Existing methods for large-scale point cloud semantic segmentation require expensive, tedious and error-prone manual point-wise annotations. Intuitively, weakly supervised training is a direct solution to reduce the cost of labeling. However, for weakly supervised large-scale point cloud semantic segmentation, too few annotations will inevitably lead to ineffective learning of network. We propose an effective weakly supervised method containing two components to solve the above problem. Firstly, we construct a pretext task, \textit{i.e.,} point cloud colorization, with a self-supervised learning to transfer the learned prior knowledge from a large amount of unlabeled point cloud to a weakly supervised network. In this way, the representation capability of the weakly supervised network can be improved by the guidance from a heterogeneous task. Besides, to generate pseudo label for unlabeled data, a sparse label propagation mechanism is proposed with the help of generated class prototypes, which is used to measure the classification confidence of unlabeled point. Our method is evaluated on large-scale point cloud datasets with different scenarios including indoor and outdoor. The experimental results show the large gain against existing weakly supervised and comparable results to fully supervised methods\footnote{Code based on mindspore: https://github.com/dmcv-ecnu/MindSpore\_ModelZoo/tree/main/WS3\_MindSpore}.

Establishing open and general benchmarks has been a critical driving force behind the success of modern machine learning techniques. As machine learning is being applied to broader domains and tasks, there is a need to establish richer and more diverse benchmarks to better reflect the reality of the application scenarios. Graph learning is an emerging field of machine learning that urgently needs more and better benchmarks. To accommodate the need, we introduce Graph Learning Indexer (GLI), a benchmark curation platform for graph learning. In comparison to existing graph learning benchmark libraries, GLI highlights two novel design objectives. First, GLI is designed to incentivize \emph{dataset contributors}. In particular, we incorporate various measures to minimize the effort of contributing and maintaining a dataset, increase the usability of the contributed dataset, as well as encourage attributions to different contributors of the dataset. Second, GLI is designed to curate a knowledge base, instead of a plain collection, of benchmark datasets. We use multiple sources of meta information to augment the benchmark datasets with \emph{rich characteristics}, so that they can be easily selected and used in downstream research or development. The source code of GLI is available at \url{https://github.com/Graph-Learning-Benchmarks/gli}.

In recent years, using a self-supervised learning framework to learn the general characteristics of graphs has been considered a promising paradigm for graph representation learning. The core of self-supervised learning strategies for graph neural networks lies in constructing suitable positive sample selection strategies. However, existing GNNs typically aggregate information from neighboring nodes to update node representations, leading to an over-reliance on neighboring positive samples, i.e., homophilous samples; while ignoring long-range positive samples, i.e., positive samples that are far apart on the graph but structurally equivalent samples, a problem we call "neighbor bias." This neighbor bias can reduce the generalization performance of GNNs. In this paper, we argue that the generalization properties of GNNs should be determined by combining homogeneous samples and structurally equivalent samples, which we call the "GC combination hypothesis." Therefore, we propose a topological signal-driven self-supervised method. It uses a topological information-guided structural equivalence sampling strategy. First, we extract multiscale topological features using persistent homology. Then we compute the structural equivalence of node pairs based on their topological features. In particular, we design a topological loss function to pull in non-neighboring node pairs with high structural equivalence in the representation space to alleviate neighbor bias. Finally, we use the joint training mechanism to adjust the effect of structural equivalence on the model to fit datasets with different characteristics. We conducted experiments on the node classification task across seven graph datasets. The results show that the model performance can be effectively improved using a strategy of topological signal enhancement.

Recent advances on text-to-image generation have witnessed the rise of diffusion models which act as powerful generative models. Nevertheless, it is not trivial to exploit such latent variable models to capture the dependency among discrete words and meanwhile pursue complex visual-language alignment in image captioning. In this paper, we break the deeply rooted conventions in learning Transformer-based encoder-decoder, and propose a new diffusion model based paradigm tailored for image captioning, namely Semantic-Conditional Diffusion Networks (SCD-Net). Technically, for each input image, we first search the semantically relevant sentences via cross-modal retrieval model to convey the comprehensive semantic information. The rich semantics are further regarded as semantic prior to trigger the learning of Diffusion Transformer, which produces the output sentence in a diffusion process. In SCD-Net, multiple Diffusion Transformer structures are stacked to progressively strengthen the output sentence with better visional-language alignment and linguistical coherence in a cascaded manner. Furthermore, to stabilize the diffusion process, a new self-critical sequence training strategy is designed to guide the learning of SCD-Net with the knowledge of a standard autoregressive Transformer model. Extensive experiments on COCO dataset demonstrate the promising potential of using diffusion models in the challenging image captioning task. Source code is available at \url{https://github.com/YehLi/xmodaler/tree/master/configs/image_caption/scdnet}.

Cone beam computed tomography (CBCT) has been widely used in clinical practice, especially in dental clinics, while the radiation dose of X-rays when capturing has been a long concern in CBCT imaging. Several research works have been proposed to reconstruct high-quality CBCT images from sparse-view 2D projections, but the current state-of-the-arts suffer from artifacts and the lack of fine details. In this paper, we propose SNAF for sparse-view CBCT reconstruction by learning the neural attenuation fields, where we have invented a novel view augmentation strategy to overcome the challenges introduced by insufficient data from sparse input views. Our approach achieves superior performance in terms of high reconstruction quality (30+ PSNR) with only 20 input views (25 times fewer than clinical collections), which outperforms the state-of-the-arts. We have further conducted comprehensive experiments and ablation analysis to validate the effectiveness of our approach.

