Notes

Classes Figured out coming from Creating a Individual Diamond Panel: A great OCHIN Record.
GTR is proposed to randomize the texture of source images into diverse unreal texture styles. It aims to alleviate the reliance of the network on texture while promoting the learning of the domain-invariant cues. In addition, we find the texture difference is not always occurred in entire image and may only appear in some local areas. Therefore, we further propose a LTR mechanism to generate diverse local regions for partially stylizing the source images. Finally, we implement a regularization of Consistency between GTR and LTR (CGL) aiming to harmonize the two proposed mechanisms during training. Extensive experiments on five publicly available datasets (i.e., GTA5, SYNTHIA, Cityscapes, BDDS and Mapillary) with various SRSS settings (i.e., GTA5/SYNTHIA to Cityscapes/BDDS/Mapillary) demonstrate that the proposed method is superior to the state-of-the-art methods for domain generalization based SRSS.Human-Object Interaction (HOI) Detection is an important task to understand how humans interact with objects. Most of the existing works treat this task as an exhaustive triplet 〈 human, verb, object 〉 classification problem. In this paper, we decompose it and propose a novel two-stage graph model to learn the knowledge of interactiveness and interaction in one network, namely, Interactiveness Proposal Graph Network (IPGN). In the first stage, we design a fully connected graph for learning the interactiveness, which distinguishes whether a pair of human and object is interactive or not. Concretely, it generates the interactiveness features to encode high-level semantic interactiveness knowledge for each pair. The class-agnostic interactiveness is a more general and simpler objective, which can be used to provide reasonable proposals for the graph construction in the second stage. In the second stage, a sparsely connected graph is constructed with all interactive pairs selected by the first stage. Specifically, we use the interactiveness knowledge to guide the message passing. By contrast with the feature similarity, it explicitly represents the connections between the nodes. Benefiting from the valid graph reasoning, the node features are well encoded for interaction learning. Experiments show that the proposed method achieves state-of-the-art performance on both V-COCO and HICO-DET datasets.Recent CNN-based methods for image deraining have achieved excellent performance in terms of reconstruction error as well as visual quality. However, these methods are limited in the sense that they can be trained only on fully labeled data. Due to various challenges in obtaining real world fully-labeled image deraining datasets, existing methods are trained only on synthetically generated data and hence, generalize poorly to real-world images. The use of real-world data in training image deraining networks is relatively less explored in the literature. We propose a Gaussian Process-based semi-supervised learning framework which enables the network in learning to derain using synthetic dataset while generalizing better using unlabeled real-world images. More specifically, we model the latent space vectors of unlabeled data using Gaussian Processes, which is then used to compute pseudo-ground-truth for supervising the network on unlabeled data. Selitrectinib The pseudo ground-truth is further used to supervise the network at the intermediate level for the unlabeled data. Through extensive experiments and ablations on several challenging datasets (such as Rain800, Rain200L and DDN-SIRR), we show that the proposed method is able to effectively leverage unlabeled data thereby resulting in significantly better performance as compared to labeled-only training. Additionally, we demonstrate that using unlabeled real-world images in the proposed GP-based framework results in superior performance as compared to the existing methods. Code is available at https//github.com/rajeevyasarla/Syn2Real.While traditional image compression algorithms take a full three-component color representation of an image as input, capturing of such images is done in many applications with Bayer CFA pattern sensors that provide only a single color information per sensor element and position. In order to avoid additional complexity at the encoder side, such CFA pattern images can be compressed directly without prior conversion to a full color image. In this paper, we describe a recent activity of the JPEG committee (ISO SC 29 WG 1) to develop such a compression algorithm in the framework of JPEG XS. It turns out that it is important to understand the "development process" from CFA patterns to full color images in order to optimize the image quality of such a compression algorithm, which we will also describe shortly. We introduce (1) a novel decorrelation step upfront processing (the so-called Star-Tetrix transform), along with (2) a pre-emphasis function to improve the compression efficiency of the subsequent compression algorithm (here, JPEG XS). Our experiments clearly indicate a gain over a RGB compression workflow in terms of complexity and quality (between 1.5dB and more than 4dB depending on the target bitrate). A comparison is also made with other state-of-the-art CFA compression techniques.We report a method to locally assess the complex shear modulus of a viscoelastic medium. The proposed approach is based on the application of a magnetic force to a millimetre-sized steel sphere embedded in the medium and the subsequent monitoring of its dynamical response. A coil is used to create a magnetic field inducing the displacement of the sphere located inside a gelatin phantom. Then, a phased-array system using 3 MHz ultrasound probe operating in pulse-echo mode is used to track the displacement of the sphere. Experiments were conducted on several samples and repeated as a function of phantom temperature. The dynamical response of the sphere measured experimentally is in good agreement with Kelvin-Voigt theory. Since the magnetic force is not affected by weak diamagnetic media, our proposal results in an accurate estimation of the force acting on the inclusion. Consequently, the estimated viscoelastic parameters show excellent robustness and the elastic modulus agrees with the measurements using a quasi-static indentation method, obtaining errors below 10% in the whole temperature range.
My Website: https://www.selleckchem.com/products/loxo-195.html