在本文中，我们专注于探索有效的方法，以更快，准确和域的不可知性语义分割。受到相邻视频帧之间运动对齐的光流的启发，我们提出了一个流对齐模块（FAM），以了解相邻级别的特征映射之间的\ textit {语义流}，并将高级特征广播到高分辨率特征有效地，有效地有效。 。此外，将我们的FAM与共同特征的金字塔结构集成在一起，甚至在轻量重量骨干网络（例如Resnet-18和DFNET）上也表现出优于其他实时方法的性能。然后，为了进一步加快推理过程，我们还提出了一个新型的封闭式双流对齐模块，以直接对齐高分辨率特征图和低分辨率特征图，在该图中我们将改进版本网络称为SFNET-LITE。广泛的实验是在几个具有挑战性的数据集上进行的，结果显示了SFNET和SFNET-LITE的有效性。特别是，建议的SFNET-LITE系列在使用RESNET-18主链和78.8 MIOU以120 fps运行的情况下，使用RTX-3090上的STDC主链在120 fps运行时，在60 fps运行时达到80.1 miou。此外，我们将四个具有挑战性的驾驶数据集（即CityScapes，Mapillary，IDD和BDD）统一到一个大数据集中，我们将其命名为Unified Drive细分（UDS）数据集。它包含不同的域和样式信息。我们基准了UDS上的几项代表性作品。 SFNET和SFNET-LITE仍然可以在UDS上取得最佳的速度和准确性权衡，这在如此新的挑战性环境中是强大的基准。所有代码和模型均可在https://github.com/lxtgh/sfsegnets上公开获得。

视频实例分割（VIS）是一个新的固有多任务问题，旨在在视频序列中检测，细分和跟踪每个实例。现有方法主要基于单帧功能或多个帧的单尺度功能，其中忽略了时间信息或多尺度信息。为了结合时间和比例信息，我们提出了一种时间金字塔路由（TPR）策略，以从两个相邻帧的特征金字塔对有条件地对齐和进行像素级聚集。具体而言，TPR包含两个新的组件，包括动态对齐细胞路由（DACR）和交叉金字塔路由（CPR），其中DACR设计用于跨时间维度对齐和门控金字塔特征，而CPR则在跨音阶范围内暂时汇总的特征。此外，我们的方法是轻巧和插件模块，可以轻松地应用于现有的实例分割方法。在包括YouTube-Vis（2019，2021）和CityScapes-VP在内的三个数据集上进行的广泛实验证明了拟议方法对几种最先进的视频实例和全盘细分方法的有效性和效率。代码将在\ url {https://github.com/lxtgh/temporalpyramidrouting}上公开获得。

最近，一些研究在图像压缩感测（CS）任务中应用了深层卷积神经网络（CNN），以提高重建质量。但是，卷积层通常具有一个小的接受场。因此，使用CNN捕获远程像素相关性是具有挑战性的，这限制了其在Image CS任务中的重建性能。考虑到这一限制，我们为图像CS任务（称为uformer-ics）提出了一个U形变压器。我们通过将CS的先验投影知识集成到原始变压器块中，然后使用基于投影基于投影的变压器块和残留卷积块构建对称重建模型来开发一个基于投影的变压器块。与以前的基于CNN的CS方法相比，只能利用本地图像特征，建议的重建模型可以同时利用图像的局部特征和远程依赖性，以及CS理论的先前投影知识。此外，我们设计了一个自适应采样模型，该模型可以基于块稀疏性自适应采样图像块，这可以确保压缩结果保留在固定采样比下原始图像的最大可能信息。提出的UFORFORFOR-ICS是一个端到端框架，同时学习采样和重建过程。实验结果表明，与现有的基于深度学习的CS方法相比，它的重建性能明显优于重建性能。

Dataset distillation has emerged as a prominent technique to improve data efficiency when training machine learning models. It encapsulates the knowledge from a large dataset into a smaller synthetic dataset. A model trained on this smaller distilled dataset can attain comparable performance to a model trained on the original training dataset. However, the existing dataset distillation techniques mainly aim at achieving the best trade-off between resource usage efficiency and model utility. The security risks stemming from them have not been explored. This study performs the first backdoor attack against the models trained on the data distilled by dataset distillation models in the image domain. Concretely, we inject triggers into the synthetic data during the distillation procedure rather than during the model training stage, where all previous attacks are performed. We propose two types of backdoor attacks, namely NAIVEATTACK and DOORPING. NAIVEATTACK simply adds triggers to the raw data at the initial distillation phase, while DOORPING iteratively updates the triggers during the entire distillation procedure. We conduct extensive evaluations on multiple datasets, architectures, and dataset distillation techniques. Empirical evaluation shows that NAIVEATTACK achieves decent attack success rate (ASR) scores in some cases, while DOORPING reaches higher ASR scores (close to 1.0) in all cases. Furthermore, we conduct a comprehensive ablation study to analyze the factors that may affect the attack performance. Finally, we evaluate multiple defense mechanisms against our backdoor attacks and show that our attacks can practically circumvent these defense mechanisms.

