Nov 21, 2013 · We propose a new method which combines part-based models and deep learning by training pose-normalized CNNs. We show substantial improvement vs.

Alignment Networks for Deep Attribute modeling, which augments deep convolutional networks to have input lay- ers based on semantically aligned part patches.

WHY BOTHER WITH ATTRIBUTES? ▻ Attributes enable. ▻ unknown object description. ▻ sample sharing (better generalization). ▻ compressed representations ...

We propose a new method which combines part-based models and deep learning by training pose-normalized CNNs. We show substantial improvement vs. state-of-the- ...

A new method which combines part-based models and deep learning by training pose-normalized CNNs for inferring human attributes from images of people under ...

Pose-‐normalizaQon significantly helps deep. convoluQonal networks in the task of a5ribute classificaQon. • Mid-‐level parts remain important in the context of ...

In this paper, we propose the PANDA model, Pose Alignment Networks for Deep Attribute modeling, which augments deep convolutional networks to have input layers ...

We propose a new method which combines part-based models and deep learning by training pose-normalized CNNs. We show substantial improvement vs. state-of-the- ...

Different parts of the body might have different signal for each attribute. ○ For example:- Deep network trained on person leg patches contain.

Many algorithms has been proposed to handle this task. The goal of this paper is to review existing works using traditional methods or based on deep learning ...