Segmentation of objects in image sequences is very important in many aspects of multimedia applications. In the second generation image/video coding, images are segmented into objects to achieve efficient compression by coding the contour and texture separately. As the purpose is to achieve high compression performance, the objects segmented may not be semantically meaningful to human observers...

Semantic video segmentation is challenging due to the sheer amount of data that needs to be processed and labeled in order to construct accurate models. In this paper we present a deep, end-to-end trainable methodology to video segmentation that is capable of leveraging information present in unlabeled data in order to improve semantic estimates. Our model combines a convolutional architecture ...

Video Instance Segmentation (VIS) is a task that simultaneously requires classification, segmentation, and instance association in video. Recent VIS approaches rely on sophisticated pipelines to achieve this goal, including RoI-related operations or 3D convolutions. In contrast, we present simple efficient single-stage framework based the segmentation method CondInst by adding an extra tracking...

We present an approach to semi-supervised video object segmentation, in the context of the DAVIS 2017 [8] challenge. Our approach combines category-based object detection, category-independent object appearance segmentation and temporal object tracking. We are motivated by the fact that the objects semantic category tends not to change throughout the video while its appearance and location can ...

A robust video watermarking scheme via temporal segmentation and middle frequency component adaptive modification is presented. First, the video clip is sliced into a shot sequence by temporal video segmentation. Then, several consecutive shots are selected from the shot sequence to makeup a video segment with proper duration. The watermark is embedded into the video segment by equally dividing...

We in this paper solve the problem of high-quality automatic real-time background cut for 720p portrait videos. We first handle the background ambiguity issue in semantic segmentation by proposing a global background attenuation model. A spatial-temporal refinement network is developed to further refine the segmentation errors in each frame and ensure temporal coherence in the segmentation map....

Automatic video segmentation and action recognition has been a long-standing problem in computer vision. Much work in the literature treats video segmentation and action recognition as two independent problems; while segmentation is often done without a temporal model of the activity, action recognition is usually performed on pre-segmented clips. In this paper we propose a novel method that av...

Semantic video segmentation is a key challenge for various applications. This paper presents new model named Noisy-LSTM, which trainable in an end-to-end manner, with convolutional LSTMs (ConvLSTMs) to leverage the temporal coherence frames, together simple yet effective training strategy that replaces frame given sequence noises. Our spoils frames and thus makes links ConvLSTMs unreliable; thi...

We present a novel mixture of trees (MoT) graphical model for video segmentation. Each component in this mixture represents a tree structured temporal linkage between super-pixels from the first to the last frame of a video sequence. Our time-series model explicitly captures the uncertainty in temporal linkage between adjacent frames which improves segmentation accuracy. We provide a variationa...