Continuous-valued deep convolutional networks (DNNs) can be converted into accurate rate-coding based spike neural networks (SNNs). However, the substantial computational and energy costs, which is caused by multiple spikes, limit their use in mobile and embedded applications. And recent works have shown that the newly emerged temporal-coding based SNNs converted from DNNs can reduce the computational load effectively. In this paper, we propose a novel method to convert DNNs to temporal-coding SNNs, called TDSNN. Combined with the characteristic of the leaky integrate-andfire (LIF) neural model, we put forward a new coding principle Reverse Coding and design a novel Ticking Neuron mechanism. According to our evaluation, our proposed method achieves 42% total operations reduction on average in large networks comparing with DNNs with no more than 0.5% accuracy loss. The evaluation shows that TDSNN may prove to be one of the key enablers to make the adoption of SNNs widespread.
Accurate detection and segmentation of anatomical structures from ultrasound images are crucial for clinical diagnosis and biometric measurements. Although ultrasound imaging has been widely used with superiorities such as low cost and portability, the fuzzy border definition and existence of abounding artifacts pose great challenges for automatically detecting and segmenting the complex anatomical structures. In this paper, we propose a multi-domain regularized deep learning method to address this challenging problem. By leveraging the transfer learning from cross domains, the feature representations are effectively enhanced. The results are further improved by the iterative refinement. Moreover, our method is quite efficient by taking advantage of a fully convolutional network, which is formulated as an end-to-end learning framework of detection and segmentation. Extensive experimental results on a large-scale database corroborated that our method achieved a superior detection and segmentation accuracy, outperforming other methods by a significant margin and demonstrating competitive capability even compared to human performance.
arXiv:1607.01855v1 [cs.CV] 7 Jul 2016Recently, fully convolutional networks (FCN), i.e, a variant of CNN, achieved the state-of-the-art performance on image segmentation related tasks [6]. Such
In this article, we focus on isolated gesture recognition and explore different modalities by involving RGB stream, depth stream, and saliency stream for inspection. Our goal is to push the boundary of this realm even further by proposing a unified framework that exploits the advantages of multi-modality fusion. Specifically, a spatial-temporal network architecture based on consensus-voting has been proposed to explicitly model the long-term structure of the video sequence and to reduce estimation variance when confronted with comprehensive inter-class variations. In addition, a three-dimensional depth-saliency convolutional network is aggregated in parallel to capture subtle motion characteristics. Extensive experiments are done to analyze the performance of each component and our proposed approach achieves the best results on two public benchmarks, ChaLearn IsoGD and RGBD-HuDaAct, outperforming the closest competitor by a margin of over 10% and 15%, respectively. Our project and codes will be released at https://davidsonic.github.io/index/acm_tomm_2017.html.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.