Currently, video semantic segmentation mainly faces two challenges: 1) the demand of
temporal consistency; 2) the balance between segmentation accuracy and inference …

Recent years have witnessed a growing interest in compressed video action recognition due
to the rapid growth of online videos. It remarkably reduces the storage by replacing raw …

Generic event boundary detection aims to localize the generic, taxonomy-free event
boundaries that segment videos into chunks. Existing methods typically require video frames …

Video semantic segmentation (VSS) is a computationally expensive task due to the per-
frame prediction for videos of high frame rates. In recent work, compact models or adaptive …

Point-based object localization (POL), which pursues high-performance object sensing
under low-cost data annotation, has attracted increased attention. However, the point …

Current approaches for human pose estimation in videos can be categorized into per-frame
and warping-based methods. Both approaches have their pros and cons. For example, per …

A tremendous amount of image and video data are being generated and shared in our daily
lives. Image and video data are typically stored and transmitted in compressed form in order …

Weakly supervised multi-class video segmentation is one of the most challenging yet least
studied research problems in computer vision. This study aims to investigate two main …

As an important and challenging problem, multidomain learning (MDL) typically seeks a set
of effective lightweight domain-specific adapter modules plugged into a common domain …

We propose an empirical study to see whether learning with convolutional neural networks
(CNNs) on entropy coded data is possible. First, we define spatial and semantic closeness …