Three suggestions:
a) pick your favourite conference and look through the proceedings. There is usually a "what's hot" session or a similar mention in the conference summary;
b) if you are looking for a grad study research topic, choose a good adviser FIRST. The adviser/student relationship is way, waaayyyy more important than the topic; and
c) if you start your research based on "what's hot", you're already behind the leading edge of research in that area, plus you will likely be pursuing something of less interest to you. That will be a problem when, as always happens, there are difficulties experienced progressing the research. Instead, ask yourself "what interests _me_" and get together with a potential adviser to see how to pursue it. Ideally, try and think (with your adviser) about WHAT IS GOING TO BE HOT, or better, WHAT YOU WANT TO MAKE HOT :-)
Three suggestions:
a) pick your favourite conference and look through the proceedings. There is usually a "what's hot" session or a similar mention in the conference summary;
b) if you are looking for a grad study research topic, choose a good adviser FIRST. The adviser/student relationship is way, waaayyyy more important than the topic; and
c) if you start your research based on "what's hot", you're already behind the leading edge of research in that area, plus you will likely be pursuing something of less interest to you. That will be a problem when, as always happens, there are difficulties experienced progressing the research. Instead, ask yourself "what interests _me_" and get together with a potential adviser to see how to pursue it. Ideally, try and think (with your adviser) about WHAT IS GOING TO BE HOT, or better, WHAT YOU WANT TO MAKE HOT :-)
I think these are totally hot direction, You on yourself decide your idea and come with the topic for you. Because these field are becoming very popular in the era of large data which is getting created from various sources. Modelling, Text Mining, Clustering, Classification are used in most of the applications.
I fully agree with Mike Bourasa, yet, to answer the original question, both Learning and Vision are dominated by deep neural nets. Data mining, I would not know, but if it's urgent for you to have a response, I'd look into conferences such as IEEE KDD.
Big data. Medical statistics. Biomarker discovery. All hot topics with plenty to explore plus potential to significant and lucrative contributions.
I would say we the computer science community now must accept this fact that the technology now evolves on the basis of trend analysis and behavior reading for the ultimate kings END-USERS; which is a good sign where the customer comes first.
By stating above i did not mean the innovation is no more required but now the innovation is well-directed, more efficient and productive.
Patterns are always good to follow and Standards are always welcomed in our time of the history. And both patterns and standards are the result of the data mining, machine learning and computer vision.
go trhrough
DATA MINING AND KNOWLEDGE DISCOVERY Bimonthly ISSN: 1384-5810
or
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING Bimonthly
Big data, evidence based decision making, Social network Mining
Specifically, in computer vision, there are several topics that can be considered trends nowadays. In the following link, you can see an interesting report about some of them which was written after CVPR 2013:
http://quantombone.blogspot.co.uk/2013/07/cvpr-2013-three-trending-computer.html
Hot topics in Data Mining, Machine Leaning:
Mining big data .
Imbalanced data set problem.
Enhance transparency of black box techniques such as support vector machine and ensemble learning.
In CVPR2014 one of hot topic was "Convolutional Neural Net" which is a kind of deep learning method. This topic was a hot topic in that conference, machine learning, and computer vision these days.
Deep multiple kernel learning. Kernel boosting. Causal inference.
It is the creation of self-learning machines with associative thinking.
Off the top of my head:
Data Mining: Big data of course. High speed streaming data mining
Machine Learning: Deep Learning
Computer Vision: Tons, but mostly object detection, recognition
Your question sounds like "what's the best tasting food on the planet?". Everyone's going to give you a different answer based on their own likes and intuition. You should taste samples here n there (a.k.a read papers or survey articles in these fields) and decide what suits your palate.
distributed processing for streaming data is also another active topic.
Well, this is a very wide question. Therfore, you get some short answers:
1) Big Data approaches are really a hot top in engineering, Services, and banking as well as investigations. Lots of reasearch is necessary.
2) Machine Learning (really difficult): Deep Learning is an interesting and relatively new topic. Interesting approaches for applications you will find in the field of image processing. However, it is worthwhile to do research in other directions, like acoustics (Speech-processing) and especially semantics.
3) In Image Processing interesting topics are Fusion approaches in Cobotics (colaborative machines) and Human-Machine-Interaction.
A general comment: All of the above mentioned topics are basics for Industry 4.0 and Cyber-Physical-Production-System concepts.(really hot in Europe).
To my knowledge (which may be outdated), I think the following is still unsolved (research has been done, but solution not mature yet), but would change the world if fully solved and requires all 3 - computer vision, machine learning, big data mining:
Image retrieval by examples or by sketching: imaging googling for all images of "a cat in a car" by showing an example or by sketching it out.
Or music retrieval by humming or whistling... I know there has been some papers on this.. but I'm not sure if this is a mature area of research.
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