71 datasets found

The 2018 davis challenge on video object segmentation

RVOS-D provides more complex language descriptions from a broader object categories within relatively longer video sequences.

The dataset used for video object segmentation task, where a first frame labelled with the foreground object mask, the goal is to find the corresponding object pixels in future...

The dataset used for video object segmentation task, where a first frame labelled with the masks of several object instances, one aims to find the corresponding masks of objects...

The dataset used for video object segmentation task, where a first frame labelled with the masks of several object instances, one aims to find the corresponding masks of objects...

The DAVIS-585 dataset is used for evaluation.

The DAVIS Challenge 2017 benchmark is a dataset for video object segmentation.

The DAVIS Challenge 2018 benchmark is a dataset for video object segmentation.

Interactive video object segmentation (iVOS) aims at efficiently harvesting high-quality segmentation masks of the target object in a video with user interactions.

The dataset is used for video salient object detection and segmentation. It contains video saliency detection datasets, including DAVSOD, DAVIS, FBMS, SegV2, and ViSal.

A benchmark dataset and evaluation methodology for video object segmentation.

LVOS is a long-term video object segmentation benchmark dataset, consisting of 220 videos with a total duration of 421 minutes.

This paper proposes the Davis 2017 validation set for video object segmentation.

A large-scale video object segmentation benchmark.

A benchmark for unsupervised multi-object segmentation.

A large-scale synthetic VOS dataset with pixel-accurate segmentations.

The MiniVSPW dataset is a benchmark for few-shot video object segmentation.

Few-Shot Video Object Segmentation (FSVOS) aims to segment objects in a query video with the same category defined by a few annotated support images.

The DAVIS 2017 challenge dataset is a benchmark for video object segmentation. It consists of 150 sequences with multiple objects and instance-level pixel-wise annotations.

The YouTube-VOS dataset is a sequence-to-sequence video object segmentation dataset, with 1000 videos and 1000 frames per video.