Vision transformers are emerging as a powerful tool to solve computer vision problems.
Recent techniques have also proven the efficacy of transformers beyond the image domain …

Action detection is an essential and challenging task, especially for densely labelled
datasets of untrimmed videos. The temporal relation is complex in those datasets, including …

Abstract Temporal Action Detection (TAD) focuses on detecting pre-defined actions, while
Moment Retrieval (MR) aims to identify the events described by open-ended natural …

Abstract We propose Token Turing Machines (TTM), a sequential, autoregressive
Transformer model with memory for real-world sequential visual understanding. Our model …

We investigate whether self-supervised learning (SSL) can improve online reinforcement
learning (RL) from pixels. We extend the contrastive reinforcement learning framework (eg …

The type and duration of construction workers' activities are useful information for project
management purposes. Therefore, several studies have used surveillance cameras and …

Developing end-to-end models for long-video action understanding tasks presents
significant computational and memory challenges. Existing works generally build models on …

The challenge of long-term video understanding remains constrained by the efficient
extraction of object semantics and the modelling of their relationships for downstream tasks …

Uncertainty estimation of the trained deep learning networks is valuable for optimizing
learning efficiency and evaluating the reliability of network predictions. In this paper, we …

Workers' productivity monitoring is an essential but time-consuming part of large
construction projects. Therefore, automating this process using surveillance cameras has …