Humans drive in a holistic fashion which entails, in particular, understanding dynamic road
events and their evolution. Injecting these capabilities in autonomous vehicles can thus take …

Spatiotemporal action localization requires the incorporation of two sources of information
into the designed architecture:(1) temporal information from the previous frames and (2) …

We consider the problem of predicting the future trajectory of scene agents from egocentric
views obtained from a moving platform. This problem is important in a variety of domains …

Behavior is an important indicator for understanding the well-being of animals. This process
has been frequently carried out by observing video records to detect changes with statistical …

Despite the advances in Human Activity Recognition, the ability to exploit the dynamics of
human body motion in videos has yet to be achieved. In numerous recent works …

In this work, we propose two Deep Neural Networks, DNN-1 and DNN-2, based on residual
Fast-Slow Refined Highway (FSRH) and Global Atomic Spatial Attention (GASA) to …

Current methods for spatiotemporal action tube detection often extend a bounding box
proposal at a given key-frame into a 3D temporal cuboid and pool features from nearby …

Recognizing actions from limited preliminary video observations has seen considerable
recent progress. Typically, however, such progress has been had without explicitly modeling …

We investigate the joint anticipation of long-term activity labels and their corresponding
times with the aim of improving both the naturalness and diversity of predictions. We address …

The task of spatio-temporal action localization (STAL) needs to detect the action and
position of individuals in the scene. Many works cannot model spatio-temporal information …