ICCV 2015: Twenty one hottest research papers

"Geometry vs Recognition" becomes ConvNet-for-X

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.


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."















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."






11. Conditional Random Fields as Recurrent Neural Networks Shuai Zheng, Sadeep Jayasumana, Bernardino Romera-Paredes, Vibhav Vineet, Zhizhong Su, Dalong Du, Chang Huang, Philip H. S. Torr



"We formulate mean-field approximate inference for the Conditional Random Fields with Gaussian pairwise potentials as Recurrent Neural Networks."






12. Flowing ConvNets for Human Pose Estimation in Videos Tomas Pfister, James Charles, Andrew Zisserman



"We investigate a ConvNet architecture that is able to benefit from temporal context by combining information across the multiple frames using optical flow."





13. Dense Optical Flow Prediction From a Static Image Jacob Walker, Abhinav Gupta, Martial Hebert



"Given a static image, P-CNN predicts the future motion of each and every pixel in the image in terms of optical flow. Our P-CNN model leverages the data in tens of thousands of realistic videos to train our model. Our method relies on absolutely no human labeling and is able to predict motion based on the context of the scene."


14. DeepBox: Learning Objectness With Convolutional Networks Weicheng Kuo, Bharath Hariharan, Jitendra Malik



"Our framework, which we call DeepBox, uses convolutional neural networks (CNNs) to rerank proposals from a bottom-up method."








15. Active Object Localization With Deep Reinforcement Learning Juan C. Caicedo, Svetlana Lazebnik



"This agent learns to deform a bounding box using simple transformation actions, with the goal of determining the most specific location of target objects following top-down reasoning."





16. Predicting Depth, Surface Normals and Semantic Labels With a Common Multi-Scale Convolutional Architecture David Eigen, Rob Fergus



"We address three different computer vision tasks using a single multiscale convolutional network architecture: depth prediction, surface normal estimation, and semantic labeling."















17. HD-CNN: Hierarchical Deep Convolutional Neural Networks for Large Scale Visual Recognition Zhicheng Yan, Hao Zhang, Robinson Piramuthu, Vignesh Jagadeesh, Dennis DeCoste, Wei Di, Yizhou Yu



"We introduce hierarchical deep CNNs (HD-CNNs) by embedding deep CNNs into a category hierarchy. An HD-CNN separates easy classes using a coarse category classifier while distinguishing difficult classes using fine category classifiers."





18. FlowNet: Learning Optical Flow With Convolutional Networks Alexey Dosovitskiy, Philipp Fischer, Eddy Ilg, Philip Häusser, Caner Hazırbaş, Vladimir Golkov, Patrick van der Smagt, Daniel Cremers, Thomas Brox



"We construct appropriate CNNs which are capable of solving the optical flow estimation problem as a supervised learning task."







19. Understanding Deep Features With Computer-Generated Imagery Mathieu Aubry, Bryan C. Russell


"Rendered images are presented to a trained CNN and responses for different layers are studied with respect to the input scene factors."







20. PoseNet: A Convolutional Network for Real-Time 6-DOF Camera Relocalization Alex Kendall, Matthew Grimes, Roberto Cipolla



"Our system trains a convolutional neural network to regress the 6-DOF camera pose from a single RGB image in an end-to-end manner with no need of additional engineering or graph optimisation."





21. Visual Tracking With Fully Convolutional Networks Lijun Wang, Wanli Ouyang, Xiaogang Wang, Huchuan Lu




"A new approach for general object tracking with fully convolutional neural network."

Conclusion

While some can argue that the great convergence upon ConvNets is making the field less diverse, it is actually making the techniques easier to comprehend. It is easier to "borrow breakthrough thinking" from one research direction when the core computations are cast in the language of ConvNets. Using ConvNets, properly trained (and motivated!) 21 year old graduate student are actually able to compete on benchmarks, where previously it would take an entire 6-year PhD cycle to compete on a non-trivial benchmark.

See you next week in Chile!

Update (January 13th, 2016)

The following awards were given at ICCV 2015.

