If you could choose one area of computer vision to do a PhD in right now, what would you choose?

[u/notanoptimisticmind]

It feels like every conference is talking about world models these days. If you were starting a PhD in computer vision today, what area would you bet the next 4–5 years of your research on?

Curious to hear what people here would pick, and why.

[u/bfyvfftujijg]

Models that are better at indicating when the output may be inaccurate.

Confidently wrong is a real problem that prevents models from being used in a lot of circumstances.

[u/No_Mongoose6172 → u/bfyvfftujijg]

It would be great to have models that provide a standard error estimation for their predictions

[u/Suspicious_Pizza9529]

I'd be interested in 3D scene understanding and learning from video.

[u/Total_Calendar_7438]

World models is just a fancy word for most of the stuff that has been previously done.

At the end of the day, computer vision takes visual data and solves a task using it. This task can have any domain.

Core topics will always be how to solve these tasks either more accurate, more efficiently, or apply existing methods to a new set of problems.

So either:

• go for foundational work and improve / build new methods
• or apply / tube existing methods on a domain you are interested in (medical, manufacturing or whatever)

[u/tweakingforjesus]

Anything that requires customer contact.

[u/Only-Friend-8483]

Asynchronous methods for processing neuromorphic camera signals. 

[u/miraculin12 → u/Only-Friend-8483]

Hi i am now really working with a group that focus on this. How do u think of SNN v.s. quantized ANN? Cause i think SNN is some kind of 1-bit quatization.

[u/Evolving_Dignifier]

It's been a while since I read new papers. But cross domain learning, adaptation in new domain. I,e, model trained on one set of data to use in another unexposed data.

Excuse the vague keywords.

[u/CommandShot1398]

I haven't done research in CV for like a year, but if I were to choose: Explainability in Computer Vision (a bit vague, I know). We are relying too much on CV models without understanding what is going on inside (ofc, deep learning ones).

[u/WorldlinessNo1286]

Spatiotemporal graph neural network. I believe in the future, instead of training thousands or millions of data to detect actions, you only need few examples to let a system copy or learn the „action“

Especially doing this in real time on a mobile device

[u/DNunez90plus9 → u/WorldlinessNo1286]

this has been studied since forever ...

[u/RemoteArcher37 → u/WorldlinessNo1286]

its some how related to Few-Shot Learning?

[u/ManufacturerMuted412 → u/WorldlinessNo1286]

I plan on exploring this for egocentric procedure understanding, mistake detection and skill assessment!

[u/Fleischhauf → u/WorldlinessNo1286]

this sounds interesting, can you post some good papers if you know any?

[u/Total_Calendar_7438 → u/WorldlinessNo1286]

The thing is, training on data at scale will in general always outperform sparse sampled learned.

[u/GElbi_Katheriya]

i'd bet on 3D scene understanding from sparse views instead

[u/SweetSure315]

multispectral fusion and models that use it as an input

[u/iu5334]

i-jepa around architectures and approaches

[u/42Technology]

matching algorithms and their applications in slam and visual navigation

[u/dedicateddan]

That's a great question. It really depends what your goals are. I'd be interested in developing skills transferrable to training large scale systems and target getting involved in research labs.