This work explores scene graphs as a distilled representation of high-level information for
autonomous driving, applied to future driver-action prediction. Given the scarcity and strong …

Explainable machine learning and artificial intelligence models have been used to justify a
model's decision-making process. This added transparency aims to help improve user …

Data augmentation is critical for deep learning-based human activity recognition (HAR)
systems. However, conventional data augmentation methods, such as random-cropping …

Visual adversarial attacks inspired by Carlini-Wagner targeted audiovisual attacks can fool
the state-of-the-art Google DeepMind LipNet model to subtitle anything with over 99 …

Action recognition has been achieved great progress in recent years because of better
feature representation learning and classification technology like convolutional neural …

In this work, we propose a committee of attention models developed for improving the deep
reinforcement learning frequently used for games. The game environment is manifested with …

One of the long-standing challenges for reinforcement learning agents is to deal with noisy
environments. Although progress has been made in producing an agent capable of …

Abstract Recent advancements in Artificial Intelligence (AI) necessitate designing systems
wary of users and stakeholders. People from different backgrounds encounter intelligent …

Learning spatiotemporal features is very effective but challenging for video understanding
especially action recognition. In this paper, we propose Multi-Group Multi-Attention, dubbed …

El reconocimiento de acciones en videos es actualmente un tema de interés en el área de
la visión por computador, debido a potenciales aplicaciones como: indexación multimedia …