5,551 research outputs found
Deep learning based pulse shape discrimination for germanium detectors
Experiments searching for rare processes like neutrinoless double beta decay
heavily rely on the identification of background events to reduce their
background level and increase their sensitivity. We present a novel machine
learning based method to recognize one of the most abundant classes of
background events in these experiments. By combining a neural network for
feature extraction with a smaller classification network, our method can be
trained with only a small number of labeled events. To validate our method, we
use signals from a broad-energy germanium detector irradiated with a Th
gamma source. We find that it matches the performance of state-of-the-art
algorithms commonly used for this detector type. However, it requires less
tuning and calibration and shows potential to identify certain types of
background events missed by other methods.Comment: Published in Eur. Phys. J. C. 9 pages, 10 figures, 3 table
Auto-Encoding Scene Graphs for Image Captioning
We propose Scene Graph Auto-Encoder (SGAE) that incorporates the language
inductive bias into the encoder-decoder image captioning framework for more
human-like captions. Intuitively, we humans use the inductive bias to compose
collocations and contextual inference in discourse. For example, when we see
the relation `person on bike', it is natural to replace `on' with `ride' and
infer `person riding bike on a road' even the `road' is not evident. Therefore,
exploiting such bias as a language prior is expected to help the conventional
encoder-decoder models less likely overfit to the dataset bias and focus on
reasoning. Specifically, we use the scene graph --- a directed graph
() where an object node is connected by adjective nodes and
relationship nodes --- to represent the complex structural layout of both image
() and sentence (). In the textual domain, we use
SGAE to learn a dictionary () that helps to reconstruct sentences
in the pipeline, where encodes the desired language prior;
in the vision-language domain, we use the shared to guide the
encoder-decoder in the pipeline. Thanks to the scene graph
representation and shared dictionary, the inductive bias is transferred across
domains in principle. We validate the effectiveness of SGAE on the challenging
MS-COCO image captioning benchmark, e.g., our SGAE-based single-model achieves
a new state-of-the-art CIDEr-D on the Karpathy split, and a competitive
CIDEr-D (c40) on the official server even compared to other ensemble
models
Neighbourhood Consensus Networks
We address the problem of finding reliable dense correspondences between a
pair of images. This is a challenging task due to strong appearance differences
between the corresponding scene elements and ambiguities generated by
repetitive patterns. The contributions of this work are threefold. First,
inspired by the classic idea of disambiguating feature matches using semi-local
constraints, we develop an end-to-end trainable convolutional neural network
architecture that identifies sets of spatially consistent matches by analyzing
neighbourhood consensus patterns in the 4D space of all possible
correspondences between a pair of images without the need for a global
geometric model. Second, we demonstrate that the model can be trained
effectively from weak supervision in the form of matching and non-matching
image pairs without the need for costly manual annotation of point to point
correspondences. Third, we show the proposed neighbourhood consensus network
can be applied to a range of matching tasks including both category- and
instance-level matching, obtaining the state-of-the-art results on the PF
Pascal dataset and the InLoc indoor visual localization benchmark.Comment: In Proceedings of the 32nd Conference on Neural Information
Processing Systems (NeurIPS 2018
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