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Found 8 papers, 0 papers with code
Predicting How to Distribute Work Between Algorithms and Humans to Segment an Image Batch
We propose a resource allocation framework for predicting how best to allocate a fixed budget of human annotation effort in order to collect higher quality segmentations for a given batch of images and automated methods.
Pixel Objectness: Learning to Segment Generic Objects Automatically in Images and Videos
We propose an end-to-end learning framework for segmenting generic objects in both images and videos.
FusionSeg: Learning to Combine Motion and Appearance for Fully Automatic Segmentation of Generic Objects in Videos
Our method learns to combine appearance and motion information to produce pixel level segmentation masks for all prominent objects in videos.
Predicting Foreground Object Ambiguity and Efficiently Crowdsourcing the Segmentation(s)
We propose the ambiguity problem for the foreground object segmentation task and motivate the importance of estimating and accounting for this ambiguity when designing vision systems.
FusionSeg: Learning to combine motion and appearance for fully automatic segmention of generic objects in videos
Our method learns to combine appearance and motion information to produce pixel level segmentation masks for all prominent objects in videos.
Pixel Objectness
We propose an end-to-end learning framework for generating foreground object segmentations.
Click Carving: Segmenting Objects in Video with Point Clicks
We present a novel form of interactive video object segmentation where a few clicks by the user helps the system produce a full spatio-temporal segmentation of the object of interest.
Active Image Segmentation Propagation
We propose a semi-automatic method to obtain foreground object masks for a large set of related images.
