6 papers with code • 1 benchmarks • 3 datasets
The interactive scenario assumes the user gives iterative refinement inputs to the algorithm, in our case in the form of a scribble, to segment the objects of interest. Methods have to produce a segmentation mask for that object in all the frames of a video sequence taking into account all the user interactions.
In our framework, the past frames with object masks form an external memory, and the current frame as the query is segmented using the mask information in the memory.
Ranked #3 on Interactive Video Object Segmentation on DAVIS 2017 (using extra training data)
We present Modular interactive VOS (MiVOS) framework which decouples interaction-to-mask and mask propagation, allowing for higher generalizability and better performance.
Ranked #1 on Interactive Video Object Segmentation on DAVIS 2017 (using extra training data)
We propose a new multi-round training scheme for the interactive video object segmentation so that the networks can learn how to understand the user's intention and update incorrect estimations during the training.
Ranked #4 on Interactive Video Object Segmentation on DAVIS 2017 (J@60s metric)
The global transfer module conveys the segmentation information in an annotated frame to a target frame, while the local transfer module propagates the segmentation information in a temporally adjacent frame to the target frame.
Ranked #2 on Interactive Video Object Segmentation on DAVIS 2017 (using extra training data)
We propose a novel guided interactive segmentation (GIS) algorithm for video objects to improve the segmentation accuracy and reduce the interaction time.
This paper proposes a framework for the interactive video object segmentation (VOS) in the wild where users can choose some frames for annotations iteratively.