22,982 research outputs found
Dual Skipping Networks
Inspired by the recent neuroscience studies on the left-right asymmetry of
the human brain in processing low and high spatial frequency information, this
paper introduces a dual skipping network which carries out coarse-to-fine
object categorization. Such a network has two branches to simultaneously deal
with both coarse and fine-grained classification tasks. Specifically, we
propose a layer-skipping mechanism that learns a gating network to predict
which layers to skip in the testing stage. This layer-skipping mechanism endows
the network with good flexibility and capability in practice. Evaluations are
conducted on several widely used coarse-to-fine object categorization
benchmarks, and promising results are achieved by our proposed network model.Comment: CVPR 2018 (poster); fix typ
Celeganser: Automated Analysis of Nematode Morphology and Age
The nematode Caenorhabditis elegans (C. elegans) serves as an important model
organism in a wide variety of biological studies. In this paper we introduce a
pipeline for automated analysis of C. elegans imagery for the purpose of
studying life-span, health-span and the underlying genetic determinants of
aging. Our system detects and segments the worm, and predicts body coordinates
at each pixel location inside the worm. These coordinates provide dense
correspondence across individual animals to allow for meaningful comparative
analysis. We show that a model pre-trained to perform body-coordinate
regression extracts rich features that can be used to predict the age of
individual worms with high accuracy. This lays the ground for future research
in quantifying the relation between organs' physiologic and biochemical state,
and individual life/health-span.Comment: Computer Vision for Microscopy Image Analysis (CVMI) 202
Population mapping in informal settlements with high-resolution satellite imagery and equitable ground-truth
We propose a generalizable framework for the population estimation of dense, informal settlements in low-income urban areas–so called ’slums’–using high-resolution satellite imagery. Precise population estimates are a crucial factor for efficient resource allocations by government authorities and NGO’s, for instance in medical emergencies. We utilize equitable ground-truth data, which is gathered in collaboration with local communities: Through training and community mapping, the local population contributes their unique domain knowledge, while also maintaining agency over their data. This practice allows us to avoid carrying forward potential biases into the modeling pipeline, which might arise from a less
rigorous ground-truthing approach. We contextualize our approach in respect to the ongoing discussion within the machine learning community, aiming to make real-world machine learning applications more inclusive, fair and accountable. Because of the resource intensive ground-truth generation process, our training data is limited. We propose a gridded population estimation model, enabling flexible and customizable spatial resolutions. We test our pipeline on three experimental site in Nigeria, utilizing pre-trained and fine-tune vision networks to overcome data sparsity. Our findings highlight the difficulties of transferring common benchmark models to real-world tasks. We discuss this and propose steps forward
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