Where to Look and How to Describe: Fashion Image Retrieval With an Attentional Heterogeneous Bilinear Network

a comparison of teachers and supervisors, with respect to teacher efficacy and reflection

supervisors play an undeniable role in training teachers, before starting their professional experience by preparing them, at the initial years of their teaching by checking their work within the proper framework, and later on during their teaching by assessing their progress. but surprisingly, exploring their attributes, professional demands, and qualifications has remained a neglected theme i...

An Inference Network Approach to Image Retrieval

Most image retrieval systems only allow a fragment of text or an example image as a query. Most users have more complex information needs that are not easily expressed in either of these forms. This paper proposes a model based on the Inference Network framework from information retrieval that employs a powerful query language that allows structured query operators, term weighting, and the comb...

NIST: An Image Classification Network to Image Semantic Retrieval

This paper proposes a classification network to image semantic retrieval (NIST) framework to counter the image retrieval challenge. Our approach leverages the successful classification network GoogleNet based on Convolutional Neural Networks to obtain the semantic feature matrix which contains the serial number of classes and corresponding probabilities. Compared with traditional image retrieva...

How do we know where to look ? Commentary on Chukoskie

Where to look next and what to look for

In [Sch 96c] we have introduced the use of Multidimensional Receptive Field Histograms for Probabilistic Object Recognition. In this paper we reverse the object recognition problem by asking the question, "where should we look?", when we want to verify the presence of an object, to track an object or to actively explore a scene. This paper describes the statistical framework from which we obtai...