Search Results for author: Vignesh Jagadeesh

Found 10 papers, 1 papers with code

Fashion Apparel Detection: The Role of Deep Convolutional Neural Network and Pose-dependent Priors

no code implementations19 Nov 2014 Kota Hara, Vignesh Jagadeesh, Robinson Piramuthu

In this work, we propose and address a new computer vision task, which we call fashion item detection, where the aim is to detect various fashion items a person in the image is wearing or carrying.

object-detection Object Detection

Efficient Media Retrieval from Non-Cooperative Queries

no code implementations19 Nov 2014 Kevin Shih, Wei Di, Vignesh Jagadeesh, Robinson Piramuthu

Text is ubiquitous in the artificial world and easily attainable when it comes to book title and author names.

Optical Character Recognition (OCR) Retrieval +1

ConceptLearner: Discovering Visual Concepts from Weakly Labeled Image Collections

no code implementations CVPR 2015 Bolei Zhou, Vignesh Jagadeesh, Robinson Piramuthu

Discovering visual knowledge from weakly labeled data is crucial to scale up computer vision recognition system, since it is expensive to obtain fully labeled data for a large number of concept categories.

object-detection Object Detection +1

Im2Fit: Fast 3D Model Fitting and Anthropometrics using Single Consumer Depth Camera and Synthetic Data

no code implementations3 Oct 2014 Qiaosong Wang, Vignesh Jagadeesh, Bryan Ressler, Robinson Piramuthu

In this paper, we propose a method for capturing accurate human body shape and anthropometrics from a single consumer grade depth sensor.

Virtual Try-on

When relevance is not Enough: Promoting Visual Attractiveness for Fashion E-commerce

no code implementations13 Jun 2014 Wei Di, Anurag Bhardwaj, Vignesh Jagadeesh, Robinson Piramuthu, Elizabeth Churchill

This study aims to address the effectiveness of types of image in showcasing fashion apparel in terms of its attractiveness, i. e. the ability to draw consumer's attention, interest, and in return their engagement.

Human-Computer Interaction K.4.4; H.2.8

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