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Found 5 papers, 5 papers with code
Depth from Camera Motion and Object Detection
This paper addresses the problem of learning to estimate the depth of detected objects given some measurement of camera motion (e. g., from robot kinematics or vehicle odometry).
Learning Object Depth from Camera Motion and Video Object Segmentation
Video object segmentation, i. e., the separation of a target object from background in video, has made significant progress on real and challenging videos in recent years.
BubbleNets: Learning to Select the Guidance Frame in Video Object Segmentation by Deep Sorting Frames
Semi-supervised video object segmentation has made significant progress on real and challenging videos in recent years.
Tukey-Inspired Video Object Segmentation
We investigate the problem of strictly unsupervised video object segmentation, i. e., the separation of a primary object from background in video without a user-provided object mask or any training on an annotated dataset.
Video Object Segmentation using Supervoxel-Based Gerrymandering
Focusing on the problem of strictly unsupervised video object segmentation, we devise a method called supervoxel gerrymandering that links masks of foregroundness and backgroundness via local and non-local consensus measures.
