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Found 8 papers, 1 papers with code
Enhancing Modality-Agnostic Representations via Meta-Learning for Brain Tumor Segmentation
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.
Temporal Context Matters: Enhancing Single Image Prediction with Disease Progression Representations
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.
Attention-based Multi-scale Gated Recurrent Encoder with Novel Correlation Loss for COVID-19 Progression Prediction
This vector is used as an input to a decoder module to predict patch severity grades at a future timepoint.
Facial Micro-Expression Spotting and Recognition using Time Contrasted Feature with Visual Memory
Facial micro-expressions are sudden involuntary minute muscle movements which reveal true emotions that people try to conceal.
Staff line Removal using Generative Adversarial Networks
Staff line removal is a crucial pre-processing step in Optical Music Recognition.
Handwriting Trajectory Recovery using End-to-End Deep Encoder-Decoder Network
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.
Word Level Font-to-Font Image Translation using Convolutional Recurrent Generative Adversarial Networks
We employ a novel convolutional recurrent model architecture in the Generator that efficiently deals with the word images of arbitrary width.
Script Identification in Natural Scene Image and Video Frame using Attention based Convolutional-LSTM Network
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.
