尽管基于经常性的神经网络（RNN）的视频预测方法已经取得了重大成就，但由于信息损失问题和基于知觉的卑鄙平方错误（MSE）损失功能，它们在具有高分辨率的数据集中的性能仍然远远不令人满意。 。在本文中，我们提出了一个时空信息保存和感知声明模型（STIP），以解决上述两个问题。为了解决信息损失问题，提出的模型旨在在功能提取和状态过渡期间分别保留视频的时空信息。首先，基于X-NET结构设计了多透明时空自动编码器（MGST-AE）。拟议的MGST-AE可以帮助解码器回忆到时间和空间域中编码器的多透明信息。这样，在高分辨率视频的功能提取过程中，可以保留更多时空信息。其次，时空门控复发单元（STGRU）是基于标准的封闭式复发单元（GRU）结构而设计的，该结构可以在状态过渡期间有效地保留时空信息。与流行的长期短期（LSTM）的预测记忆相比，提出的STGRU可以通过计算负载较低的计算负载来实现更令人满意的性能。此外，为了改善传统的MSE损失功能，基于生成的对抗网络（GAN）进一步设计了学识渊博的知觉损失（LP-loss），这可以帮助获得客观质量和感知质量之间的令人满意的权衡。实验结果表明，与各种最先进的方法相比，提出的Stip可以预测具有更令人满意的视觉质量的视频。源代码已在\ url {https://github.com/zhengchang467/stiphr}上获得。

作为一个严重的问题，近年来已经广泛研究了单图超分辨率（SISR）。 SISR的主要任务是恢复由退化程序引起的信息损失。根据Nyquist抽样理论，降解会导致混叠效应，并使低分辨率（LR）图像的正确纹理很难恢复。实际上，自然图像中相邻斑块之间存在相关性和自相似性。本文考虑了自相似性，并提出了一个分层图像超分辨率网络（HSRNET）来抑制混叠的影响。我们从优化的角度考虑SISR问题，并根据半季节分裂（HQS）方法提出了迭代解决方案模式。为了先验探索本地图像的质地，我们设计了一个分层探索块（HEB）并进行性增加了接受场。此外，设计多级空间注意力（MSA）是为了获得相邻特征的关系并增强了高频信息，这是视觉体验的关键作用。实验结果表明，与其他作品相比，HSRNET实现了更好的定量和视觉性能，并更有效地释放了别名。

本文介绍了一个新型的预训练的空间时间多对一（p-STMO）模型，用于2D到3D人类姿势估计任务。为了减少捕获空间和时间信息的困难，我们将此任务分为两个阶段：预训练（I期）和微调（II阶段）。在第一阶段，提出了一个自我监督的预训练子任务，称为蒙面姿势建模。输入序列中的人关节在空间和时间域中随机掩盖。利用denoising自动编码器的一般形式以恢复原始的2D姿势，并且编码器能够以这种方式捕获空间和时间依赖性。在第二阶段，将预训练的编码器加载到STMO模型并进行微调。编码器之后是一个多对一的框架聚合器，以预测当前帧中的3D姿势。尤其是，MLP块被用作STMO中的空间特征提取器，其性能比其他方法更好。此外，提出了一种时间下采样策略，以减少数据冗余。在两个基准上进行的广泛实验表明，我们的方法优于较少参数和较少计算开销的最先进方法。例如，我们的P-STMO模型在使用CPN作为输入的2D姿势时，在Human3.6M数据集上达到42.1mm MPJPE。同时，它为最新方法带来了1.5-7.1倍的速度。代码可在https://github.com/patrick-swk/p-stmo上找到。

将低分辨率（LR）图像恢复到超分辨率（SR）图像具有正确和清晰的细节是挑战。现有的深度学习工作几乎忽略了图像的固有结构信息，这是对SR结果的视觉感知的重要作用。在本文中，我们将分层特征开发网络设计为探测并以多尺度特征融合方式保持结构信息。首先，我们提出了在传统边缘探测器上的交叉卷积，以定位和代表边缘特征。然后，交叉卷积块（CCBS）设计有功能归一化和渠道注意，以考虑特征的固有相关性。最后，我们利用多尺度特征融合组（MFFG）来嵌入交叉卷积块，并在层次的层次上开发不同尺度的结构特征的关系，调用名为Cross-SRN的轻量级结构保护网络。实验结果表明，交叉SRN通过准确且清晰的结构细节实现了对最先进的方法的竞争或卓越的恢复性能。此外，我们设置了一个标准，以选择具有丰富的结构纹理的图像。所提出的跨SRN优于所选择的基准测试的最先进的方法，这表明我们的网络在保存边缘具有显着的优势。

端到端优化的神经图像压缩（NIC）最近获得了优异的损耗压缩性能。在本文中，我们考虑了NIC率的速率变形（R-D）特征分析和建模问题。我们努力制定使用深网络描述NIC的R-D行为的基本数学函数。因此，通过通过单个培训的网络利用这种模型可以典范地实现任意比特率点。我们提出了一个插件模块，以了解自动编码器的潜变量的目标比特率和二进制表示之间的关系。该方案解决了培训明显模型的问题，以达到R-D空间中不同的点。此外，我们分别模拟NIC的速率和失真特性分别为编码参数$ \ lambda $的函数。我们的实验表明，我们的提出方法易于采用，实现了最先进的连续比特率编码性能，这意味着我们的方法将有利于NIC的实际部署。

单像超分辨率（SISR），作为传统的不良反对问题，通过最近的卷积神经网络（CNN）的发展得到了极大的振兴。这些基于CNN的方法通常将低分辨率图像映射到其相应的高分辨率版本，具有复杂的网络结构和损耗功能，显示出令人印象深刻的性能。本文对传统的SISR算法提供了新的洞察力，并提出了一种基本上不同的方法，依赖于迭代优化。提出了一种新颖的迭代超分辨率网络（ISRN），顶部是迭代优化。我们首先分析图像SR问题的观察模型，通过以更一般和有效的方式模仿和融合每次迭代来激发可行的解决方案。考虑到批量归一化的缺点，我们提出了一种特征归一化（F-NOM，FN）方法来调节网络中的功能。此外，开发了一种具有FN的新颖块以改善作为FNB称为FNB的网络表示。剩余剩余结构被提出形成一个非常深的网络，其中FNBS与长时间跳过连接，以获得更好的信息传递和稳定训练阶段。对BICUBIC（BI）降解的测试基准的广泛实验结果表明我们的ISRN不仅可以恢复更多的结构信息，而且还可以获得竞争或更好的PSNR / SSIM结果，与其他作品相比，参数更少。除BI之外，我们除了模拟模糊（BD）和低级噪声（DN）的实际降级。 ISRN及其延伸ISRN +两者都比使用BD和DN降级模型的其他产品更好。

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.