21 datasets found

The VFX Segmentation Dataset is a high-resolution video segmentation dataset containing 27,046 video frames with pixel-level ground-truth segmentations.

Actor and Action (A2D) dataset is a popular dataset for the actor and action video segmentation task.

Advanced by transformer architecture, vision foundation models (VFMs) achieve remarkable progress in performance and generalization ability. Segment Anything Model (SAM) is one...

The Davis dataset contains fifty high-resolution videos with pixel-accurate ground truth.

The dataset used for video segmentation, including Moving MNIST, Change detection, Segtrack V2, and Davis datasets.

A deepfake detection method that can address the issue of modifying segments of videos using generative techniques.

A panoramic video object segmentation dataset, providing 150 videos with high video resolutions and diverse motions.

Video segmentation requires consistently segmenting and tracking objects over time. Due to the quadratic dependency on input size, directly applying self-attention to video...

A dataset for semantic object classes in video.

The Breakfast Actions dataset contains 70 hours of cooking activities of varying complexity. It contains 10 different cooking tasks (with about 170 videos per task), which can...

Video object segmentation is challenging due to the factors like rapidly fast motion, cluttered backgrounds, arbitrary object appearance variation and shape deformation.

The DAVIS-17 dataset, which is a benchmark for video object segmentation.

The dataset used for training and testing the OMG-Seg model, which includes COCO panoptic, COCO-SAM, VIPSeg, Youtube-VIS-2019, and Youtube-VIS-2021 datasets.

Video k-net: A simple, strong, and unified baseline for video segmentation.

Video segmentation aims to segment and track every pixel in diverse scenarios accurately. This paper presents Tube-Link, a versatile framework that addresses multiple core tasks...

The VIPSeg dataset contains videos with annotations for video panoptic segmentation.

The DAVIS dataset is a widely used dataset for video-related tasks, consisting of approximately 2000 frames from 26 human-centric scenarios.

The dataset used in the paper is a pre-trained ResNet-50 classifier, which is used for image synthesis, unpaired image-to-image translation, and feature similarity estimation.

The COIN dataset is a large-scale instructional video dataset that contains 100 hours of video. The dataset is used for instructional video analysis and understanding.

The Viper dataset is a visual perception benchmark to facilitate both low-level and high-level vision tasks, e.g., optical flow and semantic segmentation. It consists of videos...