Crowdsourcing in Computer Vision

Computer vision systems require large amounts of manually annotated data to properly learn challenging visual concepts. Crowdsourcing platforms offer an inexpensive method to capture human knowledge and understanding, for a vast number of visual perception tasks. In this survey, we describe the types of annotations computer vision researchers have collected using crowdsourcing, and how they have ensured that this data is of high quality while annotation effort is minimized. We begin by discussing data collection on both classic (e.g., object recognition) and recent (e.g., visual story-telling) vision tasks. We then summarize key design decisions for creating effective data collection interfaces and workflows, and present strategies for intelligently selecting the most important data instances to annotate. Finally, we conclude with some thoughts on the future of crowdsourcing in computer vision.Comment: A 69-page meta review of the field, Foundations and Trends in Computer Graphics and Vision, 201

Relative Comparison Kernel Learning with Auxiliary Kernels

In this work we consider the problem of learning a positive semidefinite kernel matrix from relative comparisons of the form: "object A is more similar to object B than it is to C", where comparisons are given by humans. Existing solutions to this problem assume many comparisons are provided to learn a high quality kernel. However, this can be considered unrealistic for many real-world tasks since relative assessments require human input, which is often costly or difficult to obtain. Because of this, only a limited number of these comparisons may be provided. In this work, we explore methods for aiding the process of learning a kernel with the help of auxiliary kernels built from more easily extractable information regarding the relationships among objects. We propose a new kernel learning approach in which the target kernel is defined as a conic combination of auxiliary kernels and a kernel whose elements are learned directly. We formulate a convex optimization to solve for this target kernel that adds only minor overhead to methods that use no auxiliary information. Empirical results show that in the presence of few training relative comparisons, our method can learn kernels that generalize to more out-of-sample comparisons than methods that do not utilize auxiliary information, as well as similar methods that learn metrics over objects

Human neuromaturation, juvenile extreme energy liability, and adult cognition/cooperation

Human childhood and adolescence is the period in which adult cognitive competences (including those that create the unique cooperativeness of humans) are acquired. It is also a period when neural development puts a juvenile’s survival at risk due to the high vulnerability of their brain to energy shortage. The brain of a 4 year-old human uses ≈50% of its total energy expenditure (TEE) (cf. adult ≈12%). This brain expensiveness is due to (1) the brain making up ≈6% of a 4 year-old body compared to 2% in an adult, and (2) increased energy metabolism that is ≈100% greater in the gray matter of a child than in an adult (a result of the extra costs of synaptic neuromaturation). The high absolute number of neurons in the human brain requires as part of learning a prolonged neurodevelopment. This refines inter- and intraarea neural networks so they become structured with economical “small world” connectivity attributes (such as hub organization and high cross-brain differentiation/integration). Once acquired, this connectivity enables highly complex adult cognitive capacities. Humans evolved as hunter-gatherers. Contemporary hunter-gatherers (and it is also likely Middle Paleolithic ones) pool high energy foods in an egalitarian manner that reliably supported mothers and juveniles with high energy intake. This type of sharing unique to humans protects against energy shortage happening to the immature brain. This cooperation that protects neuromaturation arises from adults having the capacity to communicate and evaluate social reputation, cognitive skills that exist as a result of extended neuromaturation. Human biology is therefore characterized by a presently overlooked bioenergetic-cognition loop (called here the “HEBE ring”) by which extended neuromaturation creates the cooperative abilities in adults that support juveniles through the potentially vulnerable period of the neurodevelopment needed to become such adults