10,748 research outputs found
Regularizing Deep Networks by Modeling and Predicting Label Structure
We construct custom regularization functions for use in supervised training
of deep neural networks. Our technique is applicable when the ground-truth
labels themselves exhibit internal structure; we derive a regularizer by
learning an autoencoder over the set of annotations. Training thereby becomes a
two-phase procedure. The first phase models labels with an autoencoder. The
second phase trains the actual network of interest by attaching an auxiliary
branch that must predict output via a hidden layer of the autoencoder. After
training, we discard this auxiliary branch.
We experiment in the context of semantic segmentation, demonstrating this
regularization strategy leads to consistent accuracy boosts over baselines,
both when training from scratch, or in combination with ImageNet pretraining.
Gains are also consistent over different choices of convolutional network
architecture. As our regularizer is discarded after training, our method has
zero cost at test time; the performance improvements are essentially free. We
are simply able to learn better network weights by building an abstract model
of the label space, and then training the network to understand this
abstraction alongside the original task.Comment: to appear at CVPR 201
Online Adaptation of Convolutional Neural Networks for Video Object Segmentation
We tackle the task of semi-supervised video object segmentation, i.e.
segmenting the pixels belonging to an object in the video using the ground
truth pixel mask for the first frame. We build on the recently introduced
one-shot video object segmentation (OSVOS) approach which uses a pretrained
network and fine-tunes it on the first frame. While achieving impressive
performance, at test time OSVOS uses the fine-tuned network in unchanged form
and is not able to adapt to large changes in object appearance. To overcome
this limitation, we propose Online Adaptive Video Object Segmentation (OnAVOS)
which updates the network online using training examples selected based on the
confidence of the network and the spatial configuration. Additionally, we add a
pretraining step based on objectness, which is learned on PASCAL. Our
experiments show that both extensions are highly effective and improve the
state of the art on DAVIS to an intersection-over-union score of 85.7%.Comment: Accepted at BMVC 2017. This version contains minor changes for the
camera ready versio
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