Over the years, datasets and benchmarks have proven their fundamental importance in computer vision research, enabling targeted progress and objective comparisons in many fields. At the same time, legacy datasets may impend the evolution of a field due to saturated algorithm performance and the lack of contemporary, high quality data. In this work we present a new benchmark dataset and evaluation methodology for the area of video object segmentation. The dataset, named DAVIS (Densely Annotated VIdeo Segmentation), consists of fifty high quality, Full HD video sequences, spanning multiple occurrences of common video object segmentation challenges such as occlusions, motionblur and appearance changes. Each video is accompanied by densely annotated, pixel-accurate and per-frame ground truth segmentation. In addition, we provide a comprehensive analysis of several state-of-the-art segmentation approaches using three complementary metrics that measure the spatial extent of the segmentation, the accuracy of the silhouette contours and the temporal coherence. The results uncover strengths and weaknesses of current approaches, opening up promising directions for future works.

/pdf/a-benchmark-dataset-and-evaluation-methodology-for-video-4eb3ugu88k.pdf

A Benchmark Dataset and Evaluation Methodology for Video Object Segmentation

In this paper we illustrate how to perform both visual object tracking and semi-supervised video object segmentation, in real-time, with a single simple approach. Our method, dubbed SiamMask, improves the offline training procedure of popular fully-convolutional Siamese approaches for object tracking by augmenting their loss with a binary segmentation task. Once trained, SiamMask solely relies on a single bounding box initialisation and operates online, producing class-agnostic object segmentation masks and rotated bounding boxes at 55 frames per second. Despite its simplicity, versatility and fast speed, our strategy allows us to establish a new state-of-the-art among real-time trackers on VOT-2018, while at the same time demonstrating competitive performance and the best speed for the semi-supervised video object segmentation task on DAVIS-2016 and DAVIS-2017.

/pdf/fast-online-object-tracking-and-segmentation-a-unifying-3vbx1hwsno.pdf

Fast Online Object Tracking and Segmentation: A Unifying Approach

We present the 2017 DAVIS Challenge on Video Object Segmentation, a public dataset, benchmark, and competition specifically designed for the task of video object segmentation. Following the footsteps of other successful initiatives, such as ILSVRC and PASCAL VOC, which established the avenue of research in the fields of scene classification and semantic segmentation, the DAVIS Challenge comprises a dataset, an evaluation methodology, and a public competition with a dedicated workshop co-located with CVPR 2017. The DAVIS Challenge follows up on the recent publication of DAVIS (Densely-Annotated VIdeo Segmentation), which has fostered the development of several novel state-of-the-art video object segmentation techniques. In this paper we describe the scope of the benchmark, highlight the main characteristics of the dataset, define the evaluation metrics of the competition, and present a detailed analysis of the results of the participants to the challenge.

pdf/the-2017-davis-challenge-on-video-object-segmentation-3ft51hcs6k.pdf

The 2017 DAVIS Challenge on Video Object Segmentation

This paper tackles the task of semi-supervised video object segmentation, i.e., the separation of an object from the background in a video, given the mask of the first frame. We present One-Shot Video Object Segmentation (OSVOS), based on a fully-convolutional neural network architecture that is able to successively transfer generic semantic information, learned on ImageNet, to the task of foreground segmentation, and finally to learning the appearance of a single annotated object of the test sequence (hence one-shot). Although all frames are processed independently, the results are temporally coherent and stable. We perform experiments on two annotated video segmentation databases, which show that OSVOS is fast and improves the state of the art by a significant margin (79.8% vs 68.0%).

/pdf/one-shot-video-object-segmentation-1uv5f8s6xb.pdf

One-Shot Video Object Segmentation

In this work, we propose a novel approach to video segmentation that operates in bilateral space. We design a new energy on the vertices of a regularly sampled spatiotemporal bilateral grid, which can be solved efficiently using a standard graph cut label assignment. Using a bilateral formulation, the energy that we minimize implicitly approximates long-range, spatio-temporal connections between pixels while still containing only a small number of variables and only local graph edges. We compare to a number of recent methods, and show that our approach achieves state-of-the-art results on multiple benchmarks in a fraction of the runtime. Furthermore, our method scales linearly with image size, allowing for interactive feedback on real-world high resolution video.

/pdf/bilateral-space-video-segmentation-2zyhgbbcen.pdf

Bilateral Space Video Segmentation

As described herein, an electronic device with a display screen may simulate the color diffusion that occurs in a physical painting process. For instance, the user may perform one or more actions that simulate a brushstroke on the display screen such as swiping a touch-sensitive area or dragging a cursor across the screen. The electronic device then calculates a geodesic distance between a pixel inside a region defined by the brushstroke and a pixel located outside this region based on the physical distance between the two pixels and a weighting factor that varies depending on whether an image boundary is between the two pixels. Based on the geodesic distance, the electronic device uses a color diffusion relationship that defines the effect of the color of the brushstroke on the pixel and a time delay controlling when the color of the brushstroke reaches the pixel in order to simulate color diffusion.

Simulating color diffusion in a graphical display

Water painting is an art, where the result and experience strongly depend on the process of creating it. Color is injected by the artist in a controlled way and diffuses into a final state. We present a system to simulate such a process, which builds on existing colorization approaches. The geodesic distance builds the mathematical foundation for our animated diffusion. We add a time-dependent weight function to generate a diffusion-like spreading effect. Tiled approximation is used to achieve interactive rates on modern mobile devices. The result is a colorization framework simulating water painting that allows giving real-time feedback on touch events with the limited hardware resources of a tablet computer.

/pdf/colorbrush-animated-diffusion-for-intuitive-colorization-4jf5p9xqb3.pdf

ColorBrush: Animated diffusion for intuitive colorization simulating water painting

This disclosure relates to system and methods for segmenting a video in a higher order dimensional space. A video may be segmented by obtaining visual information defining an image of the video. The visual information may include pixels of the image and may be represented in a display space having a first dimensionality. A designation of a subset of the visual information represented in the display space as a part of an object portrayed in the image may be obtained. The visual information and the designation may be represented in the higher order dimensional space having a second dimensionality greater than the first dimensionality. An association of the visual information represented in the higher order dimensional space with the object may be obtained. The association may be correlated with the visual information represented in the display space. The correlation may define a location of the object in the image.

Systems and methods for higher order dimensional space video segmentation

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Nicolas Marki

Author Tools

Chat about Author

Papers

Bilateral Space Video Segmentation

Simulating color diffusion in a graphical display

ColorBrush: Animated diffusion for intuitive colorization simulating water painting

Systems and methods for higher order dimensional space video segmentation