Recaptured Raw Screen Image and Video Demoir\'eing via Channel and Spatial Modulations

Capturing screen contents by smartphone cameras has become a common way for information sharing. However, these images and videos are often degraded by moir\'e patterns, which are caused by frequency aliasing between the camera filter array and digital display grids. We observe that the moir\'e patterns in raw domain is simpler than those in sRGB domain, and the moir\'e patterns in raw color channels have different properties. Therefore, we propose an image and video demoir\'eing network tailored for raw inputs. We introduce a color-separated feature branch, and it is fused with the traditional feature-mixed branch via channel and spatial modulations. Specifically, the channel modulation utilizes modulated color-separated features to enhance the color-mixed features. The spatial modulation utilizes the feature with large receptive field to modulate the feature with small receptive field. In addition, we build the first well-aligned raw video demoir\'eing (RawVDemoir\'e) dataset and propose an efficient temporal alignment method by inserting alternating patterns. Experiments demonstrate that our method achieves state-of-the-art performance for both image and video demori\'eing. We have released the code and dataset in https://github.com/tju-chengyijia/VD_raw

rPPG-MAE: Self-supervised Pre-training with Masked Autoencoders for Remote Physiological Measurement

Remote photoplethysmography (rPPG) is an important technique for perceiving human vital signs, which has received extensive attention. For a long time, researchers have focused on supervised methods that rely on large amounts of labeled data. These methods are limited by the requirement for large amounts of data and the difficulty of acquiring ground truth physiological signals. To address these issues, several self-supervised methods based on contrastive learning have been proposed. However, they focus on the contrastive learning between samples, which neglect the inherent self-similar prior in physiological signals and seem to have a limited ability to cope with noisy. In this paper, a linear self-supervised reconstruction task was designed for extracting the inherent self-similar prior in physiological signals. Besides, a specific noise-insensitive strategy was explored for reducing the interference of motion and illumination. The proposed framework in this paper, namely rPPG-MAE, demonstrates excellent performance even on the challenging VIPL-HR dataset. We also evaluate the proposed method on two public datasets, namely PURE and UBFC-rPPG. The results show that our method not only outperforms existing self-supervised methods but also exceeds the state-of-the-art (SOTA) supervised methods. One important observation is that the quality of the dataset seems more important than the size in self-supervised pre-training of rPPG. The source code is released at https://github.com/linuxsino/rPPG-MAE

Multi-scale Promoted Self-adjusting Correlation Learning for Facial Action Unit Detection

Facial Action Unit (AU) detection is a crucial task in affective computing and social robotics as it helps to identify emotions expressed through facial expressions. Anatomically, there are innumerable correlations between AUs, which contain rich information and are vital for AU detection. Previous methods used fixed AU correlations based on expert experience or statistical rules on specific benchmarks, but it is challenging to comprehensively reflect complex correlations between AUs via hand-crafted settings. There are alternative methods that employ a fully connected graph to learn these dependencies exhaustively. However, these approaches can result in a computational explosion and high dependency with a large dataset. To address these challenges, this paper proposes a novel self-adjusting AU-correlation learning (SACL) method with less computation for AU detection. This method adaptively learns and updates AU correlation graphs by efficiently leveraging the characteristics of different levels of AU motion and emotion representation information extracted in different stages of the network. Moreover, this paper explores the role of multi-scale learning in correlation information extraction, and design a simple yet effective multi-scale feature learning (MSFL) method to promote better performance in AU detection. By integrating AU correlation information with multi-scale features, the proposed method obtains a more robust feature representation for the final AU detection. Extensive experiments show that the proposed method outperforms the state-of-the-art methods on widely used AU detection benchmark datasets, with only 28.7\% and 12.0\% of the parameters and FLOPs of the best method, respectively. The code for this method is available at \url{https://github.com/linuxsino/Self-adjusting-AU}.Comment: 13pages, 7 figure

Learning to reconstruct and understand indoor scenes from sparse views

This paper proposes a new method for simultaneous 3D reconstruction and semantic segmentation for indoor scenes. Unlike existing methods that require recording a video using a color camera and/or a depth camera, our method only needs a small number of (e.g., 3~5) color images from uncalibrated sparse views, which significantly simplifies data acquisition and broadens applicable scenarios. To achieve promising 3D reconstruction from sparse views with limited overlap, our method first recovers the depth map and semantic information for each view, and then fuses the depth maps into a 3D scene. To this end, we design an iterative deep architecture, named IterNet, to estimate the depth map and semantic segmentation alternately. To obtain accurate alignment between views with limited overlap, we further propose a joint global and local registration method to reconstruct a 3D scene with semantic information. We also make available a new indoor synthetic dataset, containing photorealistic high-resolution RGB images, accurate depth maps and pixel-level semantic labels for thousands of complex layouts. Experimental results on public datasets and our dataset demonstrate that our method achieves more accurate depth estimation, smaller semantic segmentation errors, and better 3D reconstruction results over state-of-the-art methods

A high-energy-density supercapacitor with graphene-CMK-5 as the electrode and ionic liquid as the electrolyte

A graphene-based supercapacitor electrode suitable for ionic liquid electrolytes was designed and prepared based on the electrostatic interactions between negatively charged graphene oxides (GO) and positively charged mesoporous carbon CMK-5 platelets. Thermal annealing of the GO-CMK-5 composite under an inert atmosphere yielded a hierarchical carbon nanostructure with CMK-5 platelets uniformly intercalated between the GO sheets. The electrochemical results demonstrated that the CMK-5 platelets with straight and short mesochannels served as a highway for the fast transport of electrolyte ions, while the separated graphene sheets with more exposed electrochemical surface area favored the formation of electrical double layer capacitance. The RGO-CMK-5 electrode exhibited a specific capacitance of 144.4 F g-1 in 1-ethyl-3-methylimidazolium tetrafluoroborate ionic liquid electrolyte, which can be charged/discharged at an operating voltage of 3.5 V. As a result, an energy density of 60.7 W h kg-1 and a power density as high as 10 kW kg-1 were achieved, which outperforms most of the present graphene-based supercapacitors. Moreover, the superior rate performance together with the excellent cycle performance makes the RGO-CMK-5 composite a promising candidate for next generation supercapacitor electrodes

Inhibition of Epac2 Attenuates Neural Cell Apoptosis and Improves Neurological Deficits in a Rat Model of Traumatic Brain Injury

Traumatic brain injury (TBI) is a major cause of mortality and disability worldwide. TBI-induced neuronal apoptosis is one of the main contributors to the secondary injury process. The aim of this study is to investigate the involvement of Exchange protein directly activated by cAMP 2 (Epac2) on TBI. We found that the expression level of Epac2 surrounding the injured area of brain in rats of TBI model was significantly increased at 12 h after TBI. The role of Epac2 in TBI was further explored by using a selective Epac2 antagonist ESI-05 to decrease the Epac2 expression. We discovered that inhibition of Epac2 could improve the neurological impairment and attenuate brain edema following TBI. The Epac2 inhibition effectively reduced neuronal cell death and P38 MAPK signaling pathway may be involved in this process. Our results suggest that inhibition of Epac2 may be a potential therapy for TBI by reducing the neural cell death, alleviating brain edema and improving neurologic deficits