Reinforcement learning (RL) operating on attack graphs leveraging cyber terrain principles are used to develop reward and state associated with determination of surveillance detection routes (SDR). This work extends previous efforts on developing RL methods for path analysis within enterprise networks. This work focuses on building SDR where the routes focus on exploring the network services while trying to evade risk. RL is utilized to support the development of these routes by building a reward mechanism that would help in realization of these paths. The RL algorithm is modified to have a novel warm-up phase which decides in the initial exploration which areas of the network are safe to explore based on the rewards and penalty scale factor.

本文介绍了Kings Arena的荣誉，Kings Arena是基于国王荣誉的强化学习（RL）环境，这是世界上最受欢迎的游戏之一。与以前大多数工作中研究的其他环境相比，我们的人对竞争性强化学习提出了新的概括挑战。与对手竞争的一个代理商是一个多代理的问题；它需要概括能力，因为它具有控制和不同的对手竞争的不同目标。我们描述了国王域名荣誉的观察，动作和奖励规范，并提供了一个基于python的开源界面，以与游戏引擎进行通信。我们为纪念国王竞技场的二十个目标英雄提供了各种任务，并为具有可行的计算资源的基于RL的方法提供了初始基线结果。最后，我们展示了国王竞技场的荣誉和对挑战的可能补救措施所面临的概括挑战。所有软件（包括环境级）均可在https://github.com/tencent-ailab/hok_env上公开获得。该文档可在https://aiarena.tencent.com/hok/doc/上获得。

人群的理解由于其重要的实际意义引起了人们对视觉领域的普遍兴趣。不幸的是，没有努力探索桥接自然语言和计算机视觉的多模式领域中的人群理解。参考表达理解（REF）是一项代表性的多模式任务。当前的REF研究更多地集中在一般情况下从多个独特类别中扎根目标对象。很难应用于复杂的现实世界人群的理解。为了填补这一空白，我们提出了一个新的挑战性数据集，称为Refcrowd，该数据集旨在通过参考表达方式寻找人群中的目标人。它不仅需要充分挖掘自然语言信息，而且还需要仔细地专注于目标与具有相似外观的人群之间的细微差异，以实现从语言到视觉的细粒度映射。此外，我们提出了一个细粒度的多模式属性对比网络（FMAC），以在人群的理解中处理参考。它首先将复杂的视觉和语言特征分解为属性感知的多模式特征，然后捕获歧视性但健壮性的细粒属性特征，以有效地区分相似人之间的这些细微差异。所提出的方法优于我们的档案数据集和现有参考数据集中的现有最新方法（SOTA）方法。此外，我们为多模式域中的更深入研究实施了端到端的REF工具箱。我们的数据集和代码可以在：\ url {https://qiuheqian.github.io/datasets/refcrowd/}中可用。

近年来，3D视觉的自我监督预训练引起了研究的兴趣。为了学习信息的表示，许多以前的作品都利用了3D功能的不向导，\ eg，同一场景的视图之间的透视感，深度和RGB图像之间的模态侵权次数，点云和voxels之间的格式不变。尽管他们取得了令人鼓舞的结果，但以前的研究缺乏对这些不稳定的系统性比较。为了解决这个问题，我们的工作首次引入了一个统一的框架，根据该框架可以研究各种预培训方法。我们进行了广泛的实验，并仔细研究了3D预训练中不同不变的贡献。另外，我们提出了一种简单但有效的方法，该方法可以共同预先培训3D编码器和使用对比度学习的深度图编码器。通过我们的方法进行预训练的模型在下游任务方面具有显着的性能提高。例如，预先训练的投票表现优于Sun RGB-D和扫描对象检测基准的先前方法，并具有明显的利润。

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.

Knowledge graphs (KG) have served as the key component of various natural language processing applications. Commonsense knowledge graphs (CKG) are a special type of KG, where entities and relations are composed of free-form text. However, previous works in KG completion and CKG completion suffer from long-tail relations and newly-added relations which do not have many know triples for training. In light of this, few-shot KG completion (FKGC), which requires the strengths of graph representation learning and few-shot learning, has been proposed to challenge the problem of limited annotated data. In this paper, we comprehensively survey previous attempts on such tasks in the form of a series of methods and applications. Specifically, we first introduce FKGC challenges, commonly used KGs, and CKGs. Then we systematically categorize and summarize existing works in terms of the type of KGs and the methods. Finally, we present applications of FKGC models on prediction tasks in different areas and share our thoughts on future research directions of FKGC.

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.

Graph Neural Networks (GNNs) have shown satisfying performance on various graph learning tasks. To achieve better fitting capability, most GNNs are with a large number of parameters, which makes these GNNs computationally expensive. Therefore, it is difficult to deploy them onto edge devices with scarce computational resources, e.g., mobile phones and wearable smart devices. Knowledge Distillation (KD) is a common solution to compress GNNs, where a light-weighted model (i.e., the student model) is encouraged to mimic the behavior of a computationally expensive GNN (i.e., the teacher GNN model). Nevertheless, most existing GNN-based KD methods lack fairness consideration. As a consequence, the student model usually inherits and even exaggerates the bias from the teacher GNN. To handle such a problem, we take initial steps towards fair knowledge distillation for GNNs. Specifically, we first formulate a novel problem of fair knowledge distillation for GNN-based teacher-student frameworks. Then we propose a principled framework named RELIANT to mitigate the bias exhibited by the student model. Notably, the design of RELIANT is decoupled from any specific teacher and student model structures, and thus can be easily adapted to various GNN-based KD frameworks. We perform extensive experiments on multiple real-world datasets, which corroborates that RELIANT achieves less biased GNN knowledge distillation while maintaining high prediction utility.

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.

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.