Search Results for author: Qiaosong Wang

Found 9 papers, 2 papers with code

Mobile Head Tracking for eCommerce and Beyond

1 code implementation18 Dec 2018 Muratcan Cicek, Jinrong Xie, Qiaosong Wang, Robinson Piramuthu

Unlike desktop and laptop computers, they are also much easier to carry indoors and outdoors. To address this, we implement and open source button that is sensitive to head movements tracked from the front camera of iPhone X.

Human-Computer Interaction

Adversarial Learning for Fine-grained Image Search

no code implementations6 Jul 2018 Kevin Lin, Fan Yang, Qiaosong Wang, Robinson Piramuthu

Fine-grained image search is still a challenging problem due to the difficulty in capturing subtle differences regardless of pose variations of objects from fine-grained categories.

Generative Adversarial Network Image Retrieval

Towards the Success Rate of One: Real-time Unconstrained Salient Object Detection

no code implementations31 Jul 2017 Mahyar Najibi, Fan Yang, Qiaosong Wang, Robinson Piramuthu

In this work, we propose an efficient and effective approach for unconstrained salient object detection in images using deep convolutional neural networks.

Object object-detection +2

Visual Search at eBay

no code implementations10 Jun 2017 Fan Yang, Ajinkya Kale, Yury Bubnov, Leon Stein, Qiaosong Wang, Hadi Kiapour, Robinson Piramuthu

We harness the availability of large image collection of eBay listings and state-of-the-art deep learning techniques to perform visual search at scale.

GraB: Visual Saliency via Novel Graph Model and Background Priors

no code implementations CVPR 2016 Qiaosong Wang, Wen Zheng, Robinson Piramuthu

We propose an unsupervised bottom-up saliency detection approach by exploiting novel graph structure and background priors.

Saliency Detection Superpixels

Automatic Layer Separation using Light Field Imaging

no code implementations15 Jun 2015 Qiaosong Wang, Haiting Lin, Yi Ma, Sing Bing Kang, Jingyi Yu

We propose a novel approach that jointly removes reflection or translucent layer from a scene and estimates scene depth.

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.

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