8,766 research outputs found
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
How a well-adapting immune system remembers
An adaptive agent predicting the future state of an environment must weigh
trust in new observations against prior experiences. In this light, we propose
a view of the adaptive immune system as a dynamic Bayesian machinery that
updates its memory repertoire by balancing evidence from new pathogen
encounters against past experience of infection to predict and prepare for
future threats. This framework links the observed initial rapid increase of the
memory pool early in life followed by a mid-life plateau to the ease of
learning salient features of sparse environments. We also derive a modulated
memory pool update rule in agreement with current vaccine response experiments.
Our results suggest that pathogenic environments are sparse and that memory
repertoires significantly decrease infection costs even with moderate sampling.
The predicted optimal update scheme maps onto commonly considered competitive
dynamics for antigen receptors
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