Welcome to share your favorite CV papers here!

[XFeiF]

Welcome to share your favorite computer vision papers here, and I'll read and write some notes if I think it is interesting, too :)
You can also provide your notes/comments/blogs with the paper together! 🍻

[XFeiF]

分享你觉得有意思、有价值的CV类文章～

[XFeiF]

Papers(issues) summarized are set closed,
while papers newly collected or waited for reading are opened.

[XFeiF]

For a good note (understanding) about a paper, we follow the ten-questions principle designed by Doctor Gang Hua. Here is a good post (in Chinese) about how to boost your skill of reading papers.

Ten Questions to Ask When Reading a Paper

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1. What is the problem addressed in the paper?

The answer shall address: what are the input X (e.g. a single RGB image, an image sequence, or an RGBD image), what are the Y (e.g. pose of the human in the image) what are the constraints on X and/or Y, if any.

2. Is this a new problem?

If it is a new problem, why does it matter? A new, meaningful, and yet challenging research problem needs a keen eye to spot, and if it is big/important enough, it may draw many people to work on it. So in a sense, it is a kind of highest innovation.

If it is not an entirely new problem, why does it still matter? When you pick up a problem to work on, you need to clearly state why it is important.

3. What is the scientific hypothesis that the paper is trying to verify?

Answer to this question is to address what new knowledge is advanced in the paper. A scientific hypothesis sounds like: "If we did abc in our algorithm/dnn architecture, 90% of the case we can guarantee results def." A concrete example, "In ResNet, If we do the residue block, we expect to be able to learn much deeper networks, which leads to much higher recognition accuracy."

4. What are the key related works and who are the key people working on this topic?

It is important to identify the most relevant work that inspired the work in this paper. Grouping them helps with a taxonomy helps you to build a systematic view of the research problem addressed. And finally, please memorize the name of the authors and affiliations of these related works, as they will be key people who will appreciate or criticize this work.

5. What is the key to the proposed solution in the paper?

Please summarize the key differentiation of the paper when compared with the previous related works.

6. How are the experiments designed?

Experiments design is very important. A good experiments design shall validate all claims made in the paper. Indeed, experiments should be designed around this validation.

7. What datasets are built/used for the quantitative evaluation? Is the code open-sourced?

Dataset is an important factor in scientific research. And code helps readers to reproduce the results.

8. Is the scientific hypothesis well supported by evidence in the experiments?

Are the claims in the paper well supported by the experimental results?

9. What are the contributions of the paper?

Up to this point, it should be clear if the paper made one or some solid contributions, which really refers to what knowledge is advanced.

10. What should/could be done next?

This shall summarize your understanding of the limitations of the proposed method in the paper. Addressing these limitations are naturally future research, both from the problem definition itself and/or technical improvement. Or it could be linked to some other abstract knowledge in your cognitive model and stimulate new directions to go. This final question is the creativity part.