209 research outputs found
Camera On-boarding for Person Re-identification using Hypothesis Transfer Learning
Most of the existing approaches for person re-identification consider a
static setting where the number of cameras in the network is fixed. An
interesting direction, which has received little attention, is to explore the
dynamic nature of a camera network, where one tries to adapt the existing
re-identification models after on-boarding new cameras, with little additional
effort. There have been a few recent methods proposed in person
re-identification that attempt to address this problem by assuming the labeled
data in the existing network is still available while adding new cameras. This
is a strong assumption since there may exist some privacy issues for which one
may not have access to those data. Rather, based on the fact that it is easy to
store the learned re-identifications models, which mitigates any data privacy
concern, we develop an efficient model adaptation approach using hypothesis
transfer learning that aims to transfer the knowledge using only source models
and limited labeled data, but without using any source camera data from the
existing network. Our approach minimizes the effect of negative transfer by
finding an optimal weighted combination of multiple source models for
transferring the knowledge. Extensive experiments on four challenging benchmark
datasets with a variable number of cameras well demonstrate the efficacy of our
proposed approach over state-of-the-art methods.Comment: Accepted to CVPR 202
Remote optimization of an ultra-cold atoms experiment by experts and citizen scientists
We introduce a novel remote interface to control and optimize the experimental production of Bose-Einstein condensates (BECs) and find improved solutions using two distinct implementations. First, a team of theoreticians employed a Remote version of their dCRAB optimization algorithm (RedCRAB), and second a gamified interface allowed 600 citizen scientists from around the world to participate in real-time optimization. Quantitative studies of player search behavior demonstrated that they collectively engage in a combination of local and global search. This form of adaptive search prevents premature convergence by the explorative behavior of low-performing players while high-performing players locally refine their solutions. In addition, many successful citizen science games have relied on a problem representation that directly engaged the visual or experiential intuition of the players. Here we demonstrate that citizen scientists can also be successful in an entirely abstract problem visualization. This gives encouragement that a much wider range of challenges could potentially be open to gamification in the future
Identification and energy calibration of hadronically decaying tau leptons with the ATLAS experiment
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