The development of social media user stance detection and bot detection methods rely heavily on large-scale and high-quality benchmarks. However, in addition to low annotation quality, existing benchmarks generally have incomplete user relationships, suppressing graph-based account detection research. To address these issues, we propose a Multi-Relational Graph-Based Twitter Account Detection Benchmark (MGTAB), the first standardized graph-based benchmark for account detection. To our knowledge, MGTAB was built based on the largest original data in the field, with over 1.55 million users and 130 million tweets. MGTAB contains 10,199 expert-annotated users and 7 types of relationships, ensuring high-quality annotation and diversified relations. In MGTAB, we extracted the 20 user property features with the greatest information gain and user tweet features as the user features. In addition, we performed a thorough evaluation of MGTAB and other public datasets. Our experiments found that graph-based approaches are generally more effective than feature-based approaches and perform better when introducing multiple relations. By analyzing experiment results, we identify effective approaches for account detection and provide potential future research directions in this field. Our benchmark and standardized evaluation procedures are freely available at: https://github.com/GraphDetec/MGTAB.

Real-world image super-resolution (RISR) has received increased focus for improving the quality of SR images under unknown complex degradation. Existing methods rely on the heavy SR models to enhance low-resolution (LR) images of different degradation levels, which significantly restricts their practical deployments on resource-limited devices. In this paper, we propose a novel Dynamic Channel Splitting scheme for efficient Real-world Image Super-Resolution, termed DCS-RISR. Specifically, we first introduce the light degradation prediction network to regress the degradation vector to simulate the real-world degradations, upon which the channel splitting vector is generated as the input for an efficient SR model. Then, a learnable octave convolution block is proposed to adaptively decide the channel splitting scale for low- and high-frequency features at each block, reducing computation overhead and memory cost by offering the large scale to low-frequency features and the small scale to the high ones. To further improve the RISR performance, Non-local regularization is employed to supplement the knowledge of patches from LR and HR subspace with free-computation inference. Extensive experiments demonstrate the effectiveness of DCS-RISR on different benchmark datasets. Our DCS-RISR not only achieves the best trade-off between computation/parameter and PSNR/SSIM metric, and also effectively handles real-world images with different degradation levels.

Explainability of Graph Neural Networks (GNNs) is critical to various GNN applications but remains an open challenge. A convincing explanation should be both necessary and sufficient simultaneously. However, existing GNN explaining approaches focus on only one of the two aspects, necessity or sufficiency, or a trade-off between the two. To search for the most necessary and sufficient explanation, the Probability of Necessity and Sufficiency (PNS) can be applied since it can mathematically quantify the necessity and sufficiency of an explanation. Nevertheless, the difficulty of obtaining PNS due to non-monotonicity and the challenge of counterfactual estimation limits its wide use. To address the non-identifiability of PNS, we resort to a lower bound of PNS that can be optimized via counterfactual estimation, and propose Necessary and Sufficient Explanation for GNN (NSEG) via optimizing that lower bound. Specifically, we employ nearest neighbor matching to generate counterfactual samples for the features, which is different from the random perturbation. In particular, NSEG combines the edges and node features to generate an explanation, where the common edge explanation is a special case of the combined explanation. Empirical study shows that NSEG achieves excellent performance in generating the most necessary and sufficient explanations among a series of state-of-the-art methods.

Graph convolutional networks (GCNs) have achieved great success in graph representation learning by extracting high-level features from nodes and their topology. Since GCNs generally follow a message-passing mechanism, each node aggregates information from its first-order neighbour to update its representation. As a result, the representations of nodes with edges between them should be positively correlated and thus can be considered positive samples. However, there are more non-neighbour nodes in the whole graph, which provide diverse and useful information for the representation update. Two non-adjacent nodes usually have different representations, which can be seen as negative samples. Besides the node representations, the structural information of the graph is also crucial for learning. In this paper, we used quality-diversity decomposition in determinant point processes (DPP) to obtain diverse negative samples. When defining a distribution on diverse subsets of all non-neighbouring nodes, we incorporate both graph structure information and node representations. Since the DPP sampling process requires matrix eigenvalue decomposition, we propose a new shortest-path-base method to improve computational efficiency. Finally, we incorporate the obtained negative samples into the graph convolution operation. The ideas are evaluated empirically in experiments on node classification tasks. These experiments show that the newly proposed methods not only improve the overall performance of standard representation learning but also significantly alleviate over-smoothing problems.

Weakly supervised semantic segmentation is typically inspired by class activation maps, which serve as pseudo masks with class-discriminative regions highlighted. Although tremendous efforts have been made to recall precise and complete locations for each class, existing methods still commonly suffer from the unsolicited Out-of-Candidate (OC) error predictions that not belongs to the label candidates, which could be avoidable since the contradiction with image-level class tags is easy to be detected. In this paper, we develop a group ranking-based Out-of-Candidate Rectification (OCR) mechanism in a plug-and-play fashion. Firstly, we adaptively split the semantic categories into In-Candidate (IC) and OC groups for each OC pixel according to their prior annotation correlation and posterior prediction correlation. Then, we derive a differentiable rectification loss to force OC pixels to shift to the IC group. Incorporating our OCR with seminal baselines (e.g., AffinityNet, SEAM, MCTformer), we can achieve remarkable performance gains on both Pascal VOC (+3.2%, +3.3%, +0.8% mIoU) and MS COCO (+1.0%, +1.3%, +0.5% mIoU) datasets with negligible extra training overhead, which justifies the effectiveness and generality of our OCR.

