The aim of end-to-end sign language translation (SLT) is to interpret continuous sign language (SL) video sequences into coherent natural language sentences without any intermediary annotations, i.e., glosses. However, end-to-end SLT suffers several intractable issues: (i) the temporal correspondence constraint loss problem between SL videos and glosses, and (ii) the weakly supervised sequence labeling problem between long SL videos and sentences. To address these issues, we propose an adaptive video representation enhanced Transformer (AVRET), with three extra modules: adaptive masking (AM), local clip self-attention (LCSA) and adaptive fusion (AF). Specifically, we utilize the first AM module to generate a special mask that adaptively drops out temporally important SL video frame representations to enhance the SL video features. Then, we pass the masked video feature to the Transformer encoder consisting of LCSA and masked self-attention to learn clip-level and continuous video-level feature information. Finally, the output feature of encoder is fused with the temporal feature of AM module via the AF module and use the second AM module to generate more robust feature representations. Besides, we add weakly supervised loss terms to constrain these two AM modules. To promote the Chinese SLT research, we further construct CSL-FocusOn, a Chinese continuous SLT dataset, and share its collection method. It involves many common scenarios, and provides SL sentence annotations and multi-cue images of signers. Our experiments on the CSL-FocusOn, PHOENIX14T, and CSL-Daily datasets show that the proposed method achieves the competitive performance on the end-to-end SLT task without using glosses in training. The code is available at https://github.com/LzDddd/AVRET.

Low-dose computed tomography (LDCT) images are often highly degraded by amplified mottle noise and streak artifacts. Maintaining image quality under low-dose scan protocols is a well-known challenge. Recently, sparse representation-based techniques have been shown to be efficient in improving such CT images. In this paper, we propose a 3D feature constrained reconstruction (3D-FCR) algorithm for LDCT image reconstruction. The feature information used in the 3D-FCR algorithm relies on a 3D feature dictionary constructed from available high quality standard-dose CT sample. The CT voxels and the sparse coefficients are sequentially updated using an alternating minimization scheme. The performance of the 3D-FCR algorithm was assessed through experiments conducted on phantom simulation data and clinical data. A comparison with previously reported solutions was also performed. Qualitative and quantitative results show that the proposed method can lead to a promising improvement of LDCT image quality.

The rotation invariance of the classical disc-based moments, such as Zernike moments (ZMs), pseudo-ZMs (PZMs), and orthogonal Fourier-Mellin moments (OFMMs), makes them attractive as descriptors for the purpose of recognition tasks. However, less work has been performed for the generalization of these moment functions. In this paper, four general forms are developed to obtain a class of disc-based generalized radial polynomials that are orthogonal over the unit circle. These radial polynomials are scaled to ensure numerical stability, and some useful properties are discussed for potential applications they could be used in. Then, these scaled radial polynomials are used as kernel functions to construct a series of unit discbased generalized orthogonal moments (DGMs). The variation of parameters in DGMs can form various types of orthogonal moments: 1) generalized ZMs; 2) generalized PZMs; and 3) generalized OFMMs. The classical ZMs, PZMs, and OFMMs correspond to a special case of these three generalized moments for which the free parameter α = 0. Each member of this family will share some excellent properties for image representation and recognition tasks, such as orthogonality and rotation invariance. In addition, we have also developed two algorithms, the so-called m-recursive and n-recursive methods for the computation of these proposed radial polynomials to improve the numerical stability. Experimental results show that the proposed methods are superior to the classical disc-based moments in terms of image representation capability and classification accuracy.