Saurabh Gupta

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Learning Rich Features from RGB-D Images for Object Detection and Segmentation

Saurabh Gupta, Ross B. Girshick, Pablo Andrés Arbeláez, Jitendra Malik
ECCV
2014

In this paper we study the problem of object detection for RGB-D images using semantically rich image and depth features. We propose a new geocentric embedding for depth images that encodes height… (More)

From captions to visual concepts and back

Hao Fang, Saurabh Gupta, +9 authors Geoffrey Zweig
2015 IEEE Conference on Computer Vision and…
2015

This paper presents a novel approach for automatically generating image descriptions: visual detectors, language models, and multimodal similarity models learnt directly from a dataset of image… (More)

Microsoft COCO Captions: Data Collection and Evaluation Server

Xinlei Chen, Hao Fang, +4 authors C. Lawrence Zitnick
ArXiv
2015

In this paper we describe the Microsoft COCO Caption dataset and evaluation server. When completed, the dataset will contain over one and a half million captions describing over 330,000 images. For… (More)

Perceptual Organization and Recognition of Indoor Scenes from RGB-D Images

Saurabh Gupta, Pablo Andrés Arbeláez, Jitendra Malik
2013 IEEE Conference on Computer Vision and…
2013

We address the problems of contour detection, bottom-up grouping and semantic segmentation using RGB-D data. We focus on the challenging setting of cluttered indoor scenes, and evaluate our approach… (More)

Technology-Mediated Learning: A Comprehensive Theoretical Model

Saurabh Gupta University of North Florida

Semantic segmentation using regions and parts

Pablo Andrés Arbeláez, Bharath Hariharan, Chunhui Gu, Saurabh Gupta, Lubomir D. Bourdev, Jitendra Malik
2012 IEEE Conference on Computer Vision and…
2012

We address the problem of segmenting and recognizing objects in real world images, focusing on challenging articulated categories such as humans and other animals. For this purpose, we propose a… (More)

Exploring Nearest Neighbor Approaches for Image Captioning

Jacob Devlin, Saurabh Gupta, Ross B. Girshick, Margaret Mitchell, C. Lawrence Zitnick
ArXiv
2015

We explore a variety of nearest neighbor baseline approaches for image captioning. These approaches find a set of nearest neighbor images in the training set from which a caption may be borrowed for… (More)

Language Models for Image Captioning: The Quirks and What Works

Jacob Devlin, Hao Cheng, +5 authors Margaret Mitchell
ACL
2015

Two recent approaches have achieved state-of-the-art results in image captioning. The first uses a pipelined process where a set of candidate words is generated by a convolutional neural network… (More)

Cross Modal Distillation for Supervision Transfer

Saurabh Gupta, Judy Hoffman, Jitendra Malik
2016 IEEE Conference on Computer Vision and…
2016

In this work we propose a technique that transfers supervision between images from different modalities. We use learned representations from a large labeled modality as supervisory signal for… (More)

End-user training methods: what we know, need to know

End-User Training (EUT) has enjoyed a rich tradition of research in Information Systems. However, with the growing pace of change in technology and the dynamic nature of business, organizations are… (More)

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