“搜索”或“导航到”？当找到一个物体时，这两个选择总是在我们的潜意识中出现。在看到目标之前，我们根据经验搜索目标。看到目标后，我们记住目标位置并导航到。但是，最近在对象导航字段中的方法几乎仅考虑使用对象关联来增强“搜索”阶段，同时忽略了“导航到”阶段的重要性。因此，本文提出了双重自适应思维（DAT）方法，以灵活调整不同导航阶段的不同思维策略。双重思考包括具有目标位置能力的对象关联能力和导航思维的搜索思维。为了使导航思维更有效，我们设计了面向目标的内存图（TOMG）来存储历史目标信息和目标感知的多规模聚合器（TAMSA）以编码相对目标位置。我们在AI2-数据集上评估我们的方法。与最先进的方法（SOTA）方法相比，我们的方法报告成功率10.8％，21.5％和15.7％（SR），成功按路径长度（SPL）加权（SPL）和成功通过导航效率加权（SNE） ）， 分别。

为了调查现实世界中联邦学习的异质性，我们将经典的联合学习概括为联合的异性任务学习，这强调了参与者在数据分布和学习任务方面的联盟学习中的不一致性。我们还提出了B-FHTL，这是一种联合的杂项任务学习基准，该基准包括模拟数据集，FL协议和统一的评估机制。 B-FHTL数据集包含三个精心设计的联合学习任务，异质性增加。每个任务都使用不同的非IID数据和学习任务模拟客户端。为了确保不同的FL算法之间的公平比较，B-FHTL通过提供高级API来避免隐私泄漏，在整个FL协议中构建，并预设跨越不同的学习任务的最常见评估指标，例如回归，分类，文本，文本，文本此外，我们还比较了B-FHTL中联合多任务学习，联合个性化和联合元学习领域的FL算法，并突出了联盟异质任务学习的异质性和困难的影响。我们的基准测试，包括联合数据集，协议，评估机制和初步实验，可在https://github.com/alibaba/federatedscope/tree/master/master/master/benchmark/b-fhtl上开放。

联合学习（FL）的令人难以置信的发展使计算机视觉和自然语言处理领域的各种任务受益，而现有的TFF和FATE等现有框架使在现实应用程序中的部署变得容易。但是，即使图形数据很普遍，联合图形学习（FGL）由于其独特的特征和要求而没有得到很好的支持。缺乏与FGL相关的框架增加了完成可再现研究和在现实世界应用中部署的努力。在本文中，我们首先讨论了创建易于使用的FGL软件包的挑战，因此提出了我们实施的FederatedScope-GNN（FS-G）的包裹，该软件包提供了（1）统一的模块化视图并表达FGL算法； （2）用于开箱即用的FGL功能的综合数据和模型； （3）有效的模型自动调整组件； （4）现成的隐私攻击和防御能力。我们通过进行广泛的实验来验证FS-G的有效性，该实验同时获得了许多有关FGL的宝贵见解。此外，我们采用FS-G在现实世界中的电子商务方案中为FGL应用程序提供服务，在该场景中获得的改进表明了巨大的潜在业务利益。我们在https://github.com/alibaba/federatedscope上公开发布FS-G，作为FederatedScope的子模型，以促进FGL的研究，并启用由于缺乏专用包装而无法无视的广泛应用。

尽管现有联合学习平台（FL）平台已取得了显着的进展，以提供开发基础架构，但这些平台可能无法很好地应对各种异质性带来的挑战，包括参与者本地数据，资源，行为和学习目标中的异质性。为了填补这一空白，在本文中，我们提出了一个名为FederatedScope的新型FL平台，该平台采用事件驱动的架构为用户提供极大的灵活性，以独立描述不同参与者的行为。这样的设计使用户可以轻松地描述参与者具有各种本地培训过程，学习目标和后端，并通过同步或异步培训策略将其协调为FL课程。 FederatedScope为易于使用和灵活的平台提供了丰富类型的插入操作和组件，以有效地进行进一步开发，并且我们实施了几个重要组件，以更好地帮助用户进行隐私保护，攻击模拟和自动调整。我们已经在https://github.com/alibaba/federatedscope上发布了FederatedScope，以在各种情况下促进联邦学习的学术研究和工业部署。

对象导航任务要求代理根据视觉信息在未知环境中找到特定对象。以前，图形卷积被用于隐式探索对象之间的关系。但是，由于对象之间可见性的差异，很容易在对象注意中产生偏见。因此，在本文中，我们提出了一个定向的对象注意（DOA）图，以指导代理显式地学习对象之间的注意力关系，从而减少对象的注意偏置。特别是，我们使用DOA图在原始图像上分别对对象特征和无偏的自适应图像注意（UAIA）进行无偏的自适应对象注意（UAOA）。为了区分不同分支的特征，提出了一种简洁的自适应分支分布（ABED）方法。我们在AI2-数据集上评估我们的方法。与最先进的方法（SOTA）方法相比，我们的方法报告了7.4％，8.1％和17.6％的成功率（SR），成功按路径长度（SPL）加权（SPL）并通过动作效率加权成功（SAE） ）， 分别。

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.