to Computer Vision) or COMP 576 (Intro to Deep Learning) or COMP 647 (Deep Learning) or research experience in any of these topics.
|Aug 23th||Welcome: Introduction [pptx] [pdf]|
|Aug 30th||Contrastive Pre-training: SimCLR, CLIP, ALBEF -- Presenter: Ziyan Yang [slides] [pdf]|
|Sep 6th||Text-to-Image Synthesis with Conditional Diffusion Models -- Presenter: Aman Shrivastava [pdf]|
|Sept 13th||Masked Self-supervised Pretraining for Visual Recognition -- Presenter: Jefferson Hernandez [pdf]|
|Sep 20th||Visio-Linguistic Reasoning: Winoground, VL-Checklist -- Presenter: Paola Cascante-Bonilla [pdf]|
|Sep 27th||Visual Grounding: Learning to Localize Objects -- Presenter: Atanu Dahari [pdf]|
|Oct 4th||Structured Training and Subnetworks -- Presenter: Vicente [pdf]|
|Oct 11th||MIDTERM RECESS (NO SCHEDULED CLASSES)|
|Oct 18th||Semi-Supervised Learning -- Presenter: Maojie Tang|
|Oct 25th||Deep Image Retrieval and Matching|
|Nov 1st||Universal Computer Vision Models|
|Nov 8th||Recent Topics on Diffusion Models for Tasks other than Text-to-Image Generation|
|Nov 15th||Recent Topics on Zero-shot Learning through Large-scale Pretraining|
|Nov 22nd||Recent Topics in Bias and Fairness in Visual Recognition|
|Nov 29th||Recent Topics in Any Visual Recognition Task|
Disclaimer: The topics on this list are tentative and subject to adjustments throughout the semester as interests in the group evolve.
Logistics: This is a pass/fail one credit seminar. Registered students are required to participate and present a recent work in a topic of interest of the seminar at least once throughout the semester. A Satisfactory grade requires participating presenting a paper at least once during the semester, and actively participating in discussions throughout the semester. As a rule of thumb to get a satisfactory grade you are expected to attend at least 10 of the 14 sessions in the semester.
Honor Code and Academic Integrity: "In this course, all students will be held to the standards of the Rice Honor Code, a code that you pledged to honor when you matriculated at this institution. If you are unfamiliar with the details of this code and how it is administered, you should consult the Honor System Handbook at http://honor.rice.edu/honor-system-handbook/. This handbook outlines the University's expectations for the integrity of your academic work, the procedures for resolving alleged violations of those expectations, and the rights and responsibilities of students and faculty members throughout the process."
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Disability Resource Center: "If you have a documented disability or other condition that may affect academic performance you should: 1) make sure this documentation is on file with the Disability Resource Center (Allen Center, Room 111 / email@example.com / x5841) to determine the accommodations you need; and 2) talk with me to discuss your accommodation needs."