Elahe Vahdani

I'm a Ph.D. student in the Media Lab, Dept. Computer Science at The City University of New York, advised by Professor Ying-Li Tian. My current research focuses on Video Understanding, Multimodal Understanding, and Action Detection. I received my Bachelor's degree from Sharif University of Technology.

Email / Google Scholar / Curriculum Vitae / LinkedIn

News:

Our paper "Deep Learning-based Action Detection in Untrimmed Videos: A Survey" was accepted to TPAMI 2022.

Excited to join Dataminr as a Research Science Intern for Fall 2021.

Our paper "Cross-modal Center Loss" was accepted to CVPR 2021!

Excited to join Expedia Group as a Data Science Intern for Summer 2021.

Received the M.Phil degree (Master of Philosophy) in Computer Science, The City University of New York.

Advanced to Ph.D. candidacy.

Research

I'm a PhD candidate in computer vision and my research interests are video understanding, multimodal understanding, and action detection. I have also researched on self-supervised feature learning, facial expression analysis, object detection with cross-modality bridging, and vehicle re-identification.

	Deep Learning-based Action Detection in Untrimmed Videos: A Survey Elahe Vahdani*, Yingli Tian TPAMI, 2022 PDF An extensive overview of deep learning-based algorithms to tackle temporal action detection in untrimmed videos with different supervision levels.
	Cross-modal Center Loss Longlong Jing, Elahe Vahdani, Jiaxing Tan, Yingli Tian CVPR 2021 PDF We propose a novel cross-modal center loss to map the representations of different settings of modalities (e.g., images, mesh, point-cloud) into a common feature space.
	Recognizing American Sign Language Nonmanual Signal Grammar Errors in Continuous Videos Elahe Vahdani, Longlong Jing, Yingli Tian, Matt Huenerfaut ICPR , 2020 PDF We designed an educational tool for sign language students to automatically process their signing video assignments and send them an immediate feedback regarding the fluency of their signing. The framework is based on deep-learning algorithms for temporal detection of grammatically important elements from continuous signing videos, and checking their correspondence in multiple modalities such as facial expression, head movements and hand gestures.
	Recognizing American Sign Language Manual Signs from RGB-D Videos Longlong Jing, Elahe Vahdani, Yingli Tian, Matt Huenerfaut Under Review , 2020 PDF / Project Page / Dataset / We propose a 3D ConvNet based multi-stream framework to recognize American Sign Language (ASL) manual signs in real-time from RGB-D videos.
	An Isolated-Signing RGBD Dataset of 100 American Sign Language Signs Produced by Fluent ASL Signers Saad Hassan, Larwan Berke, Elahe Vahdani, Longlong Jing, Yingli Tian, Matt Huenerfaut LREC , 2020 PDF/ Project Page / Dataset / We have collected a new dataset consisting of color and depth videos of fluent American Sign Language (ASL) signers performing sequences of 100 ASL signs from a Kinect v2 sensor.
	Multi-camera Vehicle Tracking and Re-identification on AI City Challenge 2019 Yucheng Chen, Longlong Jing, Elahe Vahdani, Ling Zhang, Mingyi He, Yingli Tian CVPR AI City Workshop, 2019 PDF / Slides / Poster Our solutions to the image-based vehicle re-identification track and multi-camera vehicle tracking track on AI City Challenge 2019 (AIC2019). Our proposed framework outperforms the current state-of-the-art vehicle ReID method by 16.3% on Veri dataset.
	Gathering Information in Sensor Networks for Synchronized Freshness Elahe Vahdani, Amotz Bar-Noy, Matthew P. Johnson, Tarek Abdelzaher IEEE SECON, 2017 PDF Designed an approximation algorithm for the optimization problem of scheduling a set of n given jobs with their specific deadlines via a minimum number of channels in a sensor network. We proved the problem is NP-hard and provided a O(log n)- approximation algorithm.