使用深神经网络的自动诊断可以帮助眼科医生检测致盲眼病湿时期相关的黄斑变性（AMD）。湿AMD具有两种类似的亚型，新生血管和息肉脉络膜（PCV）。然而，由于数据收集困难和图像之间的相似性，大多数研究仅达到了湿-amd的粗粒粒度，而不是湿-amd亚型中的细粒。为了解决这个问题，在本文中，我们提出了一种知识驱动的细粒度湿法AMD分类模型（KFWC），以对数据不足的细粒疾病进行分类。随着将输入图像的10个病变迹象的先验知识引入KFWC，我们的目标是通过多标签分类预培训加速KFWC，以定位细粒疾病分类任务中的决定性图像特征因此实现了更好的分类。同时，KFWC还可以提供良好的可解释性，并有效地减轻湿法菌疾病分类领域的数据收集和注释压力。实验证明了澳大利亚委员会的持续99.71％的有效性，以及对数据驱动的W / O知识和眼科医生相当大的改进，比最强的基线和4.14％对眼科医生的速度为6.69％。

为了开发直肠癌的自动化工作流程，三维形成式放射治疗计划，结合了深度学习（DL）孔径预测和前向规划算法。我们设计了一种算法来自动化临床工作流程，以使用现场场地进行计划。对555名患者进行了训练，验证和测试DL模型，以自动生成一级和增强场的光圈形状。网络输入是数字重建的X射线照相，总肿瘤体积（GTV）和Nodal GTV。一名医师以5分制（> 3个可以接受）为20名患者的每个孔径为每个孔径评分。然后开发了一种计划算法，以使用楔形和子场的组合创建均匀剂量。该算法迭代识别热点卷，创建子字段并在没有用户干预的情况下优化光束重量。使用具有不同设置的临床光圈对20例患者进行了测试，并由医生评分结果计划（4例计划/患者）。端到端的工作流程通过医生对39名使用DL生成的孔径和计划算法进行了测试和评分。预测的孔的骰子得分分别为0.95、0.94和0.90，分别为侧面，外侧和升压场。 100％，95％和87.5％的后侧，外侧和升压孔分别为临床上可接受。在85％和50％的患者中，楔形计划和非界定计划在临床上是可以接受的。最终计划的热点剂量百分比从121％（$ \ $ 14％）降低到处方剂量的109％（$ \ pm $ 5％）。自动生成的光圈和优化现场计划的综合端到端工作流程为38/39（97％）的患者提供了可接受的计划。我们已经成功地自动化了临床工作流程，以为我们的机构生成放射疗法计划。

已经证明，基于度量学习的人重新识别（Reid）系统继承了深度神经网络（DNN）的脆弱性，这很容易被普通ararar公制攻击所迷惑。现有的工作主要依赖于对公制防御的对抗培训，并且没有完全研究更多方法。通过探索攻击对潜在特征的影响，我们提出了针对度量攻击和防御方法的有针对性的方法。在公制攻击方面，我们使用本地颜色偏差来构建输入的类内变化以攻击颜色特征。在公制防御方面，我们提出了一种联合防御方法，包括两个主动防御和被动防御的部分。主动防御有助于通过构建来自多模式图像的不同输入来增强模型到色彩变化的鲁棒性和多种方式的结构关系的学习，并且被动防御通过迂回缩放来利用变化像素空间中的结构特征的不变性以保护结构特征在消除一些对抗噪声的同时。广泛的实验表明，拟议的联合防御与现有的对抗公制防御方法相比，不仅与同时进行多次攻击而且也没有显着降低模型的泛化能力。代码可在https://github.com/finger-monkey/multi-modal_joint_defence上获得。

计算机视觉的挑战之一是它需要适应可变环境中的颜色偏差。因此，将颜色偏差对预测的不利影响最小化是视觉任务的主要目标之一。当前的解决方案着重于使用生成模型增强训练数据以增强输入变化的不变性。但是，这种方法通常会引入新的噪声，从而限制了生成数据的增益。为此，本文提出了一种策略，消除了偏差的偏差，该偏差称为随机颜色辍学（RCD）。我们的假设是，如果查询图像和画廊图像之间存在颜色偏差，那么在忽略颜色信息之后，某些示例的检索结果会更好。具体而言，该策略通过在训练数据中辍学的部分颜色信息来平衡神经网络中颜色特征和无关的特征之间的权重，以克服颜色devitaion的效果。所提出的RCD可以与各种现有的REID模型相结合而不更改学习策略，并且可以应用于其他计算机视野字段，例如对象检测。在几个REID基线和三个常见的大规模数据集（例如Market1501，Dukemtmc和MSMT17）上进行的实验已验证了该方法的有效性。跨域测试的实验表明，该策略显着消除了域间隙。此外，为了了解RCD的工作机制，我们从分类的角度分析了该策略的有效性，这表明在具有强大域变化的视觉任务中，最好利用许多而不是所有颜色信息。

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.