4,018 research outputs found
Temporal Continuity Based Unsupervised Learning for Person Re-Identification
Person re-identification (re-id) aims to match the same person from images
taken across multiple cameras. Most existing person re-id methods generally
require a large amount of identity labeled data to act as discriminative
guideline for representation learning. Difficulty in manually collecting
identity labeled data leads to poor adaptability in practical scenarios. To
overcome this problem, we propose an unsupervised center-based clustering
approach capable of progressively learning and exploiting the underlying re-id
discriminative information from temporal continuity within a camera. We call
our framework Temporal Continuity based Unsupervised Learning (TCUL).
Specifically, TCUL simultaneously does center based clustering of unlabeled
(target) dataset and fine-tunes a convolutional neural network (CNN)
pre-trained on irrelevant labeled (source) dataset to enhance discriminative
capability of the CNN for the target dataset. Furthermore, it exploits
temporally continuous nature of images within-camera jointly with spatial
similarity of feature maps across-cameras to generate reliable pseudo-labels
for training a re-identification model. As the training progresses, number of
reliable samples keep on growing adaptively which in turn boosts representation
ability of the CNN. Extensive experiments on three large-scale person re-id
benchmark datasets are conducted to compare our framework with state-of-the-art
techniques, which demonstrate superiority of TCUL over existing methods
Seven properties of self-organization in the human brain
The principle of self-organization has acquired a fundamental significance in the newly emerging field of computational philosophy. Self-organizing systems have been described in various domains in science and philosophy including physics, neuroscience, biology and medicine, ecology, and sociology. While system architecture and their general purpose may depend on domain-specific concepts and definitions, there are (at least) seven key properties of self-organization clearly identified in brain systems: 1) modular connectivity, 2) unsupervised learning, 3) adaptive ability, 4) functional resiliency, 5) functional plasticity, 6) from-local-to-global functional organization, and 7) dynamic system growth. These are defined here in the light of insight from neurobiology, cognitive neuroscience and Adaptive Resonance Theory (ART), and physics to show that self-organization achieves stability and functional plasticity while minimizing structural system complexity. A specific example informed by empirical research is discussed to illustrate how modularity, adaptive learning, and dynamic network growth enable stable yet plastic somatosensory representation for human grip force control. Implications for the design of “strong” artificial intelligence in robotics are brought forward
Learning to Generalize over Subpartitions for Heterogeneity-aware Domain Adaptive Nuclei Segmentation
Annotation scarcity and cross-modality/stain data distribution shifts are two
major obstacles hindering the application of deep learning models for nuclei
analysis, which holds a broad spectrum of potential applications in digital
pathology. Recently, unsupervised domain adaptation (UDA) methods have been
proposed to mitigate the distributional gap between different imaging
modalities for unsupervised nuclei segmentation in histopathology images.
However, existing UDA methods are built upon the assumption that data
distributions within each domain should be uniform. Based on the
over-simplified supposition, they propose to align the histopathology target
domain with the source domain integrally, neglecting severe intra-domain
discrepancy over subpartitions incurred by mixed cancer types and sampling
organs. In this paper, for the first time, we propose to explicitly consider
the heterogeneity within the histopathology domain and introduce open compound
domain adaptation (OCDA) to resolve the crux. In specific, a two-stage
disentanglement framework is proposed to acquire domain-invariant feature
representations at both image and instance levels. The holistic design
addresses the limitations of existing OCDA approaches which struggle to capture
instance-wise variations. Two regularization strategies are specifically
devised herein to leverage the rich subpartition-specific characteristics in
histopathology images and facilitate subdomain decomposition. Moreover, we
propose a dual-branch nucleus shape and structure preserving module to prevent
nucleus over-generation and deformation in the synthesized images. Experimental
results on both cross-modality and cross-stain scenarios over a broad range of
diverse datasets demonstrate the superiority of our method compared with
state-of-the-art UDA and OCDA methods
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