192,099 research outputs found
Uncertainty relations and possible experience
The uncertainty principle can be understood as a condition of joint indeterminacy of classes of properties in quantum theory. The mathematical expressions most closely associated with this principle have been the uncertainty relations, various inequalities exemplified by the well known expression regarding position and momentum introduced by Heisenberg. Here, recent work involving a new sort of “logical” indeterminacy principle and associated relations introduced by Pitowsky, expressable directly in terms of probabilities of outcomes of measurements of sharp quantum observables, is reviewed and its quantum nature is discussed. These novel relations are derivable from Boolean “conditions of possible experience” of the quantum realm and have been considered both as fundamentally logical and as fundamentally geometrical. This work focuses on the relationship of indeterminacy to the propositions regarding the values of discrete, sharp observables of quantum systems. Here, reasons for favoring each of these two positions are considered. Finally, with an eye toward future research related to indeterminacy relations, further novel approaches grounded in category theory and intended to capture and reconceptualize the complementarity characteristics of quantum propositions are discussed in relation to the former
Map-Guided Curriculum Domain Adaptation and Uncertainty-Aware Evaluation for Semantic Nighttime Image Segmentation
We address the problem of semantic nighttime image segmentation and improve
the state-of-the-art, by adapting daytime models to nighttime without using
nighttime annotations. Moreover, we design a new evaluation framework to
address the substantial uncertainty of semantics in nighttime images. Our
central contributions are: 1) a curriculum framework to gradually adapt
semantic segmentation models from day to night through progressively darker
times of day, exploiting cross-time-of-day correspondences between daytime
images from a reference map and dark images to guide the label inference in the
dark domains; 2) a novel uncertainty-aware annotation and evaluation framework
and metric for semantic segmentation, including image regions beyond human
recognition capability in the evaluation in a principled fashion; 3) the Dark
Zurich dataset, comprising 2416 unlabeled nighttime and 2920 unlabeled twilight
images with correspondences to their daytime counterparts plus a set of 201
nighttime images with fine pixel-level annotations created with our protocol,
which serves as a first benchmark for our novel evaluation. Experiments show
that our map-guided curriculum adaptation significantly outperforms
state-of-the-art methods on nighttime sets both for standard metrics and our
uncertainty-aware metric. Furthermore, our uncertainty-aware evaluation reveals
that selective invalidation of predictions can improve results on data with
ambiguous content such as our benchmark and profit safety-oriented applications
involving invalid inputs.Comment: IEEE T-PAMI 202
Guided Curriculum Model Adaptation and Uncertainty-Aware Evaluation for Semantic Nighttime Image Segmentation
Most progress in semantic segmentation reports on daytime images taken under
favorable illumination conditions. We instead address the problem of semantic
segmentation of nighttime images and improve the state-of-the-art, by adapting
daytime models to nighttime without using nighttime annotations. Moreover, we
design a new evaluation framework to address the substantial uncertainty of
semantics in nighttime images. Our central contributions are: 1) a curriculum
framework to gradually adapt semantic segmentation models from day to night via
labeled synthetic images and unlabeled real images, both for progressively
darker times of day, which exploits cross-time-of-day correspondences for the
real images to guide the inference of their labels; 2) a novel
uncertainty-aware annotation and evaluation framework and metric for semantic
segmentation, designed for adverse conditions and including image regions
beyond human recognition capability in the evaluation in a principled fashion;
3) the Dark Zurich dataset, which comprises 2416 unlabeled nighttime and 2920
unlabeled twilight images with correspondences to their daytime counterparts
plus a set of 151 nighttime images with fine pixel-level annotations created
with our protocol, which serves as a first benchmark to perform our novel
evaluation. Experiments show that our guided curriculum adaptation
significantly outperforms state-of-the-art methods on real nighttime sets both
for standard metrics and our uncertainty-aware metric. Furthermore, our
uncertainty-aware evaluation reveals that selective invalidation of predictions
can lead to better results on data with ambiguous content such as our nighttime
benchmark and profit safety-oriented applications which involve invalid inputs.Comment: ICCV 2019 camera-read
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