Deep learning based video depression analysis has been recently an interesting and challenging topic. Most of existing works focus on learning single-scale facial dynamics of participants for depression detection. Besides, they usually adopt expensive deep learning models with high computational complexity, resulting in difficulty in real-time clinical applications. To address these two issues, this work proposes a lightweight Multi-scale Temporal Difference Attention Networks (MTDAN) integrating the temporal difference and attention mechanism to model both short-term and long-term temporal facial behaviors for automated video depression detection. Initially, two simple yet effective sub-branches, i.e., a Short-term Temporal Difference Attention Network (ST-TDAN), and a Long-term Temporal Difference Attention Network (LT-TDAN), are designed to perform individually short-term and long-term depressive behavior modeling. Then, a simple Interactive Multi-head Attention Fusion (IMHAF) strategy is employed for integrating short-term and long-term spatiotemporal features, followed by a linear fully-collected layer for depression score prediction. Experiments on two public AVEC2013 and AVEC2014 datasets show that our proposed method not only achieves highly competitive performance to state-of-the-art methods, but also has much smaller computational complexity than them on video depression detection tasks.

Active contour model (ACM) has been a successful method for image segmentation. The existing ACMs poorly segment the images with intensity inhomogeneity or non-homogeneity, and the results are highly depend on initial position of the contour. To overcome these disadvantages, we proposed a fuzzy region-based active contour driven by weighting global and local fitting energy, wherein we propose a fuzzy region energy with local spatial image information, which has been proved convex and ensures the segmentation results independent of initialization, to motivate an initial evolving curve of pseudo level set function (LSF), followed by the pseudo LSF and further smoothed by an edge energy to accurately extract the object boundaries and maintain its distance feature. In addition, in the fuzzy region energy, instead of using the Euler-Lagrange equation to minimize the energy functional, we develop a more direct method to calculate the change of the fuzzy region energy. Experimental results on synthetic and real images with high noise and intensity inhomogeneity show that the proposed model can obtain better performance than the state-of-the-art active contour models, and takes less running time. The code is available at: https://github.com/fangchj2002/FRAGL.

Active contour models (ACMs) have been widely applied in the field of image segmentation. However, it is still very challenging to construct an efficient ACM to segment images with intensity inhomogeneity. In this paper, a novel ACM guided by global and local signed energy-based pressure force (GLSEPF) is proposed. First, by computing the energy difference between the inner and outer energies of the evolution curve, a global signed energy-based pressure force (GSEPF) is designed, which can improve the robustness to initial curves. Second, a local signed energy-based pressure force (LSEPF) is introduced by computing the pixel-by-pixel energy difference within local neighborhood region, which can handle images with intensity inhomogeneity and noise. Finally, the global image information and the local energy information are used for the global and local force propagation functions, respectively. The global and local variances are used to automatically balance the weights of the GSEPF and the LSEPF, which can solve the problem of setting parameters. Meanwhile, a regularization term and a penalty term are applied to avoid the re-initialization process during iterations and smooth the level set function. Experimental results on different types of images demonstrate that the proposed model is more robust than the popular region-based and mixed ACMs for segmenting images with intensity inhomogeneity and noise. The code is available at: https://github.com/HuaxiangLiu/GLSEPF/.

This study presents a novel active contour model (ACM) driven by weighted global and local region-based signed pressure force (SPF) to segment images in the presence of intensity inhomogeneity and noise. First, an adaptive weighted global region-based SPF (GRSPF) function as the driving centers is designed based on the global image information, which is based on the normalized global intensity to update the weights of the inner and outer regions of the curve during iterations. Second, by introducing the normalized absolute local intensity differences as the weighs of the inner and outer regions, an adaptive weighted local region-based SPF (LRSPF) function is similarly defined. Third, instead of setting a fixed force, a force propagation function is introduced to automatically balance the interior and exterior forces according to the image feature. Meanwhile, by combing the adaptive GWSPF and LWSPF functions, a weighted hybrid region-based SPF function is defined, which can improve the efficiency and accuracy of the proposed model. The experimental results on real images demonstrate that the proposed model is more robust than the popular region-based ACMs for segmenting images with intensity inhomogeneity and noise. The code is available at https://github.com/fangchj2002/WHRSPF.