视觉（RE）本地化解决了估计已知场景中捕获的查询图像的6-DOF（自由度）摄像头的问题，该镜头是许多计算机视觉和机器人应用程序的关键构建块。基于结构的本地化的最新进展通过记住从图像像素到场景坐标的映射与神经网络的映射来构建相机姿势优化的2D-3D对应关系。但是，这种记忆需要在每个场景中训练大量的图像，这是沉重效率降低的。相反，通常很少的图像足以覆盖场景的主要区域，以便人类操作员执行视觉定位。在本文中，我们提出了一种场景区域分类方法，以实现几乎没有拍摄图像的快速有效的场景记忆。我们的见解是利用a）预测的特征提取器，b）场景区域分类器和c）元学习策略，以加速培训，同时缓解过度拟合。我们在室内和室外基准上评估了我们的方法。该实验验证了我们方法在几次设置中的有效性，并且训练时间大大减少到只有几分钟。代码可用：\ url {https://github.com/siyandong/src}

从欧几里德绿色函数中重建频谱函数是物理学中的重要逆问题。特定物理系统的先验知识通常提供了用于求解不良问题的基本正则化方案。针对这一点，我们提出了一种自动差异框架作为从可观察数据重建的通用工具。我们代表神经网络的光谱，并将Chi-Square设置为损耗功能，以优化反向自动分化的参数。在培训过程中，除了正定的形式之外，没有明确的物理预先嵌入神经网络。通过Kullback-Leibler（KL）发散和均方误差（MSE）进行评估重建精度，在多个噪声水平。应当注意，自动差分框架和引入正则化的自由是本方法的固有优势，可能导致在未来解决逆问题的改进。

从欧几里德绿色的功能重建光谱函数是许多身体物理中的重要逆问题。然而，在具有嘈杂的绿色功能的现实系统中证明了反演。在这封信中，我们提出了一种自动分化（AD）框架作为来自传播者可观察到的光谱重建的通用工具。利用神经网络的正则化作为光谱功能的非局部平滑度调节器，我们代表神经网络的光谱功能，并使用传播者的重建误差来优化无限制的网络参数。在培训过程中，除了光谱函数的正面明确形式外，没有嵌入到神经网络中的其他显式物理前沿。通过相对熵和均方误差来评估重建性能，对于两个不同的网络表示。与最大熵方法相比，广告框架在大噪声情况下实现了更好的性能。注意，引入非局部正则化的自由是本框架的固有优势，并且可能导致求解逆问题的显着改进。

最近，行动识别因其在智能监视和人为计算机互动方面的全面和实用应用而受到了越来越多的关注。但是，由于数据稀缺性，很少有射击动作识别并未得到充分的探索，并且仍然具有挑战性。在本文中，我们提出了一种新型的分层组成表示（HCR）学习方法，以进行几次识别。具体而言，我们通过精心设计的层次聚类将复杂的动作分为几个子行动，并将子动作进一步分解为更细粒度的空间注意力亚actions（SAS-Actions）。尽管基类和新颖类之间存在很大的差异，但它们可以在子行动或SAS行为中共享相似的模式。此外，我们在运输问题中采用了地球移动器的距离，以测量视频样本之间的相似性在亚行动表示方面。它计算为距离度量的子行动之间的最佳匹配流，这有利于比较细粒模式。广泛的实验表明，我们的方法在HMDB51，UCF101和动力学数据集上实现了最新结果。

旋转不变的面部检测，即用任意旋转平面（RIP）角度的检测面，广泛需要在无约束的应用中被广泛地需要，但由于面部出现的较大变化，仍然仍然是一个具有挑战性的任务。大多数现有方法符合速度或准确性以处理大的撕裂变体。为了更有效地解决这个问题，我们提出了逐步校准网络（PCN）以粗略的方式执行旋转不变的面部检测。 PCN由三个阶段组成，每个阶段不仅将面与非面孔区分开，而且还校准了每个面部候选者的RIP方向逐渐直立。通过将校准过程划分为几个渐进步骤，并且仅预测早期阶段中的粗定向，PCN可以实现精确且快速校准。通过对脸部与逐渐减小的RIP范围进行二进制分类，PCN可以准确地检测满360 ^ {\ rIC} $ RIP角度的面部。这种设计导致实时旋转不变面检测器。在野外的多面向FDDB的实验和疯狂旋转面的较宽面的具有挑战性的子集表明我们的PCN实现了非常有希望的性能。

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.