Dynamic neural network is an emerging research topic in deep learning. Compared to static
models which have fixed computational graphs and parameters at the inference stage …

Temporal modeling still remains challenging for action recognition in videos. To mitigate this
issue, this paper presents a new video architecture, termed as Temporal Difference Network …

Temporal modeling is key for action recognition in videos. It normally considers both short-
range motions and long-range aggregations. In this paper, we propose a Temporal …

Video data is with complex temporal dynamics due to various factors such as camera
motion, speed variation, and different activities. To effectively capture this diverse motion …

Spatiotemporal and motion features are two complementary and crucial information for
video action recognition. Recent state-of-the-art methods adopt a 3D CNN stream to learn …

The canonical approach to video action recognition dictates a neural network model to do a
classic and standard 1-of-N majority vote task. They are trained to predict a fixed set of …

Deep learning (DL) approaches are part of the machine learning (ML) subfield concerned
with the development of computational models to train artificial intelligence systems. DL …

Effectively tackling the problem of temporal action localization (TAL) necessitates a visual
representation that jointly pursues two confounding goals, ie, fine-grained discrimination for …

Efficiency is an important issue in designing video architectures for action recognition. 3D
CNNs have witnessed remarkable progress in action recognition from videos. However …

Images or videos always contain multiple objects or actions. Multi-label recognition has
been witnessed to achieve pretty performance attribute to the rapid development of deep …