Blind image quality assessment (BIQA) remains challenging due to the diversity of distortion and image content variation, which complicate the distortion patterns crossing different scales and aggravate the difficulty of the regression problem for BIQA. However, existing BIQA methods often fail to consider multi-scale distortion patterns and image content, and little research has been done on learning strategies to make the regression model produce better performance. In this paper, we propose a simple yet effective Progressive Multi-Task Image Quality Assessment (PMT-IQA) model, which contains a multi-scale feature extraction module (MS) and a progressive multi-task learning module (PMT), to help the model learn complex distortion patterns and better optimize the regression issue to align with the law of human learning process from easy to hard. To verify the effectiveness of the proposed PMT-IQA model, we conduct experiments on four widely used public datasets, and the experimental results indicate that the performance of PMT-IQA is superior to the comparison approaches, and both MS and PMT modules improve the model's performance.

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.

Given the increasingly intricate forms of partial differential equations (PDEs) in physics and related fields, computationally solving PDEs without analytic solutions inevitably suffers from the trade-off between accuracy and efficiency. Recent advances in neural operators, a kind of mesh-independent neural-network-based PDE solvers, have suggested the dawn of overcoming this challenge. In this emerging direction, Koopman neural operator (KNO) is a representative demonstration and outperforms other state-of-the-art alternatives in terms of accuracy and efficiency. Here we present KoopmanLab, a self-contained and user-friendly PyTorch module of the Koopman neural operator family for solving partial differential equations. Beyond the original version of KNO, we develop multiple new variants of KNO based on different neural network architectures to improve the general applicability of our module. These variants are validated by mesh-independent and long-term prediction experiments implemented on representative PDEs (e.g., the Navier-Stokes equation and the Bateman-Burgers equation) and ERA5 (i.e., one of the largest high-resolution data sets of global-scale climate fields). These demonstrations suggest the potential of KoopmanLab to be considered in diverse applications of partial differential equations.

A recent study has shown a phenomenon called neural collapse in that the within-class means of features and the classifier weight vectors converge to the vertices of a simplex equiangular tight frame at the terminal phase of training for classification. In this paper, we explore the corresponding structures of the last-layer feature centers and classifiers in semantic segmentation. Based on our empirical and theoretical analysis, we point out that semantic segmentation naturally brings contextual correlation and imbalanced distribution among classes, which breaks the equiangular and maximally separated structure of neural collapse for both feature centers and classifiers. However, such a symmetric structure is beneficial to discrimination for the minor classes. To preserve these advantages, we introduce a regularizer on feature centers to encourage the network to learn features closer to the appealing structure in imbalanced semantic segmentation. Experimental results show that our method can bring significant improvements on both 2D and 3D semantic segmentation benchmarks. Moreover, our method ranks 1st and sets a new record (+6.8% mIoU) on the ScanNet200 test leaderboard. Code will be available at https://github.com/dvlab-research/Imbalanced-Learning.

Benefiting from the intrinsic supervision information exploitation capability, contrastive learning has achieved promising performance in the field of deep graph clustering recently. However, we observe that two drawbacks of the positive and negative sample construction mechanisms limit the performance of existing algorithms from further improvement. 1) The quality of positive samples heavily depends on the carefully designed data augmentations, while inappropriate data augmentations would easily lead to the semantic drift and indiscriminative positive samples. 2) The constructed negative samples are not reliable for ignoring important clustering information. To solve these problems, we propose a Cluster-guided Contrastive deep Graph Clustering network (CCGC) by mining the intrinsic supervision information in the high-confidence clustering results. Specifically, instead of conducting complex node or edge perturbation, we construct two views of the graph by designing special Siamese encoders whose weights are not shared between the sibling sub-networks. Then, guided by the high-confidence clustering information, we carefully select and construct the positive samples from the same high-confidence cluster in two views. Moreover, to construct semantic meaningful negative sample pairs, we regard the centers of different high-confidence clusters as negative samples, thus improving the discriminative capability and reliability of the constructed sample pairs. Lastly, we design an objective function to pull close the samples from the same cluster while pushing away those from other clusters by maximizing and minimizing the cross-view cosine similarity between positive and negative samples. Extensive experimental results on six datasets demonstrate the effectiveness of CCGC compared with the existing state-of-the-art algorithms.

Compressed videos often exhibit visually annoying artifacts, known as Perceivable Encoding Artifacts (PEAs), which dramatically degrade video visual quality. Subjective and objective measures capable of identifying and quantifying various types of PEAs are critical in improving visual quality. In this paper, we investigate the influence of four spatial PEAs (i.e. blurring, blocking, bleeding, and ringing) and two temporal PEAs (i.e. flickering and floating) on video quality. For spatial artifacts, we propose a visual saliency model with a low computational cost and higher consistency with human visual perception. In terms of temporal artifacts, self-attention based TimeSFormer is improved to detect temporal artifacts. Based on the six types of PEAs, a quality metric called Saliency-Aware Spatio-Temporal Artifacts Measurement (SSTAM) is proposed. Experimental results demonstrate that the proposed method outperforms state-of-the-art metrics. We believe that SSTAM will be beneficial for optimizing video coding techniques.