MPI-Flow: Learning Realistic Optical Flow with Multiplane Images

The accuracy of learning-based optical flow estimation models heavily relies on the realism of the training datasets. Current approaches for generating such datasets either employ synthetic data or generate images with limited realism. However, the domain gap of these data with real-world scenes constrains the generalization of the trained model to real-world applications. To address this issue, we investigate generating realistic optical flow datasets from real-world images. Firstly, to generate highly realistic new images, we construct a layered depth representation, known as multiplane images (MPI), from single-view images. This allows us to generate novel view images that are highly realistic. To generate optical flow maps that correspond accurately to the new image, we calculate the optical flows of each plane using the camera matrix and plane depths. We then project these layered optical flows into the output optical flow map with volume rendering. Secondly, to ensure the realism of motion, we present an independent object motion module that can separate the camera and dynamic object motion in MPI. This module addresses the deficiency in MPI-based single-view methods, where optical flow is generated only by camera motion and does not account for any object movement. We additionally devise a depth-aware inpainting module to merge new images with dynamic objects and address unnatural motion occlusions. We show the superior performance of our method through extensive experiments on real-world datasets. Moreover, our approach achieves state-of-the-art performance in both unsupervised and supervised training of learning-based models. The code will be made publicly available at: \url{https://github.com/Sharpiless/MPI-Flow}.Comment: Accepted to ICCV202

SHARING THE MAJORITY OF A SCREEN BASED ON OBJECT DETECTION

Users typically do not hesitate to click on a screen sharing button. However, their personal information (such as website bookmarks, notifications, and desktop files) may be revealed unwillingly through such a share. Accordingly, techniques are presented herein that protect a users\u27 private information when they share their entire desktop or separate windows. Aspects of the presented techniques adopt a state of the art (SOTA) object detection model and achieve real-time inference. Further, the incorporated algorithm is lightweight but useful in that it will not impact performance, but it will provide a positive user experience

Self-Supervised Deep Equilibrium Models for Inverse Problems with Theoretical Guarantees

Deep equilibrium models (DEQ) have emerged as a powerful alternative to deep unfolding (DU) for image reconstruction. DEQ models-implicit neural networks with effectively infinite number of layers-were shown to achieve state-of-the-art image reconstruction without the memory complexity associated with DU. While the performance of DEQ has been widely investigated, the existing work has primarily focused on the settings where groundtruth data is available for training. We present self-supervised deep equilibrium model (SelfDEQ) as the first self-supervised reconstruction framework for training model-based implicit networks from undersampled and noisy MRI measurements. Our theoretical results show that SelfDEQ can compensate for unbalanced sampling across multiple acquisitions and match the performance of fully supervised DEQ. Our numerical results on in-vivo MRI data show that SelfDEQ leads to state-of-the-art performance using only undersampled and noisy training data

STING suppresses mitochondrial VDAC2 to govern RCC growth independent of innate immunity

STING is an innate immune sensor for immune surveillance of viral/bacterial infection and maintenance of an immune-friendly microenvironment to prevent tumorigenesis. However, if and how STING exerts innate immunity-independent function remains elusive. Here, the authors report that STING expression is increased in renal cell carcinoma (RCC) patients and governs tumor growth through non-canonical innate immune signaling involving mitochondrial ROS maintenance and calcium homeostasis. Mitochondrial voltage-dependent anion channel VDAC2 is identified as a new STING binding partner. STING depletion potentiates VDAC2/GRP75-mediated MERC (mitochondria-ER contact) formation to increase mitochondrial ROS/calcium levels, impairs mitochondria function, and suppresses mTORC1/S6K signaling leading to RCC growth retardation. STING interaction with VDAC2 occurs through STING-C88/C91 palmitoylation and inhibiting STING palmitoyl-transferases ZDHHCs by 2-BP significantly impedes RCC cell growth alone or in combination with sorafenib. Together, these studies reveal an innate immunity-independent function of STING in regulating mitochondrial function and growth in RCC, providing a rationale to target the STING/VDAC2 interaction in treating RCC