31,876 research outputs found
Deformable Object Tracking with Gated Fusion
The tracking-by-detection framework receives growing attentions through the
integration with the Convolutional Neural Networks (CNNs). Existing
tracking-by-detection based methods, however, fail to track objects with severe
appearance variations. This is because the traditional convolutional operation
is performed on fixed grids, and thus may not be able to find the correct
response while the object is changing pose or under varying environmental
conditions. In this paper, we propose a deformable convolution layer to enrich
the target appearance representations in the tracking-by-detection framework.
We aim to capture the target appearance variations via deformable convolution,
which adaptively enhances its original features. In addition, we also propose a
gated fusion scheme to control how the variations captured by the deformable
convolution affect the original appearance. The enriched feature representation
through deformable convolution facilitates the discrimination of the CNN
classifier on the target object and background. Extensive experiments on the
standard benchmarks show that the proposed tracker performs favorably against
state-of-the-art methods
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
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