no code implementations • 14 Feb 2023 • Laxman Kumarapu, Shiv Ram Dubey, Snehasis Mukherjee, Parkhi Mohan, Sree Pragna Vinnakoti, Subhash Karthikeya
In order to alleviate this problem and to facilitate the research on selfie face images, we develop a challenging Wild Selfie Dataset (WSD) where the images are captured from the selfie cameras of different smart phones, unlike existing datasets where most of the images are captured in controlled environment.
1 code implementation • 16 Oct 2021 • Viswanatha Reddy G, Chaitanya B S N V, Prathyush P, Sumanth M, Mrinalini C, Dileep Kumar P, Snehasis Mukherjee
The over-saturation of content on social media platforms has persuaded us to identify the important factors that affect content popularity.
no code implementations • 30 Jan 2021 • S. H. Shabbeer Basha, Mohammad Farazuddin, Viswanath Pulabaigari, Shiv Ram Dubey, Snehasis Mukherjee
First, we train the model and select the filter pairs with redundant filters in each pair.
no code implementations • 7 Dec 2020 • Viswanatha Reddy Gajjala, Sai Prasanna Teja Reddy, Snehasis Mukherjee, Shiv Ram Dubey
The proposed model takes advantage of spatial-temporal attention and channel attention together, to learn deeper fine-grained subtle features for classification of emotions.
1 code implementation • 25 Apr 2020 • S. H. Shabbeer Basha, Sravan Kumar Vinakota, Viswanath Pulabaigari, Snehasis Mukherjee, Shiv Ram Dubey
The experimental results obtained in this study depict that tuning of the pre-trained CNN layers with the knowledge from the target dataset confesses better transfer learning ability.
no code implementations • 6 Feb 2020 • S. H. Shabbeer Basha, Viswanath Pulabaigari, Snehasis Mukherjee
Traditionally in deep learning based human activity recognition approaches, either a few random frames or every $k^{th}$ frame of the video is considered for training the 3D CNN, where $k$ is a small positive integer, like 4, 5, or 6.
no code implementations • 22 Jan 2020 • S. H. Shabbeer Basha, Sravan Kumar Vinakota, Shiv Ram Dubey, Viswanath Pulabaigari, Snehasis Mukherjee
Fine-tuning the newly learned (target-dependent) FC layers leads to state-of-the-art performance, according to the experiments carried out in this research.
1 code implementation • 12 Sep 2019 • Shiv Ram Dubey, Soumendu Chakraborty, Swalpa Kumar Roy, Snehasis Mukherjee, Satish Kumar Singh, Bidyut Baran Chaudhuri
In this paper, a novel optimizer is proposed based on the difference between the present and the immediate past gradient (i. e., diffGrad).
1 code implementation • 27 Aug 2019 • Yash Srivastava, Vaishnav Murali, Shiv Ram Dubey, Snehasis Mukherjee
The Visual Question Answering (VQA) task combines challenges for processing data with both Visual and Linguistic processing, to answer basic `common sense' questions about given images.
1 code implementation • 25 Apr 2019 • Ravi Kumar Thakur, Snehasis Mukherjee
The problem of Scene flow estimation in depth videos has been attracting attention of researchers of robot vision, due to its potential application in various areas of robotics.
1 code implementation • 27 Mar 2019 • Sai Prasanna Teja Reddy, Surya Teja Karri, Shiv Ram Dubey, Snehasis Mukherjee
This paper proposes two 3D-CNN methods: MicroExpSTCNN and MicroExpFuseNet, for spontaneous facial micro-expression recognition by exploiting the spatiotemporal information in CNN framework.
Facial Expression Recognition Micro Expression Recognition +1
1 code implementation • 21 Jan 2019 • S. H. Shabbeer Basha, Shiv Ram Dubey, Viswanath Pulabaigari, Snehasis Mukherjee
To automate the process of learning a CNN architecture, this paper attempts at finding the relationship between Fully Connected (FC) layers with some of the characteristics of the datasets.
no code implementations • 30 Sep 2018 • Shiv Ram Dubey, Snehasis Mukherjee
Multiple face recognition in unconstrained environment is a challenging task, due to the variation of view-point, scale, pose, illumination and expression of the face images.
1 code implementation • 30 Sep 2018 • S. H. Shabbeer Basha, Soumen Ghosh, Kancharagunta Kishan Babu, Shiv Ram Dubey, Viswanath Pulabaigari, Snehasis Mukherjee
The results of the proposed RCCNet model are compared with five state-of-the-art CNN models in terms of the accuracy, weighted average F1 score and training time.
no code implementations • 26 Aug 2018 • Kushal Borkar, Snehasis Mukherjee
This paper proposes a novel technique for single image dehazing.
1 code implementation • 10 Jul 2018 • Ravi Kumar Thakur, Snehasis Mukherjee
This paper introduces a first effort to apply a deep learning method for direct estimation of scene flow by presenting a fully convolutional neural network with an encoder-decoder (ED) architecture.
no code implementations • 9 Jul 2018 • Snehasis Mukherjee, Leburu Anvitha, T. Mohana Lahari
The proposed method aims in capturing the motion information of the whole video by producing a dynamic image corresponding to the input video.
1 code implementation • 17 Apr 2018 • Ashish Verma, Kranthi Koukuntla, Rohit Varma, Snehasis Mukherjee
The dataset contains some images captured by professional photographers and the rest of the images captured by common people.
no code implementations • 28 Feb 2018 • Shiv Ram Dubey, Snehasis Mukherjee
The proposed local directional order pattern (LDOP) for a particular pixel is computed by finding the relationship between the center pixel and local directional order indexes.