Recent studies on semi-supervised semantic segmentation (SSS) have seen fast progress. Despite their promising performance, current state-of-the-art methods tend to increasingly complex designs at the cost of introducing more network components and additional training procedures. Differently, in this work, we follow a standard teacher-student framework and propose AugSeg, a simple and clean approach that focuses mainly on data perturbations to boost the SSS performance. We argue that various data augmentations should be adjusted to better adapt to the semi-supervised scenarios instead of directly applying these techniques from supervised learning. Specifically, we adopt a simplified intensity-based augmentation that selects a random number of data transformations with uniformly sampling distortion strengths from a continuous space. Based on the estimated confidence of the model on different unlabeled samples, we also randomly inject labelled information to augment the unlabeled samples in an adaptive manner. Without bells and whistles, our simple AugSeg can readily achieve new state-of-the-art performance on SSS benchmarks under different partition protocols.

在这项工作中，我们重新审视了弱到较强的一致性框架，该框架由半监视分类的FixMatch推广，在该分类中，对弱扰动的图像的预测可作为其强烈扰动版本的监督。有趣的是，我们观察到，这种简单的管道已经转移到我们的细分方案时已经在最近的高级工作中取得了竞争成果。它的成功在很大程度上依赖于强大数据增强的手动设计，但是，这可能是有限的，并且不足以探索更广泛的扰动空间。在此激励的情况下，我们提出了一个辅助特征扰动流作为补充，从而导致了扩大的扰动空间。另一方面，为了充分探测原始的图像级增强，我们提出了一种双流扰动技术，从而使两个强大的观点能够同时受到共同的弱视图的指导。因此，我们整体统一的双流扰动方法（Unipatch）在Pascal，CityScapes和Coco基准的所有评估方案中都显着超过所有现有方法。我们还证明了我们方法在遥感解释和医学图像分析中的优越性。代码可从https://github.com/liheyoung/unimatch获得。

具有多传感器的3D对象检测对于自主驾驶和机器人技术的准确可靠感知系统至关重要。现有的3D探测器通过采用两阶段范式来显着提高准确性，这仅依靠激光点云进行3D提案的细化。尽管令人印象深刻，但点云的稀疏性，尤其是对于遥远的点，使得仅激光雷达的完善模块难以准确识别和定位对象。要解决这个问题，我们提出了一种新颖的多模式两阶段方法FusionRcnn，有效，有效地融合了感兴趣区域（ROI）的点云和摄像头图像。 FusionRcnn自适应地整合了LiDAR的稀疏几何信息和统一注意机制中相机的密集纹理信息。具体而言，它首先利用RoiPooling获得具有统一大小的图像集，并通过在ROI提取步骤中的建议中采样原始点来获取点设置；然后利用模式内的自我注意力来增强域特异性特征，此后通过精心设计的跨注意事项融合了来自两种模态的信息。FusionRCNN从根本上是插件，并支持不同的单阶段方法与不同的单阶段方法。几乎没有建筑变化。对Kitti和Waymo基准测试的广泛实验表明，我们的方法显着提高了流行探测器的性能。可取，FusionRCNN在Waymo上的FusionRCNN显着提高了强大的第二基线，而Waymo上的MAP则超过6.14％，并且优于竞争两阶段方法的表现。代码将很快在https://github.com/xxlbigbrother/fusion-rcnn上发布。

Benefiting from color independence, illumination invariance and location discrimination attributed by the depth map, it can provide important supplemental information for extracting salient objects in complex environments. However, high-quality depth sensors are expensive and can not be widely applied. While general depth sensors produce the noisy and sparse depth information, which brings the depth-based networks with irreversible interference. In this paper, we propose a novel multi-task and multi-modal filtered transformer (MMFT) network for RGB-D salient object detection (SOD). Specifically, we unify three complementary tasks: depth estimation, salient object detection and contour estimation. The multi-task mechanism promotes the model to learn the task-aware features from the auxiliary tasks. In this way, the depth information can be completed and purified. Moreover, we introduce a multi-modal filtered transformer (MFT) module, which equips with three modality-specific filters to generate the transformer-enhanced feature for each modality. The proposed model works in a depth-free style during the testing phase. Experiments show that it not only significantly surpasses the depth-based RGB-D SOD methods on multiple datasets, but also precisely predicts a high-quality depth map and salient contour at the same time. And, the resulted depth map can help existing RGB-D SOD methods obtain significant performance gain. The source code will be publicly available at https://github.com/Xiaoqi-Zhao-DLUT/MMFT.

