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As an amalgamation of landscape design and ichthyology, aquascape endeavors to create visually captivating aquatic environments imbued with artistic allure. Traditional methodologies in aquascape, governed by rigid principles such as composition and color coordination, may inadvertently curtail the aesthetic potential of the landscapes. In this paper, we propose Aquascape Generation based on Stable Diffusion Models (AG‐SDM), prioritizing aesthetic principles and color coordination to offer guiding principles for real artists in Aquascape creation. We meticulously curated and annotated three aquascape datasets with varying aspect ratios to accommodate diverse landscape design requirements regarding dimensions and proportions. Leveraging the Fréchet Inception Distance (FID) metric, we trained AGFID for quality assessment. Extensive experiments validate that our AG‐SDM excels in generating hyper‐realistic underwater landscape images, closely resembling real flora, and achieves state‐of‐the‐art performance in aquascape image generation. [ABSTRACT FROM AUTHOR]

Neural Radiance Field (NeRF) can render complex 3D scenes with viewpoint‐dependent effects. However, less work has been devoted to exploring its limitations in high‐resolution environments, especially when upscaled to ultra‐high resolution (e.g., 4k). Specifically, existing NeRF‐based methods face severe limitations in reconstructing high‐resolution real scenes, for example, a large number of parameters, misalignment of the input data, and over‐smoothing of details. In this paper, we present a novel and effective framework, called De‐NeRF, based on NeRF and deformable convolutional network, to achieve high‐fidelity view synthesis in ultra‐high resolution scenes: (1) marrying the deformable convolution unit which can solve the problem of misaligned input of the high‐resolution data. (2) Presenting a density sparse voxel‐based approach which can greatly reduce the training time while rendering results with higher accuracy. Compared to existing high‐resolution NeRF methods, our approach improves the rendering quality of high‐frequency details and achieves better visual effects in 4K high‐resolution scenes. [ABSTRACT FROM AUTHOR]

Accurate skin lesion segmentation from dermoscopic images is of great importance for skin cancer diagnosis. However, automatic segmentation of melanoma remains a challenging task because it is difficult to incorporate useful texture representations into the learning process. Texture representations are not only related to the local structural information learned by CNN, but also include the global statistical texture information of the input image. In this paper, we propose a transFormer network (SkinFormer) that efficiently extracts and fuses statistical texture representation for Skin lesion segmentation. Specifically, to quantify the statistical texture of input features, a Kurtosis-guided Statistical Counting Operator is designed. We propose Statistical Texture Fusion Transformer and Statistical Texture Enhance Transformer with the help of Kurtosis-guided Statistical Counting Operator by utilizing the transformer's global attention mechanism. The former fuses structural texture information and statistical texture information, and the latter enhances the statistical texture of multi-scale features. Extensive experiments on three publicly available skin lesion datasets validate that our SkinFormer outperforms other SOAT methods, and our method achieves 93.2% Dice score on ISIC 2018. It can be easy to extend SkinFormer to segment 3D images in the future.

Weakly supervised object localization (WSOL) is a challenging and promising task that aims to localize objects solely based on the supervision of image category labels. In the absence of annotated bounding boxes, WSOL methods must employ the intrinsic properties of the image classification task pipeline to generate object localizations. In this work, we propose a WSOL method for exploring the Intrinsic Discrimination and Consistency in the image classification task pipeline, and call it as IDC. First, we develop a Triplet Metrics Based Foreground Modeling (TMFM) framework to directly predict object foreground regions using intrinsic discrimination. Unlike Class Activation Map (CAM) based methods that also rely on intrinsic discrimination, our TMFM framework alleviates the problem of only focusing on the most discriminative parts by optimizing foreground and background regions synergistically. Second, we design a Dual Geometric Transformation Consistency Constraints (DGTC2) training strategy to introduce additional supervision and regularization constraints for WSOL by leveraging intrinsic geometric transformation consistency. The proposed pixel-wise and object-wise consistency constraint losses cost-effectively provide spontaneous supervision for WSOL. Extensive experiments show that our IDC method achieves significant and consistent performance gains compared to existing state-of-the-art WSOL approaches. Code is available at: https://github.com/vignywang/IDC.