no code implementations • 30 Apr 2024 • Yuekun Dai, Dafeng Zhang, Xiaoming Li, Zongsheng Yue, Chongyi Li, Shangchen Zhou, Ruicheng Feng, Peiqing Yang, Zhezhu Jin, Guanqun Liu, Chen Change Loy, Lize Zhang, Shuai Liu, Chaoyu Feng, Luyang Wang, Shuan Chen, Guangqi Shao, Xiaotao Wang, Lei Lei, Qirui Yang, Qihua Cheng, Zhiqiang Xu, Yihao Liu, Huanjing Yue, Jingyu Yang, Florin-Alexandru Vasluianu, Zongwei Wu, George Ciubotariu, Radu Timofte, Zhao Zhang, Suiyi Zhao, Bo wang, Zhichao Zuo, Yanyan Wei, Kuppa Sai Sri Teja, Jayakar Reddy A, Girish Rongali, Kaushik Mitra, Zhihao Ma, Yongxu Liu, Wanying Zhang, Wei Shang, Yuhong He, Long Peng, Zhongxin Yu, Shaofei Luo, Jian Wang, Yuqi Miao, Baiang Li, Gang Wei, Rakshank Verma, Ritik Maheshwari, Rahul Tekchandani, Praful Hambarde, Satya Narayan Tazi, Santosh Kumar Vipparthi, Subrahmanyam Murala, Haopeng Zhang, Yingli Hou, Mingde Yao, Levin M S, Aniruth Sundararajan, Hari Kumar A
The increasing demand for computational photography and imaging on mobile platforms has led to the widespread development and integration of advanced image sensors with novel algorithms in camera systems.
no code implementations • 23 Mar 2023 • Monu Verma, Murari Mandal, Satish Kumar Reddy, Yashwanth Reddy Meedimale, Santosh Kumar Vipparthi
In this paper, we proposed to search for a highly efficient and robust neural architecture for both macro and micro-level facial expression recognition.
no code implementations • 10 Oct 2022 • Monu Verma, Santosh Kumar Vipparthi, Girdhari Singh
Therefore, this paper aims to provide a deep insight into the DL-based MER frameworks with a perspective on promises in network model designing, experimental strategies, challenges, and research needs.
no code implementations • 17 May 2022 • Monu Verma, Santosh Kumar Vipparthi
Inspired from the assets of handcrafted and deep learning approaches, we proposed a RARITYNet: RARITY guided affective emotion learning framework to learn the appearance features and identify the emotion class of facial expressions.
no code implementations • 16 Jan 2022 • Monu Verma, Prafulla Saxena, Santosh Kumar Vipparthi, Girdhari Singh
CRIP encodes the transitional pattern of a facial expression by incorporating cross-centroid relationship between two ripples located at radius r1 and r2 respectively.
Facial Expression Recognition Facial Expression Recognition (FER)
no code implementations • 15 May 2021 • Monu Verma, Ayushi Gupta, Santosh Kumar Vipparthi
The FineFeat module extracts fine grained feature maps by employing attention mechanism over multiscale receptive fields.
no code implementations • 4 May 2021 • Murari Mandal, Santosh Kumar Vipparthi
To the best of our knowledge, this is a first attempt to comparatively analyze the different evaluation frameworks used in the existing deep change detection methods.
no code implementations • 4 Aug 2020 • Murari Mandal, Lav Kush Kumar, Santosh Kumar Vipparthi
Therefore, in this paper, we introduce MOR-UAV, a large-scale video dataset for MOR in aerial videos.
no code implementations • 16 May 2020 • Monu Verma, Santosh Kumar Vipparthi, Girdhari Singh
However, existing networks fail to establish a relationship between spatial features of facial appearance and temporal variations of facial dynamics.
no code implementations • WACV 2020 • Murari Mandal, Lav Kush Kumar, Mahipal Singh Saran, Santosh Kumar Vipparthi
To the best of our knowledge, this is a first attempt for simultaneous localization and classification of moving objects in a video, i. e. MOR in a single-stage deep learning framework.
no code implementations • 26 Dec 2019 • Murari Mandal, Vansh Dhar, Abhishek Mishra, Santosh Kumar Vipparthi
In this paper we propose an end-to-end swift 3D feature reductionist framework (3DFR) for scene independent change detection.
1 code implementation • 6 Dec 2019 • Shivangi Dwivedi, Murari Mandal, Shekhar Yadav, Santosh Kumar Vipparthi
Our work chalks a comparative study with the existing methods employed for abstracting deeper features and propose a model which incorporates residual features from multiple stages in the network.
no code implementations • 31 Aug 2019 • Murari Mandal, Manal Shah, Prashant Meena, Santosh Kumar Vipparthi
Detection of small-sized targets is of paramount importance in many aerial vision-based applications.
no code implementations • 17 Jul 2019 • Murari Mandal, Manal Shah, Prashant Meena, Sanhita Devi, Santosh Kumar Vipparthi
Detection of small-sized targets in aerial views is a challenging task due to the smallness of vehicle size, complex background, and monotonic object appearances.
no code implementations • 11 Jun 2019 • Kuldeep Marotirao Biradar, Ayushi Gupta, Murari Mandal, Santosh Kumar Vipparthi
In this paper, we present a three-stage pipeline to learn the motion patterns in videos to detect a visual anomaly.
no code implementations • 20 Apr 2019 • Monu Verma, Santosh Kumar Vipparthi, Girdhari Singh, Subrahmanyam Murala
We also propose a Lateral Accretive Hybrid Network (LEARNet) to capture micro-level features of an expression in the facial region.
no code implementations • 19 Apr 2018 • Murari Mandal, Prafulla Saxena, Santosh Kumar Vipparthi, Subrahmanyam Murala
Background subtraction in video provides the preliminary information which is essential for many computer vision applications.