Achievement awards

• PAMI Distinguished Researcher Award (1): Yann LeCun
• PAMI Distinguished Researcher Award (2): David Lowe
• PAMI Everingham Prize Winner (1): Andrea Vedaldi for VLFeat
• PAMI Everingham Prize Winner (2): Daniel Scharstein and Rick Szeliski for the Middlebury Datasets

Paper awards

• PAMI Helmholtz Prize (1): David Martin, Charles Fowlkes, Doron Tal, and Jitendra Malik for their ICCV 2001 paper "A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics".
• PAMI Helmholtz Prize (2): Serge Belongie, Jitendra Malik, and Jan Puzicha, for their ICCV 2001 paper "Matching Shapes".
• Marr Prize: Peter Kontschieder, Madalina Fiterau, Antonio Criminisi, and Samual Rota Bulo, for "Deep Neural Decision Forests".
• Marr Prize honorable mention: Saining Xie and Zhuowen Tu for"Holistically-Nested Edge Detection".

For more information about awards, see Sebastian Nowozin's ICCV-day-2 blog post.

I also wrote another ICCV-related blog post (January 13, 2016) about the Future of Real-Time SLAM.

Just Add Vision: Turning Computers Into Robots

The future of technology is all about improving the human experience. And the human experience is all about you -- you filling your life with less tedious work, more fun, less discomfort, and more meaningful human interactions. Whether new technology will let us enjoy life more during our spare time (think of what big screen TVs did for entertainment), or, let us become more productive at work (think of what calculators did for engineers), successful technologies have the tendency to improve our quality of life. 

Let’s take a quick look at how things got started... 

IBM started a chain of events by building affordable computers for small businesses to increase their productivity. Microsoft and Apple then created easy-to-use operating systems which allowed the common man to use computers at home for both entertainment (computer games) and being more productive (MS Office). Once personal computers started entering our homes, it was only a matter of a years until broadband internet access become widespread. Google then came along and changed the way we retrieve information from the internet while Social networking redefined how we interact with the people in our lives. Let's not forget modern smartphones, which let us use all of this amazing technology while on the go! 

Surely our iPhones will get faster and smaller while Google search will become more robust, but does the way we interact with these devices have to stay the same? And will these devices always do the same things? 

Computers without keyboards 
A lot of the world’s most exciting technology is designed to be used directly by people and ceases to provide much value once we stop directly interacting with our devices. I honestly believe that instead of wearing more computing devices (such as Google Glass) and learning new iOS commands, what we need is technology that can do useful things on its own, without requiring a person to hit buttons or custom keyboards. Because doing useful things entails having some sort of computational unit inside, it is fair to think of these future devices as “computers.” However, making computers do useful things on their own requires making machines intelligent, something which is yet to reach the masses, so I think a better name for these devices is robots. 

What is a robot? 
If we want machines to help us out in our daily tasks (e.g., cleaning, cooking, driving, playing with us, teaching us) we need machines that can both perceive their immediate environment and act intelligently. The perception-and-action loop is all that is necessary in order to turn everyday computers into intelligent robots. While it would be “nice” to build humanoid robots which look like this: 

In my opinion, a robot is any device capable of executing its own perception and action loop. Thus, it is not necessary to have full-fledged humanoid robots to start reaping the benefit of consumer-robotics in-home robotics. Once we stop looking for smart machines with legs, and broaden our definition of a robot, it is easy to tell that the revolution has already begun. 

Current desktop computers and laptops, which require input in the form of a key being pressed or a movement on the trackpad, can be viewed as semi-intelligent machines -- but because the input interfaces render the perception problem unnecessary, I do not consider them full-fledged robots. However, an iPhone running Siri is capable of sending a text message to one of our contacts via speech, so to some extent I consider Siri-enabled iPhones as robots. Tasks such as cleaning cannot be easily automated using Siri because no matter how dirty a floor is, it will never exclaim, “I’m dirty, please clean me!”. What we need is the ability for our devices to see -- namely, recognize objects in the environment (is this a sofa or a chair?), infer their state (clean vs. dirty), and track their spatial extent in the environment (these pixels belong to the plate). 

