no code implementations • 13 May 2023 • Pirazh Khorramshahi, Zhe Wu, Tianchen Wang, Luke DeLuccia, Hongcheng Wang
Despite recent advances in video-based action recognition and robust spatio-temporal modeling, most of the proposed approaches rely on the abundance of computational resources to afford running huge and computation-intensive convolutional or transformer-based neural networks to obtain satisfactory results.
no code implementations • 16 May 2022 • Pirazh Khorramshahi, Vineet Shenoy, Rama Chellappa
As Computer Vision technologies become more mature for intelligent transportation applications, it is time to ask how efficient and scalable they are for large-scale and real-time deployment.
no code implementations • 15 Apr 2022 • Pirazh Khorramshahi, Vineet Shenoy, Michael Pack, Rama Chellappa
Multi-camera vehicle tracking is one of the most complicated tasks in Computer Vision as it involves distinct tasks including Vehicle Detection, Tracking, and Re-identification.
no code implementations • 13 Oct 2021 • Hossein Souri, Pirazh Khorramshahi, Chun Pong Lau, Micah Goldblum, Rama Chellappa
The adversarial attack literature contains a myriad of algorithms for crafting perturbations which yield pathological behavior in neural networks.
no code implementations • 24 Sep 2020 • Pirazh Khorramshahi, Hossein Souri, Rama Chellappa, Soheil Feizi
To tackle this issue, we take an information-theoretic approach and maximize a variational lower bound on the entropy of the generated samples to increase their diversity.
no code implementations • ECCV 2020 • Pirazh Khorramshahi, Neehar Peri, Jun-Cheng Chen, Rama Chellappa
In recent years, the research community has approached the problem of vehicle re-identification (re-id) with attention-based models, specifically focusing on regions of a vehicle containing discriminative information.
1 code implementation • ICCV 2019 • Pirazh Khorramshahi, Amit Kumar, Neehar Peri, Sai Saketh Rambhatla, Jun-Cheng Chen, Rama Chellappa
In this paper, we present a novel dual-path adaptive attention model for vehicle re-identification (AAVER).
Vehicle Key-Point and Orientation Estimation Vehicle Re-Identification