no code implementations • ICCV 2023 • Aishik Konwer, Xiaoling Hu, Joseph Bae, Xuan Xu, Chao Chen, Prateek Prasanna
We propose a novel approach to learn enhanced modality-agnostic representations by employing a meta-learning strategy in training, even when only limited full modality samples are available.
no code implementations • CVPR 2022 • Aishik Konwer, Xuan Xu, Joseph Bae, Chao Chen, Prateek Prasanna
In our method, a self-attention based Temporal Convolutional Network (TCN) is used to learn a representation that is most reflective of the disease trajectory.
no code implementations • 18 Jul 2021 • Aishik Konwer, Joseph Bae, Gagandeep Singh, Rishabh Gattu, Syed Ali, Jeremy Green, Tej Phatak, Prateek Prasanna
This vector is used as an input to a decoder module to predict patch severity grades at a future timepoint.
no code implementations • 9 Feb 2019 • Sauradip Nag, Ayan Kumar Bhunia, Aishik Konwer, Partha Pratim Roy
Facial micro-expressions are sudden involuntary minute muscle movements which reveal true emotions that people try to conceal.
no code implementations • 22 Jan 2018 • Ankan Kumar Bhunia, Ayan Kumar Bhunia, Prithaj Banerjee, Aishik Konwer, Abir Bhowmick, Partha Pratim Roy, Umapada Pal
We employ a novel convolutional recurrent model architecture in the Generator that efficiently deals with the word images of arbitrary width.
no code implementations • 22 Jan 2018 • Aishik Konwer, Ayan Kumar Bhunia, Abir Bhowmick, Ankan Kumar Bhunia, Prithaj Banerjee, Partha Pratim Roy, Umapada Pal
Staff line removal is a crucial pre-processing step in Optical Music Recognition.
no code implementations • 22 Jan 2018 • Ayan Kumar Bhunia, Abir Bhowmick, Ankan Kumar Bhunia, Aishik Konwer, Prithaj Banerjee, Partha Pratim Roy, Umapada Pal
Our encoder module consists of Convolutional LSTM network, which takes an offline character image as the input and encodes the feature sequence to a hidden representation.
1 code implementation • 1 Jan 2018 • Ankan Kumar Bhunia, Aishik Konwer, Ayan Kumar Bhunia, Abir Bhowmick, Partha P. Roy, Umapada Pal
In this paper, we propose a novel method that involves extraction of local and global features using CNN-LSTM framework and weighting them dynamically for script identification.