Just add vision
We have spent decades using keyboards and mice, essentially learning a machine-specific language between us and machines. Whether you consider keystrokes as a high-level or low-level language is besides the point -- it is still a language, and more specifically a language which requires inputting everything explicitly. If we want machines to effortlessly interact with the world, we need to teach them our language and let them perceive the world directly. With the current advancements in computer vision, this is becoming a reality. But the world needs more visionary thinkers to become computer vision experts, more vision experts to start caring about broader uses of their technology, more everyday programmers to use computer vision in their projects, and more expert-grade computer vision tools accessible to those just starting out. Only then, will we be able to pool our collective efforts and finally interweave in-home robotics with the everyday human experience. 

What's next?
Wouldn’t it be great if we had a general-purpose machine vision API which would render the most tedious and time-consuming part of training object detectors obsolete? Wouldn't it be awesome if we could all use computer vision without becoming mathematics gurus or having years of software engineering experience?  Well, this might be happening sooner than you think.  In an upcoming blog post, I will describe what this API is going to look like and why it’s going to make your life a whole lot easier.  I promise not to disappoint...

Beyond Categorization: Getting Away From Object Categories in Computer Vision

Natural language evolved over thousands of years to become the powerful tool that is is today. When we say things using language to convey our experiences with the world, we can't help but refer to object categories. When we say things such as "this is a car" what we are actually saying is "this is an instance from the car category." Categories let us get away from referring to individual object instances -- in most cases knowing that something belongs to a particular category is more than enough knowledge to deal with it. This is a type of "understanding by compression" or understanding by abstracting away the unnecessary details. In the words of Rosch, "the task of category systems is to provide maximum information with the least cognitive effort." Rosch would probably agree that it only makes sense to talk about the utility of a category system (a for getting a grip on reality) as opposed to the truth value of a category system with respect how well it aligns to observer-independent reality. The degree of pragmatism expressed by Rosch is something that William James would have been proud of.

From a very young age we are taught language and soon it takes over our inner world. We 'think' in language. Language provides us with a list of nouns -- a way of cutting up the world into categories. Different cultures have different languages that cut up the world differently and one might wonder how well the object categories contained in any given single language correspond to reality -- if it even makes sense to talk about an observer independent reality. Rosch would argue that human categorization is the result of "psychological principles of categorization" and is more related to how we interact with the world than how the world is. If the only substances we ingested for nutrients were types of grass, then categorizing all of the different strains of grass with respect to flavor, vitamin content, color, etc would be beneficial for us (as a species). Rosch points out in her works that her ideas refer to categorization at the species-level and she calls it human categorization. She is not referring to a personal categorization; for example, the way a child might cluster concepts when he/she starts learning about the world.

It is not at all clear to me whether we should be using the categories from natural language as the to-be-recognized entities in our image understanding systems. Many animals do not have a language with which they can compress percepts into neat little tokens -- yet they have no problem interacting with the world. Of course, if we want to build machines that understand the world around them in a way that they can communicate with us (humans), then language and its inherent categorization will play a crucial role.

While we ultimately use language to convey our ideas to other humans, how early are the principles of categorization applied to perception? Is the grouping of percepts into categories even essential for perception? I doubt that anybody would argue that language and its inherent categorization is not useful for dealing with the world -- the only question is how it interacts with perception.

Most computer vision researchers are stuck in the world of categorization and many systems rely on categorization at a very early stage. A problem with categorization is its inability to deal with novel categories -- something which humans must deal with at a very young age. We (humans) can often deal with arbitrary input and using analogies can still get a grip and the world around us (even when it is full of novel categories). One hypothesis is that at the level of visual perception things do not get recognized into discrete object classes -- but a continuous recognition space. Thus instead of asking the question, "What is this?" we focus on similarity measurements and ask "What is this like?". Such a comparison-based view would help us cope with novel concepts.