从相机中检测3D车道是自动车辆的一个上升问题。在此任务中，正确的相机姿势是生成准确通道的关键，可以将图像从透视图转换为顶视图。通过这种转变，我们可以摆脱透视效果，使得3D车道看起来相似，可以精确地装配低阶多项式。然而，主流3D车道探测器依赖于其他传感器提供的完美相机姿势，这是昂贵的并且遇到多传感器校准问题。为了克服这个问题，我们建议通过用双级框架估计来自单个图像的摄像机姿势来预测3D车道。第一阶段针对从透视图图像的相机姿势任务。为了提高姿势估计，我们介绍了辅助3D车道任务和几何约束，从多任务学习中受益，这增强了3D和2D之间的常规，以及在上述两个任务中的兼容性。第二阶段针对3D Lane任务。它使用先前估计的姿势来生成包含距离不变通道外观的顶视图，以预测准确的3D车道。实验表明，如果没有地面真相相机姿势，我们的方法优于最先进的完美相机姿势的方法，并且具有最少的参数和计算。代码在https://github.com/liuruijin17/clgo提供。

深度神经网络（DNN）越来越多地应用于恶意软件检测中，其鲁棒性已广泛争论。传统上，对抗性示例生成方案依赖于详细的模型信息（基于梯度的方法）或许多样本来训练替代模型，在大多数情况下都无法使用。我们提出了基于实例的攻击的概念。我们的方案是可解释的，可以在黑箱环境中起作用。给定一个特定的二进制示例和恶意软件分类器，我们使用数据增强策略来生成足够的数据，我们可以从中训练一个简单的可解释模型。我们通过显示特定二进制的不同部分的重量来解释检测模型。通过分析解释，我们发现数据小节在Windows PE恶意软件检测中起重要作用。我们提出了一个新函数，以保存可以应用于数据子分校的转换算法。通过采用我们提出的二进制多样化技术，我们消除了最加权零件对产生对抗性例子的影响。在某些情况下，我们的算法可以欺骗DNN，成功率接近100 \％。我们的方法的表现优于最新方法。最重要的方面是我们的方法在黑框设置中运行，并且可以通过域知识来验证结果。我们的分析模型可以帮助人们改善恶意软件探测器的鲁棒性。

功能级二进制代码相似性检测在网络空间安全性领域至关重要。它可以帮助我们在发布的软件中找到错误并检测专利侵权，并在预防供应链攻击中起关键作用。一个实用的嵌入学习框架依赖于矢量表示系统的鲁棒性以及功能对注释的准确性。传统上，基于学习的方法是基于学习的方法。但是，用准确的标签对不同的功能对进行注释非常困难。这些监督的学习方法很容易被过度训练，并且遭受了鲁棒性问题的困扰。为了减轻这些问题，我们提出了FUN2VEC：二进制功能级表示的对比学习框架。我们采用一种无监督的学习方法，并将二进制代码相似性检测作为实例歧视。 FUN2VEC直接用于分解的二进制功能，并且可以使用任何编码器实现。它不需要标记类似或不同信息的手动。我们使用编译器优化选项和代码混淆技术来生成增强数据。我们的实验结果表明，我们的方法超过了准确性的最先进，并且在几次射击设置中具有很大的优势。

在6G无线通信网络中，按需服务提供是一个至关重要的问题，因为新兴服务的需求大大不同，并且网络资源变得越来越异质和动态。在本文中，我们研究了按需无线资源编排问题，重点是编排决策过程的计算延迟。具体而言，我们将决策延迟延迟到优化问题。然后，提出了一个基于动态的神经网络（DYNN）的方法，可以根据服务要求调整模型复杂性。我们进一步建立一个知识库，代表服务需求之间的关系，可用的计算资源和资源分配绩效。通过利用知识，可以及时选择DYNN的宽度，从而进一步提高编排的性能。仿真结果表明，所提出的方案大大优于传统的静态神经网络，并且在按需服务提供方面也表现出足够的灵活性。

