no code implementations • 25 Oct 2024 • Syed Sameen Ahmad Rizvi, Aryan Seth, Pratik Narang
This work leverages representation learning based on latent spaces to mitigate bias in facial expression recognition systems, thereby enhancing a deep learning model's fairness and overall accuracy.
Facial Expression Recognition Facial Expression Recognition (FER) +2
no code implementations • 4 Aug 2024 • Dwij Mehta, Aditya Mehta, Pratik Narang
This paper proposes a novel facial swapping module, termed as LDFaceNet (Latent Diffusion based Face Swapping Network), which is based on a guided latent diffusion model that utilizes facial segmentation and facial recognition modules for a conditioned denoising process.
no code implementations • 30 Sep 2023 • Syed Sameen Ahmad Rizvi, Preyansh Agrawal, Jagat Sesh Challa, Pratik Narang
In this work, we present InFER, a real-world multi-ethnic Indian Facial Expression Recognition dataset consisting of 10, 200 images and 4, 200 short videos of seven basic facial expressions.
Facial Expression Recognition Facial Expression Recognition (FER)
1 code implementation • 13 Jan 2023 • Esha Pahwa, Achleshwar Luthra, Pratik Narang
It is thus crucial to form a solution that can result in a high-quality image and is efficient enough to be deployed for everyday use.
1 code implementation • 12 Jul 2022 • Vikram S Chundawat, Ayush K Tarun, Murari Mandal, Mukund Lahoti, Pratik Narang
We present several baseline models for comparative analysis of the proposed evaluation metric with existing generative models.
no code implementations • 10 Feb 2021 • Harsh Sinha, Aditya Mehta, Murari Mandal, Pratik Narang
We incorporate a self-supervision and a spectral profile regularization network to infer a plausible HSI from an RGB image.
no code implementations • 10 Feb 2021 • Prateek Garg, Murari Mandal, Pratik Narang
Low light conditions in aerial images adversely affect the performance of several vision based applications.
no code implementations • 7 Nov 2020 • Aditya Mehta, Harsh Sinha, Murari Mandal, Pratik Narang
The performance of SkyGAN is evaluated on the recent SateHaze1k dataset as well as the HAI dataset.
no code implementations • 7 May 2020 • Codruta O. Ancuti, Cosmin Ancuti, Florin-Alexandru Vasluianu, Radu Timofte, Jing Liu, Haiyan Wu, Yuan Xie, Yanyun Qu, Lizhuang Ma, Ziling Huang, Qili Deng, Ju-Chin Chao, Tsung-Shan Yang, Peng-Wen Chen, Po-Min Hsu, Tzu-Yi Liao, Chung-En Sun, Pei-Yuan Wu, Jeonghyeok Do, Jongmin Park, Munchurl Kim, Kareem Metwaly, Xuelu Li, Tiantong Guo, Vishal Monga, Mingzhao Yu, Venkateswararao Cherukuri, Shiue-Yuan Chuang, Tsung-Nan Lin, David Lee, Jerome Chang, Zhan-Han Wang, Yu-Bang Chang, Chang-Hong Lin, Yu Dong, Hong-Yu Zhou, Xiangzhen Kong, Sourya Dipta Das, Saikat Dutta, Xuan Zhao, Bing Ouyang, Dennis Estrada, Meiqi Wang, Tianqi Su, Siyi Chen, Bangyong Sun, Vincent Whannou de Dravo, Zhe Yu, Pratik Narang, Aryan Mehra, Navaneeth Raghunath, Murari Mandal
We focus on the proposed solutions and their results evaluated on NH-Haze, a novel dataset consisting of 55 pairs of real haze free and nonhomogeneous hazy images recorded outdoor.