In this paper, we propose self-supervised training for video transformers using unlabeled
video data. From a given video, we create local and global spatiotemporal views with …

Videos of animal behavior are used to quantify researcher-defined behaviors of interest to
study neural function, gene mutations, and pharmacological therapies. Behaviors of interest …

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 …

The task of skeleton-based action recognition remains a core challenge in human-centred
scene understanding due to the multiple granularities and large variation in human motion …

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 …

In this paper, we propose a new, simple, and effective Self-supervised Spatio-temporal
Transformers (SPARTAN) approach to Group Activity Recognition (GAR) using unlabeled …

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

Traditional temporal action detection (TAD) usually handles untrimmed videos with small
number of action instances from a single label (eg, ActivityNet, THUMOS). However, this …

We present PAT, a transformer-based network that learns complex temporal co-occurrence
action dependencies in a video by exploiting multi-scale temporal features. In existing …

Temporal action localization (TAL), which involves recognizing and locating action
instances, is a challenging task in video understanding. Most existing approaches directly …