Recently, Siamese network based trackers have received tremendous interest for their fast tracking speed and high performance. Despite the great success, this tracking framework still suffers from several limitations....
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In this paper, we propose a cooperative video transmission scheme in D2D networks. This research is motivated by the growing interests in hybrid digital-analog video transmissions and device-to-device (D2D) communicat...
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In this paper, we propose a cooperative video transmission scheme in D2D networks. This research is motivated by the growing interests in hybrid digital-analog video transmissions and device-to-device (D2D) communications. The framework of D2D communications can be generally modeled as a three-node network. In this network, coset coding is used to allow the destination to exploit the correlations between the video signals received in two phases. We have done some work of further optimization to improve the video quality at destination in this network. First, we derive a closed form of the reconstruction error at the destination. This provides a theoretical foundation for finding the optimal quantization step size in coset coding. Then, based on the accurate analysis on the coset coding we design a new power allocation algorithm. Experimental results verify that our scheme outperforms the recently proposed WCVC and DCVC.
Observing that Semantic features learned in an image classification task and Appearance features learned in a similarity matching task complement each other, we build a twofold Siamese network, named SA-Siam, for real...
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Fractional-pixel interpolation has been widely used in the modern video coding standards to improve the accuracy of motion compensated prediction. Traditional interpolation filters are designed based on the signal pro...
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Fractional-pixel interpolation has been widely used in the modern video coding standards to improve the accuracy of motion compensated prediction. Traditional interpolation filters are designed based on the signal processing theory. However, video signal is non-stationary, making the traditional methods less effective. In this paper, we reveal that the interpolation filter can not only generate the fractional pixels from the integer pixels, but also reconstruct the integer pixels from the fractional ones. This property is called invertibility. Inspired by the invertibility of fractional-pixel interpolation, we propose an end-to-end scheme based on convolutional neural network (CNN) to derive the invertible interpolation filter, termed CNNInvIF. CNNlnvIF does not need the “ground-truth” of fractional pixels for training. Experimental results show that the proposed CNNInvIF can achieve up to 4.6% and on average 2.2% BD-rate reduction than HEVC under the low-delay P configuration.
A salient seed extraction based target detection method is proposed in this paper, aiming to distinguish target points from background points in SAR images. Different from recent superpixel based method which generate...
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A salient seed extraction based target detection method is proposed in this paper, aiming to distinguish target points from background points in SAR images. Different from recent superpixel based method which generates superpixels firstly, and for each superpixel decides whether it belongs to part of a target. The proposed method employs a salient point to region scheme. At first, salient seeds are extracted by mean-shift and region feature based approach. Then, pixels are assigned to the most similar seed and those assigned to the salient seeds are extracted to form the foreground region. Finally, constant false alarm rate (CFAR) operation is employed to detect the target points from the foreground region. The effectiveness of the proposed method is validated by comparing with five state-of-the-art methods on TerraSAR-X images.
In the state-of-the-art video coding standard-High Efficiency Video Coding (HEVC), context-adaptive binary arithmetic coding (CABAC) is adopted as the entropy coding tool. In CABAC, the binarization processes are manu...
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In the state-of-the-art video coding standard-High Efficiency Video Coding (HEVC), context-adaptive binary arithmetic coding (CABAC) is adopted as the entropy coding tool. In CABAC, the binarization processes are manually designed, and the context models are empirically crafted, both of which incur that the probability distribution of the syntax elements may not be estimated accurately, and restrict the coding efficiency. In this paper, we adopt a convolutional neural network-based arithmetic coding (CNNAC) strategy, and conduct studies on the coding of the DC coefficients for HEVC intra coding. Instead of manually designing binarization process and context model, we propose to directly estimate the probability distribution of the value of the DC coefficient using densely connected convolutional networks. The estimated probability together with the real DC coefficient are then input into a multi-level arithmetic codec to fulfill entropy coding. Simulation results show that our proposed CNNAC leads to on average 22.47% bits saving compared with CABAC for the bits of DC coefficients, which corresponds to 1.6% BD-rate reduction than the HEVC anchor.
An identity regularized sparse representation (IRSR) based SAR target recognition method is proposed in this paper. The method aims to find a transformation that can map the data to a transformed space, in which targe...
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An identity regularized sparse representation (IRSR) based SAR target recognition method is proposed in this paper. The method aims to find a transformation that can map the data to a transformed space, in which targets from the same class are close with each other, no matter the distance of them in the original space. This identity constraint can be formulated as a ℓ 1 -norm minimization problem. By decoupling the problem into the sparse coding problem and the dictionary learning problem, the solution can be obtained iteratively. The solution is simply the weighted average of the sparse coding of all training data. Experimental results demonstrate that the proposed method is superior to several related methods.
Neural Machine Translation (NMT) has achieved remarkable progress with the quick evolvement of model structures. In this paper, we propose the concept of layer-wise coordination for NMT, which explicitly coordinates t...
Neural Machine Translation (NMT) has achieved remarkable progress with the quick evolvement of model structures. In this paper, we propose the concept of layer-wise coordination for NMT, which explicitly coordinates the learning of hidden representations of the encoder and decoder together layer by layer, gradually from low level to high level. Specifically, we design a layer-wise attention and mixed attention mechanism, and further share the parameters of each layer between the encoder and decoder to regularize and coordinate the learning. Experiments show that combined with the state-of-the-art Transformer model, layer-wise coordination achieves improvements on three IWSLT and two WMT translation tasks. More specifically, our method achieves 34.43 and 29.01 BLEU score on WMT16 English-Romanian and WMT14 English-German tasks, outperforming the Transformer baseline.
Polarization converter is used in the applications of the polar SAR observations. There exists coupling between TE and TM modes when plane wave is oblique incident on the surface of dielectric periodic structure, the ...
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Polarization converter is used in the applications of the polar SAR observations. There exists coupling between TE and TM modes when plane wave is oblique incident on the surface of dielectric periodic structure, the single TE or TM polarized wave incident will cause TE and TM mixed transmission wave. In some proper incident conditions, complete polarization conversion can be realized between TE and TM mode. In this work, a design of complete polarization converter by using dielectric periodic structure is designed and it is carefully investigated by a method which combines the multimode network theory with the rigorous mode matching method. We revealed TE/TM complete polarization conversion characteristics of dielectric periodic structure, and also analyzed the effects of structure parameters. These investigations provide important guideline for accurate designing new millimeter wave polarization converters.
Motion blur is one of the most common degradation artifacts in dynamic scene photography. This paper reviews the NTIRE 2020 Challenge on Image and Video Deblurring. In this challenge, we present the evaluation results...
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