相位检索（PR）是从其仅限强度测量中恢复复杂值信号的长期挑战，由于其在数字成像中的广泛应用，引起了很大的关注。最近，开发了基于深度学习的方法，这些方法在单发PR中取得了成功。这些方法需要单个傅立叶强度测量，而无需对测量数据施加任何其他约束。然而，由于PR问题的输入和输出域之间存在很大的差异，香草深神经网络（DNN）并没有提供良好的性能。物理信息的方法试图将傅立叶强度测量结果纳入提高重建精度的迭代方法。但是，它需要一个冗长的计算过程，并且仍然无法保证准确性。此外，其中许多方法都在模拟数据上工作，这些数据忽略了一些常见问题，例如实用光学PR系统中的饱和度和量化错误。在本文中，提出了一种新型的物理驱动的多尺度DNN结构，称为PPRNET。与其他基于深度学习的PR方法类似，PPRNET仅需要一个傅立叶强度测量。物理驱动的是，网络被指导遵循不同尺度的傅立叶强度测量，以提高重建精度。 PPRNET具有前馈结构，可以端到端训练。因此，它比传统物理驱动的PR方法更快，更准确。进行了实用光学平台上的大量模拟和实验。结果证明了拟议的PPRNET比传统的基于基于学习的PR方法的优势和实用性。

在许多图像处理任务中，深度学习方法的成功，最近还将深度学习方法引入了阶段检索问题。这些方法与传统的迭代优化方法不同，因为它们通常只需要一个强度测量，并且可以实时重建相位图像。但是，由于巨大的领域差异，这些方法给出的重建图像的质量仍然有很大的改进空间来满足一般应用要求。在本文中，我们设计了一种新型的深神经网络结构，名为Sisprnet，以基于单个傅立叶强度测量值进行相检索。为了有效利用测量的光谱信息，我们建议使用多层感知器（MLP）作为前端提出一个新的特征提取单元。它允许将输入强度图像的所有像素一起考虑，以探索其全局表示。 MLP的大小经过精心设计，以促进代表性特征的提取，同时减少噪音和异常值。辍学层还可以减轻训练MLP的过度拟合问题。为了促进重建图像中的全局相关性，将自我注意力的机制引入了提议的Sisprnet的上采样和重建（UR）块。这些UR块被插入残留的学习结构中，以防止由于其复杂的层结构而导致的较弱的信息流和消失的梯度问题。使用线性相关幅度和相位的仅相位图像和图像的不同测试数据集对所提出的模型进行了广泛的评估。在光学实验平台上进行了实验，以了解在实用环境中工作时不同深度学习方法的性能。

由于基于图像的“部件控制器”，因此人造形状图像的形状操纵，例如调整椅子的靠背或更换杯柄的大小，因为缺少基于图像的部分控制器，这是不直观的。为了解决这个问题，我们呈现风格栏，这是一种框架，它通过利用图像和3D形状的生成模型来实现图像的直接形状操纵。我们的主要贡献是一种形状一致的潜在映射函数，可以连接图像生成潜像和3D人造形状潜伏空间。我们的方法将图像内容“向前映射到其对应的3D形状属性，其中可以容易地操纵形状部分。然后，被操纵的3D形状的属性代码被“向后映射”到图像潜在代码以获得最终操纵图像。我们通过各种操纵任务展示了我们的方法，包括部分替代，零件调整大小和观点操纵，并通过广泛的消融研究评估其有效性。

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.

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.