Leveraging Predictions of Task-Related Latents for Interactive Visual Navigation.

Interactive visual navigation (IVN) involves tasks where embodied agents learn to interact with the objects in the environment to reach the goals. Current approaches exploit visual features to train a reinforcement learning (RL) navigation control policy network. However, RL-based methods continue to struggle at the IVN tasks as they are inefficient in learning a good representation of the unknown environment in partially observable settings.

A few-shot segmentation method for prohibited item inspection.

Background: With the rapid development of deep learning, several neural network models have been proposed for automatic segmentation of prohibited items. These methods usually based on a substantial amount of labelled training data. However, for some prohibited items of rarely appearing, it is difficult to obtain enough labelled samples.