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Author ORCID Identifier

https://orcid.org/0000-0002-2300-4237

Document Type

Open Access Dissertation

Degree Name

Doctor of Philosophy (PhD)

Degree Program

Computer Science

Year Degree Awarded

2020

Month Degree Awarded

September

First Advisor

Erik Learned-Miller

Subject Categories

Artificial Intelligence and Robotics | Graphics and Human Computer Interfaces

Abstract

We live in a dynamic world, which is continuously in motion. Perceiving and interpreting the dynamic surroundings is an essential capability for an intelligent agent. Human beings have the remarkable capability to learn from limited data, with partial or little annotation, in sharp contrast to computational perception models that rely on large-scale, manually labeled data. Reliance on strongly supervised models with manually labeled data inherently prohibits us from modeling the dynamic visual world, as manual annotations are tedious, expensive, and not scalable, especially if we would like to solve multiple scene understanding tasks at the same time. Even worse, in some cases, manual annotations are completely infeasible, such as the motion vector of each pixel (i.e., optical flow) since humans cannot reliably produce these types of labeling. In fact, living in a dynamic world, when we move around, motion information, as a result of moving camera, independently moving objects, and scene geometry, consists of abundant information, revealing the structure and complexity of our dynamic visual world. As the famous psychologist James J. Gibson suggested, “we must perceive in order to move, but we also must move in order to perceive”. In this thesis, we investigate how to use the motion information contained in unlabeled or partially labeled videos to better understand and synthesize the dynamic visual world.

This thesis consists of three parts. In the first part, we focus on the “move to perceive” aspect. When moving through the world, it is natural for an intelligent agent to associate image patterns with the magnitude of their displacement over time: as the agent moves, far away mountains don’t move much; nearby trees move a lot. This natural relationship between the appearance of objects and their apparent motion is a rich source of information about the relationship between the distance of objects and their appearance in images. We present a pretext task of estimating the relative depth of elements of a scene (i.e., ordering the pixels in an image according to distance from the viewer) recovered from motion field of unlabeled videos. The goal of this pretext task was to induce useful feature representations in deep Convolutional Neural Networks (CNNs). These induced representations, using 1.1 million video frames crawled from YouTube within one hour without any manual labeling, provide valuable starting features for the training of neural networks for downstream tasks. It is promising to match or even surpass what ImageNet pre-training gives us today, which needs a huge amount of manual labeling, on tasks such as semantic image segmentation as all of our training data comes almost for free.

In the second part, we study the “perceive to move” aspect. As we humans look around, we do not solve a single vision task at a time. Instead, we perceive our surroundings in a holistic manner, doing visual understanding using all visual cues jointly. By simultaneously solving multiple tasks together, one task can influence another. In specific, we propose a neural network architecture, called SENSE, which shares common feature representations among four closely-related tasks: optical flow estimation, disparity estimation from stereo, occlusion detection, and semantic segmentation. The key insight is that sharing features makes the network more compact and induces better feature representations. For real-world data, however, not all an- notations of the four tasks mentioned above are always available at the same time. To this end, loss functions are designed to exploit interactions of different tasks and do not need manual annotations, to better handle partially labeled data in a semi- supervised manner, leading to superior understanding performance of the dynamic visual world.

Understanding the motion contained in a video enables us to perceive the dynamic visual world in a novel manner. In the third part, we present an approach, called SuperSloMo, which synthesizes slow-motion videos from a standard frame-rate video. Converting a plain video into a slow-motion version enables us to see memorable moments in our life that are hard to see clearly otherwise with naked eyes: a difficult skateboard trick, a dog catching a ball, etc. Such a technique also has wide applications such as generating smooth view transition on a head-mounted virtual reality (VR) devices, compressing videos, synthesizing videos with motion blur, etc.

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