Video object segmentation has attracted remarkable attention since it is more and more critical in real video understanding scenarios. Raw videos have very high redundancies. Therefore, using a heavy backbone network to extract features from all individual frames may be a waste of time. Also, the motion vectors and residuals in compressed videos provide motion information that can be utilized directly. Therefore, this thesis will discuss semi-supervised video object segmentation methods working directly on compressed videos. First, we discuss a supervised learning method for semi-supervised video object segmentation on compressed videos. To reduce the running time of the model, we design to only use a heavy backbone network for several k...
Object detection and segmentation are important computer vision problems that have applications in s...
This paper proposes a new framework for semantic segmentation of objects in videos. We address the l...
We propose a novel approach to boost the performance of generic object detectors on videos by learni...
The advent of deep learning has brought about great progress on many funda- mental computer vision t...
We present a compressed domain video object segmentation method for the MPEG encoded video sequences...
Video object segmentation (VOS) is a highly challenging problem since the initial mask, defining the...
Semi-supervised video object segmentation is a task of propagating instance masks given in the first...
This paper addresses the problem of extracting video objects from MPEG compressed video. The only cu...
Object detection and segmentation are some of the key components of Computer Vision, which have wide...
International audienceWe study the problem of segmenting moving objects in unconstrained videos. Giv...
In robot sensing and automotive driving domains, producing precise semantic segmentation masks for ...
This Ph.D. thesis offers a perspective of the theory, applications, and implementation of motion mod...
This paper tackles the task of semi-supervised video object segmentation, i.e., the separation of an...
Much progress has been made in image and video segmentation over the last years. To a large extent, ...
Video object segmentation is the task of estimating foreground object segments from the background t...
Object detection and segmentation are important computer vision problems that have applications in s...
This paper proposes a new framework for semantic segmentation of objects in videos. We address the l...
We propose a novel approach to boost the performance of generic object detectors on videos by learni...
The advent of deep learning has brought about great progress on many funda- mental computer vision t...
We present a compressed domain video object segmentation method for the MPEG encoded video sequences...
Video object segmentation (VOS) is a highly challenging problem since the initial mask, defining the...
Semi-supervised video object segmentation is a task of propagating instance masks given in the first...
This paper addresses the problem of extracting video objects from MPEG compressed video. The only cu...
Object detection and segmentation are some of the key components of Computer Vision, which have wide...
International audienceWe study the problem of segmenting moving objects in unconstrained videos. Giv...
In robot sensing and automotive driving domains, producing precise semantic segmentation masks for ...
This Ph.D. thesis offers a perspective of the theory, applications, and implementation of motion mod...
This paper tackles the task of semi-supervised video object segmentation, i.e., the separation of an...
Much progress has been made in image and video segmentation over the last years. To a large extent, ...
Video object segmentation is the task of estimating foreground object segments from the background t...
Object detection and segmentation are important computer vision problems that have applications in s...
This paper proposes a new framework for semantic segmentation of objects in videos. We address the l...
We propose a novel approach to boost the performance of generic object detectors on videos by learni...