July 2019: Excited to present our paper on event detection using multi-aspect attention at the DLRL Summer School in Edmonton, Canada!
July 2019: Featured in DAC News!
May 2019: I'll be spending another wonderful summer at Netflix working on Neural Machine Translation. Stay tuned for our paper!
Jan 2019: 1 paper accepted to The Web Conference 2019 !
Jan 2019: Best paper award at IUI 2019 !
May 2018: I'll be spending the summer at Netflix working on Neural Machine Translation hosted by Ritwik Kumar, Boris Chen and Vinith Misra.
Aug 2017: Our submission for automatic narrative generation from a large collection of documents selected for the final round of ODNI Xpress Challenge !
April 2017: I'll be joining Discovery Analytics Centre as a PhD student in summer !
April 2017: The undergraduate students that I supervised and worked with on the PhotoSleuth project won 1st place at VTURCS Spring Symposium.
Nov 2016: Presented our paper and poster on understanding human performance in image geolocation at the GroupSight workshop at HCOMP 2016 !
June 2016: Student Volunteer at CVPR 2016 at Las Vegas, Nevada. Bio.
I am a PhD student in the Computer Science department at Virginia Tech's College of Engineering, working with Prof. Naren Ramakrishnan.
My research falls under the umbrella of Natural Language Processing, Deep Learning and Data Mining. I'm interested in developing AI systems for natural language understanding and generation. I also love developing Django webapps.
My research is graciously supported by DARPA and NSF.
Event Detection Using Hierarchical Multi-Aspect Attention
Event encoding can be viewed as a hierarchical task where the coarser level task is event detection and the fine-grained task is one of event encoding. In this work we present a novel attention mechanism and show its effectivenes on the event detection task when plugged into hierarchical attention models.
Sneha Mehta, M. Raihanul, Huzefa Rangwala, Naren Ramakrishnan
An Exploratory Study of Human Performance in Image Geolocation
We perform an exploratory study of image geolocation tasks performed by novice and expert humans on a diverse image dataset we developed.
Our findings include a model of sensemaking strategies, a taxonomy of image clues, and key challenges and design ideas for image sensemaking and crowdsourcing.
We study the performance of a siamese network based deep neural architecture on the task of binary(Yes/No) visual question answering. Comparing the performance of a siamese network based VQA model to a non-siamese VQA model we find that having a pairwise loss helps perform better than a loss from a non-siamese VQA network.
We develop the topic analysis component of a robust Information Retrieval system for search and retrieval of large-scale tweets and webpages built on top of Solr, a general purpose open-source search engine. Our contribution enables semantic search and retrieval of tweets and webpages based on topics.
We employ a random forest approach for bird species identification. First we train 25 SVMs to predict 25 bird features for our dataset images. Then we train a random-forest to identify the specific bird species. We used the CUB-200-2011 bird dataset for this task.
Seam-carving is a dynamic programming based algorithm for content-aware image resizing developed by Ariel Shamir. In the above example, the algorithm reduces the width of the image by removing vertical seams of pixels between the girl and the cliff edges. > Code
Creating image panorama by image warping based on homographies. In the first example (left), the image on the bottom is the panorama created by warping (by computing a homography matrix) and stitching together the three images above.
In the second example(right), a photo of mine (a really embarrasing one now I realize) was warped and merged onto a billboard in a Times Square image. Find code and more examples > Code
Presenting at GroupSight (Workshop on Human Computation for Image and Video Analysis), HCOMP 2016 at Austin, Texas.