| Date | Topics | Assigned Readings for Class | Assignments Due Before Class (posted on Canvas) |
|---|---|---|---|
| Mon, Aug 28 | Course Introduction (lecture slides) |
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| Wed, Aug 30 | Course Introduction (lecture slides) |
How to Read a CS Research Paper, How to Read an Engineering Research Paper | |
| Mon, Sep 4 | No Class (Labor Day) | ||
| Wed, Sep 6 | Object Recognition (lecture slides) |
ImageNet: A large-scale hierarchical image database | Reading Assignment |
| Mon, Sep 11 | Object Recognition (lecture slides) |
ImageNet classification with deep convolutional neural networks (AlexNet) | Reading Assignment, Lecture Topic Selection |
| Wed, Sep 13 | Scene and Attribute Classification (lecture slides) |
Learning Deep Features for Scene Recognition using Places Database | Reading Assignment |
| Mon, Sep 18 | Object Detection (lecture slides) |
Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation (R-CNN) | Reading Assignment |
| Wed, Sep 20 | Object Detection (lecture slides) |
You Only Look Once: Unified, Real-Time Object Detection (YOLO) | Reading Assignment |
| Mon, Sep 25 | Guest Lecture: Samreen Anjum on Single Object Tracking | SiamRPN++: Evolution of Siamese Visual Tracking with Very Deep Networks | Reading Assignment |
| Wed, Sep 27 | Semantic Segmentation (lecture slides) |
Fully Convolutional Networks for Semantic Segmentation | Reading Assignment |
| Mon, Oct 2 | Vision Transformers (lecture slides) |
An Image is Worth 16X16 Words: Transformers for Image Recognition at Scale | Reading Assignment |
| Wed, Oct 4 | Visual Foundation Models and Prompts (lecture slides) |
Visual Prompt Tuning | Reading Assignment, Project Proposal |
| Mon, Oct 9 | Instance Segmentation | Required: IDOL algorithm; Optional: YoutubeVIS2019 dataset | Reading Assignment |
| Wed, Oct 11 | Panoptic Segmentation | Required: YOSO algorithm; Optional: CityScapes dataset | Reading Assignment |
| Mon, Oct 16 | Object Part Segmentation | Required: Compositor; Optional: PartImageNet dataset | Reading Assignment |
| Wed, Oct 18 | Referring Expression Comprehension | Required: MDETR; Optional: RefCOCOg dataset | Reading Assignment |
| Mon, Oct 23 | Image Captioning | Required: mPLUG; Optional: COCO-Captions | Reading Assignment |
| Wed, Oct 25 | Visual Question Answering | Required: LaTr; Optional: Context-VQA | Reading Assignment |
| Mon, Oct 30 | Image Synthesis | Required: Denoising Diffusion Probabilistic Models; Optional: CIFAR | Reading Assignment |
| Wed, Nov 1 | Image Inpainting | Required: MAT; Optional: NTIRE 2022 | Reading Assignment |
| Mon, Nov 6 | 3D Model Synthesis | Required: NeRF; Optional: ShapeNet | Reading Assignment |
| Wed, Nov 8 | Image/Video Restoration | Required: SwinIR; Optional: DIV2K | Reading Assignment |
| Mon, Nov 13 | Image/Video Super Resolution | Required: HAT; Optional: NTIRE 2017 | Reading Assignment |
| Wed, Nov 15 | Action Recognition in Videos | Required: ActionCLIP; Optional: Kinetics | Reading Assignment |
| Mon, Nov 20 | No Class (Fall Break) | ||
| Wed, Nov 22 | No Class (Fall Break) | ||
| Mon, Nov 27 | Style Transfer (lecture slides) |
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| Wed, Nov 29 | Model Compression (lecture slides) |
Project Outline | |
| Mon, Dec 4 | Efficient Learning (lecture slides) |
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| Wed, Dec 6 | Responsible Computer Vision (lecture slides) |
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| Mon, Dec 11 | Responsible Computer Vision | ||
| Wed, Dec 13 | Final Project Presentation | Final Project Presentation, Peer Evaluations | |
| Tue, Dec 19 | No Class (Final Exam Week) | Final Project Report |
* Blue entries indicate the weeks for the student-led lectures.