以前的无监督句子嵌入研究集中在数据增强方法上，例如辍学和基于规则的句子转换方法。但是，这些方法限制了控制句子增强观点的细粒语义。这导致监督信号不足以捕获类似句子的语义相似性。在这项工作中，我们发现使用邻居句子可以捕获相似句子之间更准确的语义相似性。基于这一发现，我们提出了RankEncoder，该发现使用了输入句子和语料库中的句子之间的关系来训练无监督的句子编码器。我们从三个角度评估rankencoder：1）语义文本相似性性能，2）相似句子对的功效，以及3）rankencoder的普遍性。实验结果表明，与先前的最新性能相比，Rankencoder达到80.07 \％Spearman的相关性，绝​​对提高了1.1％。在类似的句子对上，改进更加显着，改善了1.73％。另外，我们证明了RankEncoder普遍适用于现有的无监督句子编码器。

深度学习已被广​​泛用于医学图像细分和其他方面。但是，现有的医学图像分割模型的性能受到获得足够数量的高质量数据的挑战的限制。为了克服限制，我们提出了一个新的视觉医学图像分割模型LVIT（语言符合视觉变压器）。在我们的模型中，引入了医学文本注释，以弥补图像数据的质量缺陷。此外，文本信息可以在一定程度上指导伪标签的产生，并进一步保证半监督学习中伪标签的质量。我们还提出了指数伪标签迭代机制（EPI），以帮助扩展LVIT和像素级注意模块（PLAM）的半监督版本，以保留图像的局部特征。在我们的模型中，LV（语言视觉）损失旨在直接使用文本信息监督未标记图像的培训。为了验证LVIT的性能，我们构建了包含病理图像，X射线等的多模式医学分割数据集（图像 +文本）。实验结果表明，我们提出的LVIT在完全和半监督条件下具有更好的分割性能。代码和数据集可在https://github.com/huanglizi/lvit上找到。

盲人面部修复（BFR）旨在从低品质的图像中恢复高质量的面部图像，并通常求助于面部先验，以改善恢复性能。但是，当前的方法仍然遇到两个主要困难：1）如何在不进行大规模调整的情况下得出强大的网络体系结构； 2）如何从一个网络中的多个面部先验捕获互补信息以提高恢复性能。为此，我们提出了一个面部修复搜索网络（FRSNET），以适应我们指定的搜索空间内的合适特征提取体系结构，这可以直接有助于恢复质量。在FRSNET的基础上，我们通过多个学习方案进一步设计了多个面部先验搜索网络（MFPSNET）。 MFPSNET最佳地从不同的面部先验中提取信息，并将信息融合到图像特征中，以确保保留外部指导和内部特征。通过这种方式，MFPSNet充分利用了语义级别（解析图），几何级别（面部热图），参考级别（面部词典）和像素级（降级图像）信息，从而产生忠实且逼真的图像。定量和定性实验表明，MFPSNET在合成和现实世界数据集上对最先进的BFR方法表现出色。这些代码可公开可用：https：//github.com/yyj1ang/mfpsnet。

盲面修复（BFR）旨在从相应的低质量（LQ）输入中构建高质量（HQ）面部图像。最近，已经提出了许多BFR方法，并取得了杰出的成功。但是，这些方法经过私人合成的数据集进行了培训或评估，这使得与后续方法相比的方法是不可行的。为了解决这个问题，我们首先合成两个称为EDFEACE-CELEB-1M（BFR128）和EDFACE-CELEB-150K（BFR512）的盲面恢复基准数据集。在五个设置下，将最先进的方法在它们的五个设置下进行了基准测试，包括模糊，噪声，低分辨率，JPEG压缩伪像及其组合（完全退化）。为了使比较更全面，应用了五个广泛使用的定量指标和两个任务驱动的指标，包括平均面部标志距离（AFLD）和平均面部ID余弦相似性（AFICS）。此外，我们开发了一个有效的基线模型，称为Swin Transformer U-NET（昏迷）。带有U-NET体系结构的昏迷器应用了注意机制和移动的窗口方案，以捕获远程像素相互作用，并更多地关注重要功能，同时仍受到有效训练。实验结果表明，所提出的基线方法对各种BFR任务的SOTA方法表现出色。

随着机器学习（ml）的进步及其日益增长的意识，许多拥有数据但不是ML专业知识（数据所有者）的组织希望汇集他们的数据并与那些具有专业知识的人合作，但需要来自不同来源的数据，以便训练真正普遍的资料模型（模型所有者）。在这种协作ML中，数据所有者希望保护其培训数据的隐私，而模型所有者希望模型的机密性和可能包含知识产权的培训方法。但是，现有的私人ML解决方案，如联合学习和分裂学习，不能同时满足数据和模型所有者的隐私要求。本文介绍了城可扩展的协作ML系统，可根据英特尔SGX在不受信任的基础架构中保护两个数据所有者和模型所有者的隐私。 CITADEL在代表数据所有者和代表模型所有者运行的多个训练环路中执行分布式训练。 CITADEL通过零和屏蔽和分层聚合进一步在这些外地之间建立了强大的信息屏障，以防止在协同培训期间防止数据/模型泄漏。与现有的SGX保护培训系统相比，Citadel实现了合作ML的更好的可扩展性和更强大的隐私保障。具有各种ML模型的云部署显示，Citadel缩放到大量的环路，由SGX引起的小于1.73x放缓。

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.