Despite the remarkable progress in person re-identification (Re-ID), such approaches still
suffer from the failure cases where the discriminative body parts are missing. To mitigate this …

This research strives for natural language moment retrieval in long, untrimmed video
streams. The problem is not trivial especially when a video contains multiple moments of …

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 …

In video understanding, most cross-modal knowledge distillation (KD) methods are tailored
for classification tasks, focusing on the discriminative representation of the trimmed videos …

Handling long and complex temporal information is an important factor for action detection
tasks. This challenge is further aggravated by densely distributed actions in untrimmed …

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 …

Designing activity detection systems that can be successfully deployed in daily-living
environments requires datasets that pose the challenges typical of real-world scenarios. In …

Video action recognition is a vital area of computer vision. By adding temporal dimension
into convolution structure, 3D convolution neural network owns the capacity to extract spatio …

Recognizing instances at varying scales simultaneously is a fundamental challenge in
visual detection problems. While spatial multi-scale modeling has been well studied in …

Action detection is an essential and challenging task, especially for densely labelled
datasets of untrimmed videos. There are many real-world challenges in those datasets, such …