ALR-GAN: Adaptive Layout Refinement for Text-to-Image Synthesis

We propose a novel Text-to-Image Generation Network, Adaptive Layout Refinement Generative Adversarial Network (ALR-GAN), to adaptively refine the layout of synthesized images without any auxiliary information. The ALR-GAN includes an Adaptive Layout Refinement (ALR) module and a Layout Visual Refinement (LVR) loss. The ALR module aligns the layout structure (which refers to locations of objects and background) of a synthesized image with that of its corresponding real image. In ALR module, we proposed an Adaptive Layout Refinement (ALR) loss to balance the matching of hard and easy features, for more efficient layout structure matching. Based on the refined layout structure, the LVR loss further refines the visual representation within the layout area. Experimental results on two widely-used datasets show that ALR-GAN performs competitively at the Text-to-Image generation task.Comment: Accepted by TM

Study on friction mechanism and performance of disc brakes for mining motor vehicle

According to the convexity theory, the working principle of the disc brake of mining motor vehicle is analyzed. The dry friction force between brake disc and brake block is mainly composed of three mechanisms, such as meshing effect, adhesion effect and furrow effect. The Link NVH 3900 device is used to simulate the long downhill braking condition with bench test method, which can obtain friction performance with high credibility in different brake conditions. The average friction coefficient and friction stability coefficient are chosen as the evaluation parameter for friction performance judgment. Through the research results, it can be known that the average friction coefficient of the brake decreases linearly with brake pressure increases, the friction stability coefficient under different braking pressure showed numerically larger and smaller fluctuations. When the temperature of disc surface exceeds 200 °C, the resin lubricating film will be produced in the brake block, which will decrease the friction coefficient obviously. The actual braking torque does not increase linearly with the increase of braking pressure, especially when the brake speed is high, the friction coefficient will decrease obviously

Fine-grained Text and Image Guided Point Cloud Completion with CLIP Model

This paper focuses on the recently popular task of point cloud completion guided by multimodal information. Although existing methods have achieved excellent performance by fusing auxiliary images, there are still some deficiencies, including the poor generalization ability of the model and insufficient fine-grained semantic information for extracted features. In this work, we propose a novel multimodal fusion network for point cloud completion, which can simultaneously fuse visual and textual information to predict the semantic and geometric characteristics of incomplete shapes effectively. Specifically, to overcome the lack of prior information caused by the small-scale dataset, we employ a pre-trained vision-language model that is trained with a large amount of image-text pairs. Therefore, the textual and visual encoders of this large-scale model have stronger generalization ability. Then, we propose a multi-stage feature fusion strategy to fuse the textual and visual features into the backbone network progressively. Meanwhile, to further explore the effectiveness of fine-grained text descriptions for point cloud completion, we also build a text corpus with fine-grained descriptions, which can provide richer geometric details for 3D shapes. The rich text descriptions can be used for training and evaluating our network. Extensive quantitative and qualitative experiments demonstrate the superior performance of our method compared to state-of-the-art point cloud completion networks

MRI Abdominal Organ Tissue Identification using Statistical Distance in Color Space

Abstract Magnetic resonance imaging (MRI) is a powerful medical imaging technique to provide detailed images of soft abdomen organ tissues. An automatic organ tissue identification algorithm is useful for physicians to perform initial reading and interpret MRI images. The algorithm presented in the paper uses the distance in color space between centers of organ tissues to identify abdominal organs in MRI images. Experimental results show the algorithm is effective in the RGB, LAB and AB color spaces