Accurate 2D human pose estimation from images is vital for understanding human actions. However, deploying the latest models, e.g., regression-based models, on resource-limited devices remains challenging due to their high computational requirements. In this paper, we address the resolution dilemma in regression-based multiperson pose estimation, where low-resolution inputs cause performance degradation, while high-resolution inputs drastically increase computational costs. To achieve a lightweight regression approach, it becomes crucial to enhance the model's capabilities in low-resolution scenarios. We propose the staggered alignment self-distillation (SASD) method and a corresponding network architecture. Our approach involves training two twin networks with shared weights: a high-resolution network and a low-resolution network. The high-resolution network serves as a teacher, guiding the learning process of the low-resolution network through feature map staggered alignment. The knowledge from the high-resolution network enhances the performance of the low-resolution network during low-resolution inference. Additionally, we employ a normalized skeleton loss to capture the loss of bone-related structure during training. Through extensive experiments on the MS-COCO and CrowdPose datasets, we demonstrate the superiority of our proposed method over state-of-the-art, lightweight multiperson pose estimation techniques, achieving much better performance with lower computational costs. Furthermore, our method achieves comparable performance to recent advanced regression-based pose estimation methods but with only 1/4 of the computational cost.

Dictionary learning aims to adapt elementary codewords directly from training data so that each training signal can be best approximated by a linear combination of only a few codewords. Following the two-stage iterative processes: sparse coding and dictionary update, that are commonly used, for example, in the algorithms of MOD and K-SVD, we propose a novel framework that allows one to update an arbitrary set of codewords and the corresponding sparse coefficients simultaneously, hence termed simultaneous codeword optimization (SimCO). Under this framework, we have developed two algorithms, namely the primitive and the regularized SimCO. Simulations are provided to show the advantages of our approach over the K-SVD algorithm in terms of both learning performance and running speed.

A block-based compressed sensing approach coupled with binary time-frequency masking is presented for the underdetermined speech separation problem. The proposed algorithm consists of multiple steps. First, the mixed signals are segmented to a number of blocks. For each block, the unknown mixing matrix is estimated in the transform domain by a clustering algorithm. Using the estimated mixing matrix, the sources are recovered by a compressed sensing approach. The coarsely separated sources are then used to estimate the time-frequency binary masks which are further applied to enhance the separation performance. The separated source components from all the blocks are concatenated to reconstruct the whole signal. Numerical experiments are provided to show the improved separation performance of the proposed algorithm, as compared with two recent approaches. The block-based operation has the advantage in improving considerably the computational efficiency of the compressed sensing algorithm without degrading its separation performance.