大多数现有的RGB-D突出物体检测方法利用卷积操作并构建复杂的交织融合结构来实现跨模型信息集成。卷积操作的固有局部连接将基于卷积的方法的性能进行了限制到天花板的性能。在这项工作中，我们从全球信息对齐和转换的角度重新思考此任务。具体地，所提出的方法（Transcmd）级联几个跨模型集成单元来构造基于自上而下的变换器的信息传播路径（TIPP）。 Transcmd将多尺度和多模态特征集成作为序列到序列上下文传播和内置于变压器上的更新过程。此外，考虑到二次复杂性W.R.T.输入令牌的数量，我们设计了具有可接受的计算成本的修补程序令牌重新嵌入策略（Ptre）。七个RGB-D SOD基准数据集上的实验结果表明，在配备TIPP时，简单的两流编码器 - 解码器框架可以超越最先进的基于CNN的方法。

通过当地地区的点特征聚合来捕获的细粒度几何是对象识别和场景理解在点云中的关键。然而，现有的卓越点云骨架通常包含最大/平均池用于局部特征聚集，这在很大程度上忽略了点的位置分布，导致细粒结构组装不足。为了缓解这一瓶颈，我们提出了一个有效的替代品，可以使用新颖的图形表示明确地模拟了本地点之间的空间关系，并以位置自适应方式聚合特征，从而实现位置敏感的表示聚合特征。具体而言，Papooling分别由两个关键步骤，图形结构和特征聚合组成，分别负责构造与将中心点连接的边缘与本地区域中的每个相邻点连接的曲线图组成，以将它们的相对位置信息映射到通道 - 明智的细心权重，以及基于通过图形卷积网络（GCN）的生成权重自适应地聚合局部点特征。 Papooling简单而且有效，并且足够灵活，可以随时为PointNet ++和DGCNN等不同的流行律源，作为即插即说运算符。关于各种任务的广泛实验，从3D形状分类，部分分段对场景分割良好的表明，伪装可以显着提高预测准确性，而具有最小的额外计算开销。代码将被释放。

推断在时间知识图中的缺失的事实是一项关键任务，已被广泛探索。在时间推理任务中的外推更具挑战性，并且由于没有直接的历史事实来预测，逐渐引起研究人员的注意力。以前的作品试图申请进化的代表学习，以解决推断问题。然而，这些技术没有明确地利用各种时序感知属性表示，即，推理性能受到历史长度的显着影响。为了减轻推理未来缺失事实时的时间依赖，我们提出了一种记忆触发的决策（MTDM）网络，该网络包括瞬态记忆，长期记忆和深回忆。具体地，瞬态学习网络认为瞬态存储器作为静态知识图，并且时间感知的经常性演化网络通过长短期存储器的一系列经常性演化单元来学习表示。每个演化单元由结构编码器组成，以聚合边缘信息，具有用于更新实体的属性表示的Gating单元的时间编码器。 MTDM利用制备的残余多关系聚合器作为结构编码器来解决多跳覆盖问题。我们还介绍了更好地理解事件溶解过程的溶解学习限制。广泛的实验证明了MTDM减轻了历史依赖性并实现了最先进的预测性能。此外，与最先进的基线相比，MTDM显示了更快的收敛速度和训练速度。

现有的基于CNNS的RGB-D突出物体检测（SOD）网络全部需要在想象网上预先预先磨削以学习层次结构功能，有助于提供良好的初始化。但是，大规模数据集的收集和注释是耗时和昂贵的。在本文中，我们利用自我监督的表示学习（SSL）来设计两个借口任务：跨模型自动编码器和深度轮廓估计。我们的借口任务只需要几个和未标记的RGB-D数据集来执行预先润廓，这使得网络捕获丰富的语义上下文并降低两个模态之间的间隙，从而为下游任务提供有效的初始化。此外，对于RGB-D SOD中的跨模态融合的固有问题，我们提出了一种一致性差异聚合（CDA）模块，其将单个特征融合分成多路径融合，以实现对一致和差分信息的充分看法。 CDA模块是通用的，适用于跨模型和交叉级别融合。关于六个基准数据集的广泛实验表明，我们的自我监督净化模型对想象成的最先进的方法有利地表现出有利的。源代码将在\ textColor {红色} {\ url {https://github.com/xiaoqi-zhao-dlut/sslsod}}上公开可用。

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.