由于其稀疏和细长的性质，估算3D空间中准确的车道线仍然具有挑战性。在这项工作中，我们提出了M^2-3dlanenet，这是一个有效3D车道检测的多模式框架。旨在集成来自多传感器的互补信息，M^2-3dlanenet首先将多模式特征提取具有模态特异性骨架，然后将它们融合在统一的鸟眼视图（BEV）空间中。具体而言，我们的方法由两个核心组成部分组成。 1）要获得准确的2D-3D映射，我们提出了自上而下的BEV生成。其中，使用线条限制的变形（LRDA）模块可用于以自上而下的方式有效地增强图像特征，从而充分捕获车道的细长特征。之后，它使用深度感知的举重将2D锥体特征投入到3D空间中，并通过枕形生成BEV特征。 2）我们进一步提出了自下而上的BEV融合，该融合通过多尺度的级联注意力汇总了多模式特征，从而集成了来自摄像头和激光雷达传感器的互补信息。足够的实验证明了M^2-3dlanenet的有效性，该实验的有效性超过了先前的最先进方法，即在OpenLane数据集上提高了12.1％的F1-SCORE改善。

玻璃在现实世界中非常普遍。受玻璃区域的不确定性以及玻璃背后的各种复杂场景的影响，玻璃的存在对许多计算机视觉任务构成了严重的挑战，从而使玻璃分割成为重要的计算机视觉任务。玻璃没有自己的视觉外观，而只能传输/反映其周围环境的外观，从而与其他常见对象根本不同。为了解决此类具有挑战性的任务，现有方法通常会探索并结合深网络中不同特征级别的有用线索。由于存在级别不同的特征之间的特征差距，即，深层特征嵌入了更多高级语义，并且更好地定位目标对象，而浅层特征具有更大的空间尺寸，并保持更丰富，更详细的低级信息，因此，将这些特征融合到天真的融合将导致亚最佳溶液。在本文中，我们将有效的特征融合到两个步骤中，以朝着精确的玻璃分割。首先，我们试图通过开发可区分性增强（DE）模块来弥合不同级别特征之间的特征差距，该模块使特定于级别的特征成为更具歧视性的表示，从而减轻了融合不兼容的特征。其次，我们设计了一个基于焦点和探索的融合（FEBF）模块，以通过突出显示常见并探索级别差异特征之间的差异，从而在融合过程中丰富挖掘有用的信息。

图像目标导航是一项具有挑战性的任务，因为它要求代理必须导航到以前看不见的场景中图像指示的目标。当前方法介绍了各种存储机制，这些记忆机制可以保存导航历史记录以解决此任务。但是，这些方法使用内存中的所有观察值来生成导航操作，而无需考虑该内存的哪一部分是有益的。为了解决这一限制，我们提出了Memonav，这是一种用于图像目标导航的新型内存机制，该机制保留了代理商的短期记忆和长期记忆，以改善多进球任务上的导航性能。代理拓扑图上的节点功能存储在短期内存中，因为这些功能已动态更新。为了帮助短期记忆，我们还通过通过图形注意模块连续汇总短期内存来生成长期记忆。 MEMONAV通过基于变压器解码器的遗忘模块保留短期内存的信息部分，然后将此保留的短期内存和长期内存结合到工作内存中。最后，代理使用工作内存进行动作生成。我们在新的多进球导航数据集上评估了我们的模型。实验结果表明，MEMONAV的表现优于SOTA方法，而导航历史悠久的比例较小。从经验上看，结果还表明，我们的模型不太可能被困在僵局中，这进一步验证了Memonav通过减少冗余步骤来提高代理商的导航效率。

语义细分是计算机视觉中的一个流行研究主题，并且在其上做出了许多努力，结果令人印象深刻。在本文中，我们打算搜索可以实时运行此问题的最佳网络结构。为了实现这一目标，我们共同搜索深度，通道，扩张速率和特征空间分辨率，从而导致搜索空间约为2.78*10^324可能的选择。为了处理如此大的搜索空间，我们利用差异架构搜索方法。但是，需要离散地使用使用现有差异方法搜索的体系结构参数，这会导致差异方法找到的架构参数与其离散版本作为体系结构搜索的最终解决方案之间的离散差距。因此，我们从解决方案空间正则化的创新角度来缓解离散差距的问题。具体而言，首先提出了新型的解决方案空间正则化（SSR）损失，以有效鼓励超级网络收敛到其离散。然后，提出了一种新的分层和渐进式解决方案空间缩小方法，以进一步实现较高的搜索效率。此外，我们从理论上表明，SSR损失的优化等同于L_0-NORM正则化，这说明了改善的搜索评估差距。综合实验表明，提出的搜索方案可以有效地找到最佳的网络结构，该结构具有较小的模型大小（1 m）的分割非常快的速度（175 fps），同时保持可比较的精度。

细粒度的动作识别是计算机视觉中的一项具有挑战性的任务。由于细粒的数据集在空间和时间空间中具有较小的类间变化，因此细粒度的动作识别模型需要良好的时间推理和属性动作语义的歧视。利用CNN捕获高级时空特征表示能力以及变压器在捕获潜在语义和全球依赖性方面的建模效率，我们研究了两个结合CNN视觉骨干和变压器编码器以增强良好粒度动作识别的框架：1）基于编码器学习潜在的时间语义，以及2）多模式视频文本交叉编码器，以利用其他文本输入并学习视觉语义和文本语义之间的交叉关联。我们的实验结果表明，我们的变压器编码器框架有效地学习潜在的时间语义和跨模式关联，并且比CNN视觉模型改善了识别性能。我们在firgym基准数据集上实现了新的最先进的性能，用于两种拟议的架构。