一个良好的动作效果预测模型，称为环境模型，对于在机器人控制，推荐系统和患者治疗选择等许多领域中实现样本有效的决策政策学习非常重要。我们可以使用这种模型进行无限的试验来确定适当的行动，以便可以节省现实世界中的查询成本。它要求模型正确处理看不见的数据，也称为反事实数据。但是，标准数据拟合技术不会自动实现这种概括能力，通常会导致不可靠的模型。在这项工作中，我们在模型学习中引入了反事实风险最小化（CQRM），以推广到特定目标策略查询的反事实数据集。由于目标策略在政策学习中可能是各种各样且未知的，因此我们提出了一个对抗性CQRM目标，其中模型在对抗性策略查询的反事实数据上学习，并最终得出可拖延的解决方案Galileo。我们还发现，对抗性CQRM与对抗模型学习密切相关，从而解释了后者的有效性。我们将伽利略应用于综合任务和现实应用程序中。结果表明，伽利略对反事实数据做出了准确的预测，从而显着改善了现实世界测试的策略。

In this paper, we propose a robust 3D detector, named Cross Modal Transformer (CMT), for end-to-end 3D multi-modal detection. Without explicit view transformation, CMT takes the image and point clouds tokens as inputs and directly outputs accurate 3D bounding boxes. The spatial alignment of multi-modal tokens is performed implicitly, by encoding the 3D points into multi-modal features. The core design of CMT is quite simple while its performance is impressive. CMT obtains 73.0% NDS on nuScenes benchmark. Moreover, CMT has a strong robustness even if the LiDAR is missing. Code will be released at https://github.com/junjie18/CMT.

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.

Few Shot Instance Segmentation (FSIS) requires models to detect and segment novel classes with limited several support examples. In this work, we explore a simple yet unified solution for FSIS as well as its incremental variants, and introduce a new framework named Reference Twice (RefT) to fully explore the relationship between support/query features based on a Transformer-like framework. Our key insights are two folds: Firstly, with the aid of support masks, we can generate dynamic class centers more appropriately to re-weight query features. Secondly, we find that support object queries have already encoded key factors after base training. In this way, the query features can be enhanced twice from two aspects, i.e., feature-level and instance-level. In particular, we firstly design a mask-based dynamic weighting module to enhance support features and then propose to link object queries for better calibration via cross-attention. After the above steps, the novel classes can be improved significantly over our strong baseline. Additionally, our new framework can be easily extended to incremental FSIS with minor modification. When benchmarking results on the COCO dataset for FSIS, gFSIS, and iFSIS settings, our method achieves a competitive performance compared to existing approaches across different shots, e.g., we boost nAP by noticeable +8.2/+9.4 over the current state-of-the-art FSIS method for 10/30-shot. We further demonstrate the superiority of our approach on Few Shot Object Detection. Code and model will be available.

This paper focuses on designing efficient models with low parameters and FLOPs for dense predictions. Even though CNN-based lightweight methods have achieved stunning results after years of research, trading-off model accuracy and constrained resources still need further improvements. This work rethinks the essential unity of efficient Inverted Residual Block in MobileNetv2 and effective Transformer in ViT, inductively abstracting a general concept of Meta-Mobile Block, and we argue that the specific instantiation is very important to model performance though sharing the same framework. Motivated by this phenomenon, we deduce a simple yet efficient modern \textbf{I}nverted \textbf{R}esidual \textbf{M}obile \textbf{B}lock (iRMB) for mobile applications, which absorbs CNN-like efficiency to model short-distance dependency and Transformer-like dynamic modeling capability to learn long-distance interactions. Furthermore, we design a ResNet-like 4-phase \textbf{E}fficient \textbf{MO}del (EMO) based only on a series of iRMBs for dense applications. Massive experiments on ImageNet-1K, COCO2017, and ADE20K benchmarks demonstrate the superiority of our EMO over state-of-the-art methods, \eg, our EMO-1M/2M/5M achieve 71.5, 75.1, and 78.4 Top-1 that surpass \textbf{SoTA} CNN-/Transformer-based models, while trading-off the model accuracy and efficiency well.

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.