Learning continuous image representations is recently gaining popularity for image super-resolution (SR) because of its ability to reconstruct high-resolution images with arbitrary scales from low-resolution inputs. Existing methods mostly ensemble nearby features to predict the new pixel at any queried coordinate in the SR image. Such a local ensemble suffers from some limitations: i) it has no learnable parameters and it neglects the similarity of the visual features; ii) it has a limited receptive field and cannot ensemble relevant features in a large field which are important in an image; iii) it inherently has a gap with real camera imaging since it only depends on the coordinate. To address these issues, this paper proposes a continuous implicit attention-in-attention network, called CiaoSR. We explicitly design an implicit attention network to learn the ensemble weights for the nearby local features. Furthermore, we embed a scale-aware attention in this implicit attention network to exploit additional non-local information. Extensive experiments on benchmark datasets demonstrate CiaoSR significantly outperforms the existing single image super resolution (SISR) methods with the same backbone. In addition, the proposed method also achieves the state-of-the-art performance on the arbitrary-scale SR task. The effectiveness of the method is also demonstrated on the real-world SR setting. More importantly, CiaoSR can be flexibly integrated into any backbone to improve the SR performance.

Neural Radiance Field (NeRF) has revolutionized free viewpoint rendering tasks and achieved impressive results. However, the efficiency and accuracy problems hinder its wide applications. To address these issues, we propose Geometry-Aware Generalized Neural Radiance Field (GARF) with a geometry-aware dynamic sampling (GADS) strategy to perform real-time novel view rendering and unsupervised depth estimation on unseen scenes without per-scene optimization. Distinct from most existing generalized NeRFs, our framework infers the unseen scenes on both pixel-scale and geometry-scale with only a few input images. More specifically, our method learns common attributes of novel-view synthesis by an encoder-decoder structure and a point-level learnable multi-view feature fusion module which helps avoid occlusion. To preserve scene characteristics in the generalized model, we introduce an unsupervised depth estimation module to derive the coarse geometry, narrow down the ray sampling interval to proximity space of the estimated surface and sample in expectation maximum position, constituting Geometry-Aware Dynamic Sampling strategy (GADS). Moreover, we introduce a Multi-level Semantic Consistency loss (MSC) to assist more informative representation learning. Extensive experiments on indoor and outdoor datasets show that comparing with state-of-the-art generalized NeRF methods, GARF reduces samples by more than 25\%, while improving rendering quality and 3D geometry estimation.

Recent years we have witnessed rapid development in NeRF-based image rendering due to its high quality. However, point clouds rendering is somehow less explored. Compared to NeRF-based rendering which suffers from dense spatial sampling, point clouds rendering is naturally less computation intensive, which enables its deployment in mobile computing device. In this work, we focus on boosting the image quality of point clouds rendering with a compact model design. We first analyze the adaption of the volume rendering formulation on point clouds. Based on the analysis, we simplify the NeRF representation to a spatial mapping function which only requires single evaluation per pixel. Further, motivated by ray marching, we rectify the the noisy raw point clouds to the estimated intersection between rays and surfaces as queried coordinates, which could avoid \textit{spatial frequency collapse} and neighbor point disturbance. Composed of rasterization, spatial mapping and the refinement stages, our method achieves the state-of-the-art performance on point clouds rendering, outperforming prior works by notable margins, with a smaller model size. We obtain a PSNR of 31.74 on NeRF-Synthetic, 25.88 on ScanNet and 30.81 on DTU. Code and data are publicly available at https://github.com/seanywang0408/RadianceMapping.

随着推荐系统变得越来越复杂和复杂，它们通常会缺乏公平和透明度。为建议提供强大而公正的解释，人们越来越关注，因为它可以帮助解决这些问题并提高推荐系统的信任度和信息性。然而，尽管事实是为人类生成了这种解释，这些人类对具有适当情绪的信息做出更强烈反应，但在为建议解释时，人们缺乏对情绪的考虑。发现当前的解释生成模型可以夸大某些情绪，而无需准确捕获基本的语调或含义。在本文中，我们提出了一种基于多头变压器的新方法，称为“情感感知变压器”，以解释推荐（情感者），以产生更健壮，公平和情感增强的解释。为了衡量产生的解释的语言质量和情感公平性，我们采用自动文本指标和人类的看法进行评估。在具有多个评估指标的三个广泛使用基准数据集上进行的实验表明，情感者在文本质量，解释性和对情感分布的公平性方面始终优于现有的最新解释生成模型。 Emoter的实施将作为开源工具包发布，以支持进一步的研究。

在图像中恢复任意缺失区域的合理和现实内容是一个重要而挑战性的任务。尽管最近的图像批量模型在生动的视觉细节方面取得了重大进展，但它们仍然可以导致纹理模糊或由于在处理更复杂的场景时由于上下文模糊而导致的结构扭曲。为了解决这个问题，我们提出了通过学习来自特定借口任务的多尺度语义代理的想法激励的语义金字塔网络（SPN）可以大大使图像中局部缺失内容的恢复极大地利益。 SPN由两个组件组成。首先，它将语义前视图从托管模型蒸馏到多尺度特征金字塔，实现对全局背景和局部结构的一致了解。在现有的学习者内，我们提供了一个可选模块，用于变分推理，以实现由各种学习的前沿驱动的概率图像染色。 SPN的第二组件是完全上下文感知的图像生成器，其在与（随机）先前金字塔一起自适应地和逐渐地改进低级视觉表示。我们将先前的学习者和图像发生器培训为统一模型，而无需任何后处理。我们的方法在多个数据集中实现了本领域的最先进，包括在确定性和概率的侵略设置下，包括Parket2，Paris Streetview，Celeba和Celeba-HQ。

