Best Papers
CSC 262 - Computer Vision - Weinman
1 Introduction
We will read and discuss in class one or two of the best papers from
the most recent top computer vision conferences. In this way, we'll
be learning together:
- How to read research papers
- About the latest in computer vision research
- What the community thinks is currently important
2 Candidates
Our candidates (listed in no particular order) are drawn from CVPR
2023, CVPR 2024, ECCV 2024, and ICCV 2023. See the list of papers
below and read their abstracts.
- Minimalist
Vision with Freeform Pixels. Jeremy Klotz, Shree Nayar. (ECCV '24)
- Visual
Programming: Compositional visual reasoning without training. Tanmay
Gupta, Aniruddha Kembhavi. (CVPR '23).
- Planning-oriented
Autonomous Driving. Yihan Hu, Jiazhi Yang, Li Chen, Keyu Li Chonghao
Sima, Xizhou Zhu, Siqi Chai, Senyao Du, Tianwei Lin, Wenhai Wang,
Lewei Lu, Xiaosong Jia, Qiang Liu, Jifeng Dai, Yu Qiao, and Hongyang
Li. (CVPR '23)
- Generative
Image Dynamics. Zhengqi Li, Richard Tucker, Noah Snavely, Aleksander
Holynski. (CVPR '24)
- Rich
Human Feedback for Text-to-Image Generation. ouwei Liang, Junfeng
He, Gang Li, Peizhao Li, Arseniy Klimovskiy, Nicholas Carolan, Jiao
Sun, Jordi Pont-Tuset, Sarah Young, Feng Yang, Junjie Ke, Krishnamurthy
Dj Dvijotham, Katherine M. Collins, Yiwen Luo, Yang Li, Kai J. Kohlhoff,
Deepak Ramachandran, and Vidhya Navalpakkam (CVPR '24)
- Passive
Ultra-Wideband Single-Photon Imaging. M. Wei, S. Nousias, R. Gulve,
D. Lindell, and K. Kutulakos (ICCV '23)
3 Voting
Please vote by emailing your TOP TWO choices (by number) to the instructor
by Tue 3 Dec.
4 Responses
You will be required to submit a brief 225-275 word critical response
to
each paper before class to help prepare you for the
discussion. In particular, you should note:
- What problem are they trying to solve?
- Why is the problem important?
- How does it currently get done and what are the limitations?
- What are the authors' goals?
- Does the paper have a scientific thesis? Is it falsifiable?
- What are the paper's claims?
- Are the claims substantiated (by theory or experiment)? If so, how?
- What are the limitations of the proposed approach?
- Are there ways to extend the method?
You should include at least two primary points that critique, dispute,
extend, or reinforce the paper. Submit your responses (in PDF format
only) via Gradescope; they are
due by the beginning of class
on the day of discussion.
Acknowledgments
The questions above are inspired by and adapted from the following
works.
Fong, Philip W.L., Reading a computer science research
paper ,
SIGCSE Bulletin 41, 2 (2009), pp. 138-140.
doi:10.1145/1595453.1595493
Keshav, S., How to read a paper ,
SIGCOMM
Computer Communication Review 37, 3 (2007), pp. 83-84.
doi:dx.doi.org/10.1145/1273445.1273458