99 research outputs found
Learning Intelligent Dialogs for Bounding Box Annotation
We introduce Intelligent Annotation Dialogs for bounding box annotation. We
train an agent to automatically choose a sequence of actions for a human
annotator to produce a bounding box in a minimal amount of time. Specifically,
we consider two actions: box verification, where the annotator verifies a box
generated by an object detector, and manual box drawing. We explore two kinds
of agents, one based on predicting the probability that a box will be
positively verified, and the other based on reinforcement learning. We
demonstrate that (1) our agents are able to learn efficient annotation
strategies in several scenarios, automatically adapting to the image
difficulty, the desired quality of the boxes, and the detector strength; (2) in
all scenarios the resulting annotation dialogs speed up annotation compared to
manual box drawing alone and box verification alone, while also outperforming
any fixed combination of verification and drawing in most scenarios; (3) in a
realistic scenario where the detector is iteratively re-trained, our agents
evolve a series of strategies that reflect the shifting trade-off between
verification and drawing as the detector grows stronger.Comment: This paper appeared at CVPR 201
Fluid Annotation: A Human-Machine Collaboration Interface for Full Image Annotation
We introduce Fluid Annotation, an intuitive human-machine collaboration
interface for annotating the class label and outline of every object and
background region in an image. Fluid annotation is based on three principles:
(I) Strong Machine-Learning aid. We start from the output of a strong neural
network model, which the annotator can edit by correcting the labels of
existing regions, adding new regions to cover missing objects, and removing
incorrect regions. The edit operations are also assisted by the model. (II)
Full image annotation in a single pass. As opposed to performing a series of
small annotation tasks in isolation, we propose a unified interface for full
image annotation in a single pass. (III) Empower the annotator. We empower the
annotator to choose what to annotate and in which order. This enables
concentrating on what the machine does not already know, i.e. putting human
effort only on the errors it made. This helps using the annotation budget
effectively. Through extensive experiments on the COCO+Stuff dataset, we
demonstrate that Fluid Annotation leads to accurate annotations very
efficiently, taking three times less annotation time than the popular LabelMe
interface.Comment: ACM MultiMedia 2018. Live demo is available at fluidann.appspot.co
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