对于视频识别任务，总结了视频片段的整个内容的全局表示为最终性能发挥着重要作用。然而，现有的视频架构通常通过使用简单的全局平均池（GAP）方法来生成它，这具有有限的能力捕获视频的复杂动态。对于图像识别任务，存在证据表明协方差汇总具有比GAP更强的表示能力。遗憾的是，在图像识别中使用的这种普通协方差池是无数的代表，它不能模拟视频中固有的时空结构。因此，本文提出了一个时间 - 细心的协方差池（TCP），插入深度架构结束时，以产生强大的视频表示。具体而言，我们的TCP首先开发一个时间注意力模块，以适应性地校准后续协方差汇集的时空特征，近似地产生细心的协方差表示。然后，时间协方差汇总执行临界协方差表示的时间汇集，以表征校准特征的帧内相关性和帧间互相关。因此，所提出的TCP可以捕获复杂的时间动态。最后，引入了快速矩阵功率归一化以利用协方差表示的几何形状。请注意，我们的TCP是模型 - 不可知的，可以灵活地集成到任何视频架构中，导致TCPNet用于有效的视频识别。使用各种视频架构的六个基准（例如动力学，某事物和电力）的广泛实验显示我们的TCPNet明显优于其对应物，同时具有强大的泛化能力。源代码公开可用。

Recent works on domain adaptation reveal the effectiveness of adversarial learning on filling the discrepancy between source and target domains. However, two common limitations exist in current adversarial-learning-based methods. First, samples from two domains alone are not sufficient to ensure domain-invariance at most part of latent space. Second, the domain discriminator involved in these methods can only judge real or fake with the guidance of hard label, while it is more reasonable to use soft scores to evaluate the generated images or features, i.e., to fully utilize the inter-domain information. In this paper, we present adversarial domain adaptation with domain mixup (DM-ADA), which guarantees domain-invariance in a more continuous latent space and guides the domain discriminator in judging samples' difference relative to source and target domains. Domain mixup is jointly conducted on pixel and feature level to improve the robustness of models. Extensive experiments prove that the proposed approach can achieve superior performance on tasks with various degrees of domain shift and data complexity.

Mitosis nuclei count is one of the important indicators for the pathological diagnosis of breast cancer. The manual annotation needs experienced pathologists, which is very time-consuming and inefficient. With the development of deep learning methods, some models with good performance have emerged, but the generalization ability should be further strengthened. In this paper, we propose a two-stage mitosis segmentation and classification method, named SCMitosis. Firstly, the segmentation performance with a high recall rate is achieved by the proposed depthwise separable convolution residual block and channel-spatial attention gate. Then, a classification network is cascaded to further improve the detection performance of mitosis nuclei. The proposed model is verified on the ICPR 2012 dataset, and the highest F-score value of 0.8687 is obtained compared with the current state-of-the-art algorithms. In addition, the model also achieves good performance on GZMH dataset, which is prepared by our group and will be firstly released with the publication of this paper. The code will be available at: https://github.com/antifen/mitosis-nuclei-segmentation.

Depth map super-resolution (DSR) has been a fundamental task for 3D computer vision. While arbitrary scale DSR is a more realistic setting in this scenario, previous approaches predominantly suffer from the issue of inefficient real-numbered scale upsampling. To explicitly address this issue, we propose a novel continuous depth representation for DSR. The heart of this representation is our proposed Geometric Spatial Aggregator (GSA), which exploits a distance field modulated by arbitrarily upsampled target gridding, through which the geometric information is explicitly introduced into feature aggregation and target generation. Furthermore, bricking with GSA, we present a transformer-style backbone named GeoDSR, which possesses a principled way to construct the functional mapping between local coordinates and the high-resolution output results, empowering our model with the advantage of arbitrary shape transformation ready to help diverse zooming demand. Extensive experimental results on standard depth map benchmarks, e.g., NYU v2, have demonstrated that the proposed framework achieves significant restoration gain in arbitrary scale depth map super-resolution compared with the prior art. Our codes are available at https://github.com/nana01219/GeoDSR.

成功的材料选择对于设计和制造产品的设计自动化至关重要。设计师通过通过性能，制造性和可持续性评估选择最合适的材料来利用他们的知识和经验来创建高质量的设计。智能工具可以通过提供从先前的设计中学到的建议来帮助具有不同专业知识的设计师。为了实现这一目标，我们介绍了一个图表表示学习框架，该框架支持组装中身体的物质预测。我们将材料选择任务作为节点级预测任务，对CAD模型的汇编图表示，并使用图形神经网络（GNN）对其进行处理。在Fusion 360画廊数据集上执行的三个实验协议的评估表明我们的方法的可行性，达到了0.75 TOP-3 Micro-F1分数。提出的框架可以扩展到大型数据集，并将设计师的知识纳入学习过程。这些功能使该框架可以作为设计自动化的推荐系统以及未来工作的基准，从而缩小了人类设计师与智能设计代理之间的差距。