Recent success of vision transformers has inspired a series of vision backbones with novel feature transformation paradigms, which report steady performance gain. Although the novel feature transformation designs are often claimed as the source of gain, some backbones may benefit from advanced engineering techniques, which makes it hard to identify the real gain from the key feature transformation operators. In this paper, we aim to identify real gain of popular convolution and attention operators and make an in-depth study of them. We observe that the main difference among these feature transformation modules, e.g., attention or convolution, lies in the way of spatial feature aggregation, or the so-called "spatial token mixer" (STM). Hence, we first elaborate a unified architecture to eliminate the unfair impact of different engineering techniques, and then fit STMs into this architecture for comparison. Based on various experiments on upstream/downstream tasks and the analysis of inductive bias, we find that the engineering techniques boost the performance significantly, but the performance gap still exists among different STMs. The detailed analysis also reveals some interesting findings of different STMs, such as effective receptive fields and invariance tests. The code and trained models will be publicly available at https://github.com/OpenGVLab/STM-Evaluation

在本文中，我们表明样品的欧几里得规范的差异可以在空间翻译和划分归一化之后对语义差异甚至混乱做出贡献。为了解决这个问题，我们提出了一种直观但有效的方法，以均衡样品向量的欧几里得规范。具体来说，我们$ l_2 $ - 在批准之前将每个样品向量归一化，因此样品向量的幅度相同。由于所提出的方法结合了$ L_2 $归一化和批量归一化，因此我们将我们的方法称为$ L_2 $ bn。 $ l_2 $ bn可以增强阶层内特征的紧凑性，并扩大阶层间特征的差异。此外，它可以帮助梯度收敛到稳定的量表。 $ l_2 $ bn易于实现，并且可以在没有任何其他参数和超参数的情况下发挥其效果。因此，它可以用作神经网络的基本归一化方法。我们通过对图像分类和声学场景分类任务进行各种模型的广泛实验来评估$ L_2 $亿美元的有效性。实验结果表明，$ L_2 $ bn能够提高各种神经网络模型的概括能力，并取得了可观的性能改进。

建议制度，依靠历史观察数据来模仿用户和物品之间的复杂关系，取得了巨大的成功，在现实世界中取得了巨大的成功。选择偏见是现有的现有观测数据基于方法的最重要问题之一，其实际上是由多种类型的不观察室的暴露策略引起的（例如促销和假期效应）。虽然已经提出了各种方法来解决这个问题，但它们主要依赖于隐含的脱叠技术，但没有明确建立未观察的曝光策略。通过明确重建曝光策略（简称休息），我们将推荐问题正式化为反事实推理，并提出了脱叠的社会推荐方法。在休息时，我们假设项目的曝光由潜在曝光策略，用户和项目控制。基于上述生成过程，首先通过识别分析提供我们方法的理论保证。其次，在社交网络和项目的帮助下，我们采用了变分自动编码器来重建潜在的曝光策略。第三，我们通过利用回收的曝光策略制定基于反事实推理的建议算法。四个现实世界数据集的实验，包括三个已发布的数据集和一个私人微信官方帐户数据集，展示了几种最先进的方法的显着改进。

在许多科学领域，观察数据中的因果发现是一项重要但具有挑战性的任务。最近，一种称为宣传的非组合定向无环约束的方法将因果结构学习问题作为使用最小二乘损失的连续优化问题。尽管在标准高斯噪声假设下，最小二乘损耗函数是合理的，但如果假设不存在，则受到限制。在这项工作中，我们从理论上表明，违反高斯噪声假设将阻碍因果方向的识别，从而使因果强度以及线性案例中的噪声和噪声方差完全确定。在非线性情况下的噪音。因此，我们提出了一个更一般的基于熵的损失，理论上与任何噪声分布下的可能性得分一致。我们对合成数据和现实世界数据进行了广泛的经验评估，以验证所提出的方法的有效性，并表明我们的方法在结构锤距离，错误发现率和真实的正速率矩阵方面达到了最佳状态。

Witnessing the impressive achievements of pre-training techniques on large-scale data in the field of computer vision and natural language processing, we wonder whether this idea could be adapted in a grab-and-go spirit, and mitigate the sample inefficiency problem for visuomotor driving. Given the highly dynamic and variant nature of the input, the visuomotor driving task inherently lacks view and translation invariance, and the visual input contains massive irrelevant information for decision making, resulting in predominant pre-training approaches from general vision less suitable for the autonomous driving task. To this end, we propose PPGeo (Policy Pre-training via Geometric modeling), an intuitive and straightforward fully self-supervised framework curated for the policy pretraining in visuomotor driving. We aim at learning policy representations as a powerful abstraction by modeling 3D geometric scenes on large-scale unlabeled and uncalibrated YouTube driving videos. The proposed PPGeo is performed in two stages to support effective self-supervised training. In the first stage, the geometric modeling framework generates pose and depth predictions simultaneously, with two consecutive frames as input. In the second stage, the visual encoder learns driving policy representation by predicting the future ego-motion and optimizing with the photometric error based on current visual observation only. As such, the pre-trained visual encoder is equipped with rich driving policy related representations and thereby competent for multiple visuomotor driving tasks. Extensive experiments covering a wide span of challenging scenarios have demonstrated the superiority of our proposed approach, where improvements range from 2% to even over 100% with very limited data. Code and models will be available at https://github.com/OpenDriveLab/PPGeo.