Topic models have been widely used to learn text representations and gain insight into document corpora. To perform topic discovery, …

One-shot image segmentation (OSS) methods enable semantic labeling of image pixels without supervised training using an extensive …

Image-based virtual try-on involves synthesizing perceptually convincing images of a model wearing a particular garment and has …

Reinforcement Learning (RL) has recently been applied to sequential estimation and prediction problems identifying and developing …

Interpretability is an emerging area of research in trustworthy machine learning. Safe deployment of machine learning system mandates …

Few-shot learning algorithms aim to learn model parameters capable of adapting to unseen classes with the help of only a few labeled …

Structure extraction from document images has been a long-standing research topic due to its high impact on a wide range of practical …

As deep reinforcement learning (RL) is applied to more tasks, there is a need to visualize and understand the behavior of learned …

Document structure extraction has been a widely researched area for decades with recent works performing it as a semantic segmentation …

Visual Speech Recognition (VSR) is the process of recognizing or interpreting speech by watching the lip movements of the speaker. …