SpikeSEG: Spiking Segmentation via STDP Saliency Mapping

Kirkland, Paul; Caterina, Gaetano Di; Soraghan, John J.; Matich, George

doi:10.1109/ijcnn48605.2020.9207075

Cited by 16 publications

(16 citation statements)

References 35 publications

(53 reference statements)

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“…Testing was carried out using 2 input sequences, so 600 buffered inputs. The network parameters are set to be the same as within [34], with the only difference being 36 features available for the one class present. This was to limit the number of external factors and focus the testing on the intra-classification abilities rather than inter classification.…”

Section: Resultsmentioning

confidence: 99%

“…The proposed temporal spike matching algorithm HULK SMASH makes use of an image classification SNN trained using STDP, similar to that seen in [31,32,33]. Prior developments have shown that extending an image classification SNN to a semantic segmentation network was possible [34,35]. These two prior works also utilised the N-Caltech dataset [36] making use of the event driven nature of the input sequences.…”

Section: Temporal Spike Matchingmentioning

confidence: 99%

“…Figure 1 highlights that the intra-sequence Process starts at the intersection of the encoder decoder network from SpikeSEG [34]. The first step of the process is to individually process each of the spiking instances from the classification layer of the SpikeSEG network.…”

Section: Temporal Spike Matchingmentioning

confidence: 99%

“…This allows the overall interpretability of the network to increase, with the decisions made by the network to become viewable and assessable, so removing the black box stigma of neural networks. This work makes use of previous research that showed how an SCNN trained with STDP could first perform image classification [33], and recently how it can be transformed into an encoder-decoder network, allowing semantic segmentation to be realised [34,35]. However, this previous research could not distinguish instances within these semantic segmentations.…”

Section: Introductionmentioning

confidence: 99%

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Unsupervised Spiking Instance Segmentation on Event Data using STDP

Kirkland¹,

Manna²,

Vicente-Sola³

et al. 2021

Preprint

Self Cite

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Spiking Neural Networks (SNN) and the field of Neuromorphic Engineering has brought about a paradigm shift in how to approach Machine Learning (ML) and Computer Vision (CV) problem. This paradigm shift comes from the adaption of event-based sensing and processing. An event-based vision sensor allows for sparse and asynchronous events to be produced that are dynamically related to the scene. Allowing not only the spatial information but a high-fidelity of temporal information to be captured. Meanwhile avoiding the extra overhead and redundancy of conventional high frame rate approaches. However, with this change in paradigm, many techniques from traditional CV and ML are not applicable to these event-based spatial-temporal visual streams. As such a limited number of recognition, detection and segmentation approaches exist. In this paper, we present a novel approach that can perform instance segmentation using just the weights of a Spike Time Dependent Plasticity trained Spiking Convolutional Neural Network that was trained for object recognition. This exploits the spatial and temporal aspects of the network's internal feature representations adding this new discriminative capability. We highlight the new capability by successfully transforming a single class unsupervised network for face detection into a multi-person face recognition and instance segmentation network.

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Section: Resultsmentioning

confidence: 99%

Section: Temporal Spike Matchingmentioning

confidence: 99%

Section: Temporal Spike Matchingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Unsupervised Spiking Instance Segmentation on Event Data using STDP

Kirkland¹,

Manna²,

Vicente-Sola³

et al. 2021

Preprint

Self Cite

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show abstract

“…DVS cameras). For instance, [16] performs image segmentation from an event-based camera with a spike-based approach trained with STDP.…”

Section: Related Workmentioning

confidence: 99%

Deep Spiking Convolutional Neural Network for Single Object Localization Based On Deep Continuous Local Learning

Barchid

Mennesson

Djeraba

2021

2021 International Conference on Content-Based Multimedia Indexing (CBMI)

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With the advent of neuromorphic hardware, spiking neural networks can be a good energy-efficient alternative to artificial neural networks. However, the use of spiking neural networks to perform computer vision tasks remains limited, mainly focusing on simple tasks such as digit recognition. It remains hard to deal with more complex tasks (e.g. segmentation, object detection) due to the small number of works on deep spiking neural networks for these tasks. The objective of this paper is to make the first step towards modern computer vision with supervised spiking neural networks. We propose a deep convolutional spiking neural network for the localization of a single object in a grayscale image. We propose a network based on DECOLLE, a spiking model that enables local surrogate gradient-based learning. The encouraging results reported on Oxford-IIIT-Pet validates the exploitation of spiking neural networks with a supervised learning approach for more elaborate vision tasks in the future.

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