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20+ hottest research papers on Computer Vision, Machine Learning


December's ICCV 2015 conference in Santiago, Chile has come and gone, but that's no reason not to know about its top papers. Get an update on which computer vision papers and researchers won awards.

By Tomasz Milisiewicz.

Computer Vision used to be cleanly separated into two schools: geometry and recognition. Geometric methods like structure from motion and optical flow usually focus on measuring objective real-world quantities like 3D "real-world" distances directly from images and recognition techniques like support vector machines and probabilistic graphical models traditionally focus on perceiving high-level semantic information (i.e., is this a dog or a table) directly from images.

The world of computer vision is changing fast has changed. We now have powerful convolutional neural networks that are able to extract just about anything directly from images. So if your input is an image (or set of images), then there's probably a ConvNet for your problem. While you do need a large labeled dataset, believe me when I say that collecting a large dataset is much easier than manually tweaking knobs inside your 100K-line codebase. As we're about to see, the separation between geometric methods and learning-based methods is no longer easily discernible.

By 2016 just about everybody in the computer vision community will have tasted the power of ConvNets, so let's take a look at some of the hottest new research directions in computer vision.

ICCV 2015's Twenty One Hottest Research Papers

This December in Santiago, Chile, the International Conference of Computer Vision 2015 is going to bring together the world's leading researchers in Computer Vision, Machine Learning, and Computer Graphics.

To no surprise, this year's ICCV is filled with lots of ConvNets, but this time the applications of these Deep Learning tools are being applied to much much more creative tasks. Let's take a look at the following twenty one ICCV 2015 research papers, which will hopefully give you a taste of where the field is going.

CNN vision application

1. Ask Your Neurons: A Neural-Based Approach to Answering Questions About Images Mateusz Malinowski, Marcus Rohrbach, Mario Fritz

We propose a novel approach based on recurrent neural networks for the challenging task of answering of questions about images. It combines a CNN with a LSTM into an end-to-end architecture that predict answers conditioning on a question and an image.

2. Aligning Books and Movies: Towards Story-Like Visual Explanations by Watching Movies and Reading Books Yukun Zhu, Ryan Kiros, Rich Zemel, Ruslan Salakhutdinov, Raquel Urtasun, Antonio Torralba, Sanja Fidler

To align movies and books we exploit a neural sentence embedding that is trained in an unsupervised way from a large corpus of books, as well as a video-text neural embedding for computing similarities between movie clips and sentences in the book.

3. Learning to See by Moving Pulkit Agrawal, Joao Carreira, Jitendra Malik

We show that using the same number of training images, features learnt using egomotion as supervision compare favourably to features learnt using class-label as supervision on the tasks of scene recognition, object recognition, visual odometry and keypoint matching.

4. Local Convolutional Features With Unsupervised Training for Image Retrieval Mattis Paulin, Matthijs Douze, Zaid Harchaoui, Julien Mairal, Florent Perronin, Cordelia Schmid

We introduce a deep convolutional architecture that yields patch-level descriptors, as an alternative to the popular SIFT descriptor for image retrieval.

5. Deep Networks for Image Super-Resolution With Sparse Prior Zhaowen Wang, Ding Liu, Jianchao Yang, Wei Han, Thomas Huang

We show that a sparse coding model particularly designed for super-resolution can be incarnated as a neural network, and trained in a cascaded structure from end to end.

6. High-for-Low and Low-for-High: Efficient Boundary Detection From Deep Object Features and its Applications to High-Level Vision Gedas Bertasius, Jianbo Shi, Lorenzo Torresani

In this work we show how to predict boundaries by exploiting object level features from a pretrained object-classification network.

Left and right image patches

7. A Deep Visual Correspondence Embedding Model for Stereo Matching Costs Zhuoyuan Chen, Xun Sun, Liang Wang, Yinan Yu, Chang Huang

A novel deep visual correspondence embedding model is trained via Convolutional Neural Network on a large set of stereo images with ground truth disparities. This deep embedding model leverages appearance data to learn visual similarity relationships between corresponding image patches, and explicitly maps intensity values into an embedding feature space to measure pixel dissimilarities.

8. Im2Calories: Towards an Automated Mobile Vision Food Diary Austin Meyers, Nick Johnston, Vivek Rathod, Anoop Korattikara, Alex Gorban, Nathan Silberman, Sergio Guadarrama, George Papandreou, Jonathan Huang, Kevin P. Murphy

We present a system which can recognize the contents of your meal from a single image, and then predict its nutritional contents, such as calories.

9. Unsupervised Visual Representation Learning by Context Prediction Carl Doersch, Abhinav Gupta, Alexei A. Efros

How can one write an objective function to encourage a representation to capture, for example, objects, if none of the objects are labeled?

10. Deep Neural Decision Forests Peter Kontschieder, Madalina Fiterau, Antonio Criminisi, Samuel Rota Bulò

We introduce a stochastic and differentiable decision tree model, which steers the representation learning usually conducted in the initial layers of a (deep) convolutional network.