从大规模嘈杂的面孔中学习强大的特征表示是高性能面部识别的关键挑战之一。最近通过减轻了阶层内冲突和阶级冲突来应对这一挑战。但是，每种冲突中无约束的噪声类型仍然使这些算法难以表现良好。为了更好地理解这一点，我们将每个类别的噪声类型以更细粒度的方式重新制定为n-身份| k^c-clusters。可以通过调整\ nkc的值来生成不同类型的嘈杂面。基于这种统一的公式，我们发现噪声射击表示学习背后的主要障碍是在不同的N，K和C下算法的灵活性。对于此潜在问题，我们提出了一种新方法，称为Evolving子中心学习〜（ESL），找到最佳的超平面，以准确描述大型嘈杂面的潜在空间。更具体地说，我们将每个类的M子中心初始化，ESL鼓励它通过生产，合并和丢弃操作自动与n-身份| k^c-clusters面对面。嘈杂面上属于相同身份的图像可以有效地收敛到同一子中心，并且具有不同身份的样本将被推开。我们通过对具有不同n，k和C的合成噪声数据集进行了精心的消融研究来检查其有效性

CutMix是一种流行的增强技术，通常用于训练现代卷积和变压器视觉网络。它最初旨在鼓励卷积神经网络（CNN）更多地关注图像的全球环境，而不是本地信息，从而大大提高了CNN的性能。但是，我们发现它对自然具有全球接收领域的基于变压器的体系结构的好处有限。在本文中，我们提出了一种新型的数据增强技术图，以提高视觉变压器的性能。 TokenMix通过将混合区分为多个分离的零件，将两个图像在令牌级别混合。此外，我们表明，Cutmix中的混合学习目标是一对地面真相标签的线性组合，可能是不准确的，有时是违反直觉的。为了获得更合适的目标，我们建议根据预先训练的教师模型的两个图像的基于内容的神经激活图分配目标得分，该图像不需要具有高性能。通过大量有关各种视觉变压器体系结构的实验，我们表明我们提出的TokenMix可以帮助视觉变形金刚专注于前景区域，以推断班级并增强其稳健性，以稳定的性能增长。值得注意的是，我们使用 +1％Imagenet TOP-1精度改善DEIT-T/S/B。此外，TokenMix的训练较长，在Imainet上获得了81.2％的TOP-1精度，而DEIT-S训练了400个时代。代码可从https://github.com/sense-x/tokenmix获得。

在本文中，我们提出了一种快速的单眼深度估计方法，用于启用低成本水下机器人的3D感知能力。我们制定了一种名为udepth的新型端到端深度视觉学习管道，该管道结合了自然水下场景的图像形成特征的领域知识。首先，我们通过利用水下光线衰减来调整新的输入空间，然后在粗像素深度预测中设计最小二乘配方。随后，我们将其扩展到一个域投影损失，该损失指导超过9K RGB-D训练样本的Udepth的端到端学习。 Udepth采用计算轻型MobilenETV2骨架和基于变压器的优化器设计，以确保嵌入式系统上的快速推理速率。通过域感知的设计选择并通过全面的实验分析，我们证明了可以在确保较小的计算足迹的同时实现最新的深度估计性能。具体而言，与现有基准相比，网络参数少70％-80％，Udepth实现了可比性的，并且通常更高的深度估计性能。虽然完整的模型在单个GPU（CPU核心）上提供了超过66 fps（13 fps）的推理率，但我们对粗深度预测的域投影在单板NVIDIA JETSON TX2S上以51.5 fps的速率运行。推理管道可在https://github.com/uf-robopi/udepth上找到。

我们考虑了一个固定的销售库存控制系统，该系统在计划中$ t $上有交货时间$ l $。供应不确定，并且是订单数量（由于随机产量/容量等）的函数。我们的目标是最大程度地减少$ t $ - 周期成本，即使在已知的需求和供应分布下，该问题也已知在计算上是棘手的。在本文中，我们假设需求和供应分布均未知并开发出一种计算高效的在线学习算法。我们表明，我们的算法在$ O（l+\ sqrt {t}} $时，我们的算法（即我们的算法成本与最佳政策的成本之间的性能差异） （t）$。我们这样做1）显示我们的算法成本最多，最多$ o（l+\ sqrt {t}）$对于任何$ l \ geq 0 $，与完整信息下的最佳恒定订单策略相比以及广泛使用的算法）和2）利用其现有文献的已知绩效保证。据我们所知，有限的样本$ O（\ sqrt {t}）$（$ l $中的多项式）遗憾的是，在在线库存控制文献中以前不知道针对最佳策略的基准标记。这个学习问题的一个关键挑战是，可以审查需求和供应数据。因此，只能观察到截短的值。我们通过证明在订单数量$ q^2 $中生成的数据允许我们模拟全部$ q^2 $的性能，还可以模拟所有$ q^1 $，从而避免了这一挑战。 $，即使在数据审查下，也可以获取足够信息的关键观察。通过建立高概率耦合参数，我们能够在有限的时间范围内评估和比较其稳定状态下不同顺序策略的性能。由于该问题缺乏凸度，因此我们开发了一种活跃的消除方法，可以适应地排除次优的解决方案。

使用单视图2D照片仅集合，无监督的高质量多视图 - 一致的图像和3D形状一直是一个长期存在的挑战。现有的3D GAN是计算密集型的，也是没有3D-一致的近似;前者限制了所生成的图像的质量和分辨率，并且后者对多视图一致性和形状质量产生不利影响。在这项工作中，我们提高了3D GAN的计算效率和图像质量，而无需依赖这些近似。为此目的，我们介绍了一种表现力的混合明确隐式网络架构，与其他设计选择一起，不仅可以实时合成高分辨率多视图一致图像，而且还产生高质量的3D几何形状。通过解耦特征生成和神经渲染，我们的框架能够利用最先进的2D CNN生成器，例如Stylega2，并继承它们的效率和表现力。在其他实验中，我们展示了与FFHQ和AFHQ猫的最先进的3D感知合成。

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.

Dataset distillation has emerged as a prominent technique to improve data efficiency when training machine learning models. It encapsulates the knowledge from a large dataset into a smaller synthetic dataset. A model trained on this smaller distilled dataset can attain comparable performance to a model trained on the original training dataset. However, the existing dataset distillation techniques mainly aim at achieving the best trade-off between resource usage efficiency and model utility. The security risks stemming from them have not been explored. This study performs the first backdoor attack against the models trained on the data distilled by dataset distillation models in the image domain. Concretely, we inject triggers into the synthetic data during the distillation procedure rather than during the model training stage, where all previous attacks are performed. We propose two types of backdoor attacks, namely NAIVEATTACK and DOORPING. NAIVEATTACK simply adds triggers to the raw data at the initial distillation phase, while DOORPING iteratively updates the triggers during the entire distillation procedure. We conduct extensive evaluations on multiple datasets, architectures, and dataset distillation techniques. Empirical evaluation shows that NAIVEATTACK achieves decent attack success rate (ASR) scores in some cases, while DOORPING reaches higher ASR scores (close to 1.0) in all cases. Furthermore, we conduct a comprehensive ablation study to analyze the factors that may affect the attack performance. Finally, we evaluate multiple defense mechanisms against our backdoor attacks and show that our attacks can practically circumvent these defense mechanisms.

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.

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.

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.