105,425 research outputs found
Let's Agree to Disagree: Learning Highly Debatable Multirater Labelling
Classification and differentiation of small pathological objects may greatly vary among human raters due to differences in training, expertise and their consistency over time. In a radiological setting, objects commonly have high within-class appearance variability whilst sharing certain characteristics across different classes, making their distinction even more difficult. As an example, markers of cerebral small vessel disease, such as enlarged perivascular spaces (EPVS) and lacunes, can be very varied in their appearance while exhibiting high inter-class similarity, making this task highly challenging for human raters. In this work, we investigate joint models of individual rater behaviour and multi-rater consensus in a deep learning setting, and apply it to a brain lesion object-detection task. Results show that jointly modelling both individual and consensus estimates leads to significant improvements in performance when compared to directly predicting consensus labels, while also allowing the characterization of human-rater consistency
Collaborative Deep Reinforcement Learning for Joint Object Search
We examine the problem of joint top-down active search of multiple objects
under interaction, e.g., person riding a bicycle, cups held by the table, etc..
Such objects under interaction often can provide contextual cues to each other
to facilitate more efficient search. By treating each detector as an agent, we
present the first collaborative multi-agent deep reinforcement learning
algorithm to learn the optimal policy for joint active object localization,
which effectively exploits such beneficial contextual information. We learn
inter-agent communication through cross connections with gates between the
Q-networks, which is facilitated by a novel multi-agent deep Q-learning
algorithm with joint exploitation sampling. We verify our proposed method on
multiple object detection benchmarks. Not only does our model help to improve
the performance of state-of-the-art active localization models, it also reveals
interesting co-detection patterns that are intuitively interpretable
Joint-SRVDNet: Joint Super Resolution and Vehicle Detection Network
In many domestic and military applications, aerial vehicle detection and
super-resolutionalgorithms are frequently developed and applied independently.
However, aerial vehicle detection on super-resolved images remains a
challenging task due to the lack of discriminative information in the
super-resolved images. To address this problem, we propose a Joint
Super-Resolution and Vehicle DetectionNetwork (Joint-SRVDNet) that tries to
generate discriminative, high-resolution images of vehicles fromlow-resolution
aerial images. First, aerial images are up-scaled by a factor of 4x using a
Multi-scaleGenerative Adversarial Network (MsGAN), which has multiple
intermediate outputs with increasingresolutions. Second, a detector is trained
on super-resolved images that are upscaled by factor 4x usingMsGAN architecture
and finally, the detection loss is minimized jointly with the super-resolution
loss toencourage the target detector to be sensitive to the subsequent
super-resolution training. The network jointlylearns hierarchical and
discriminative features of targets and produces optimal super-resolution
results. Weperform both quantitative and qualitative evaluation of our proposed
network on VEDAI, xView and DOTAdatasets. The experimental results show that
our proposed framework achieves better visual quality than thestate-of-the-art
methods for aerial super-resolution with 4x up-scaling factor and improves the
accuracy ofaerial vehicle detection
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