Learning Quintuplet Loss for Large-Scale Visual Geolocalization

Large-Scale Image Geolocalization

In this chapter, we explore the task of global image geolocalization— estimating where on the Earth a photograph was captured. We examine variants of the “im2gps” algorithm using millions of “geotagged” Internet photographs as training data. We first discuss a simple to understand nearest-neighbor baseline. Next, we introduce a lazy-learning approach with more sophisticated features that double...

Learning from Large-scale Visual Data for Robots

Learning Compact Visual Attributes for Large-Scale Image Classification

Attributes based image classification has received a lot of attention recently, as an interesting tool to share knowledge across different categories or to produce compact signature of images. However, when high classification performance is expected, state-of-the-art results are typically obtained by combining Fisher Vectors (FV) and Spatial Pyramid Matching (SPM), leading to image signatures ...

Large Scale Visual Recognition

Visual recognition remains one of the grand goals of artificial intelligence research. One major challenge is endowing machines with human ability to recognize tens of thousands of categories. Moving beyond previous work that is mostly focused on hundreds of categories, we make progress toward human scale visual recognition. Specifically, our contributions are as follows: First, we have constru...

Tools for large-scale learning

A batch learning algorithm takes as input the training data and returns a classifier. Later, we’ll also talk about randomized classifiers f ; the probability defining err(f) is then taken with respect to both the internal randomization in f as well as the random draw (x, y) from P. We abstractly think of an online prediction algorithm as taking as input a sequence of n labeled examples, and pro...