语义细分是一种关键技术，涉及高分辨率遥感（HRS）图像的自动解释，并引起了遥感社区的广泛关注。由于其层次表示能力，深度卷积神经网络（DCNN）已成功应用于HRS图像语义分割任务。但是，对大量培训数据的严重依赖性以及对数据分布变化的敏感性严重限制了DCNNS在HRS图像的语义分割中的潜在应用。这项研究提出了一种新型的无监督域适应性语义分割网络（MemoryAdaptnet），用于HRS图像的语义分割。 MemoryAdaptnet构建了一种输出空间对抗学习方案，以弥合源域和目标域之间的域分布差异，并缩小域移位的影响。具体而言，我们嵌入了一个不变的特征内存模块来存储不变的域级上下文信息，因为从对抗学习获得的功能仅代表当前有限输入的变体特征。该模块由类别注意力驱动的不变域级上下文集合模块集成到当前伪不变功能，以进一步增强像素表示。基于熵的伪标签滤波策略用于更新当前目标图像的高额伪不变功能的内存模块。在三个跨域任务下进行的广泛实验表明，我们提出的记忆ADAPTNET非常优于最新方法。

基于深度学习的模型占主导地位的生产推荐系统的当前景观。此外，近年来目睹了模型规模的指数增长 - 从谷歌的2016年模型，最新的Facebook的型号有10亿个参数，具有12万亿参数。型号容量的每次跳跃都有显着的质量增强，这使我们相信100万亿参数的时代即将来临。然而，即使在工业规模数据中心内，这些模型的培训也在挑战。这种困难是从训练计算的惊人的异质性继承 - 模型的嵌入层可以包括总模型尺寸的99.99％，这是极其内存密集的;虽然其余的神经网络越来越多地计算密集型。为支持培训此类巨大模式，迫切需要有效的分布式培训系统。在本文中，我们通过仔细共同设计优化算法和分布式系统架构来解决这一挑战。具体而言，为了确保培训效率和训练精度，我们设计一种新型混合训练算法，其中嵌入层和密集的神经网络由不同的同步机制处理;然后，我们构建一个名为Persia的系统（短暂的并行推荐培训系统，其中包含混合加速），以支持这种混合培训算法。理论上的示范和实证研究均达到100万亿参数，以证明了波斯的系统设计和实施。我们将Pensia公开使用（在https://github.com/persiamml/persia），以便任何人都能够以100万亿参数的规模轻松培训推荐模型。

无数据知识蒸馏（DFKD）的目的是在没有培训数据的情况下培训从教师网络的轻量级学生网络。现有方法主要遵循生成信息样本的范式，并通过针对数据先验，边界样本或内存样本来逐步更新学生模型。但是，以前的DFKD方法很难在不同的训练阶段动态调整生成策略，这反过来又很难实现高效且稳定的训练。在本文中，我们探讨了如何从课程学习（CL）的角度来教学学生，并提出一种新方法，即“ CUDFKD”，即“使用课程的无数据知识蒸馏”。它逐渐从简单的样本到困难的样本学习，这类似于人类学习的方式。此外，我们还提供了对主要化最小化（MM）算法的理论分析，并解释了CUDFKD的收敛性。在基准数据集上进行的实验表明，使用简单的课程设计策略，CUDFKD可以在最先进的DFKD方法和不同的基准测试中实现最佳性能，例如CIFAR10上RESNET18模型的95.28 \％TOP1的精度，这是更好的而不是从头开始培训数据。训练很快，在30个时期内达到90 \％的最高精度，并且训练期间的差异稳定。同样在本文中，还分析和讨论了CUDFKD的适用性。

负载预测在电力系统的分析和网格计划中至关重要。因此，我们首先提出一种基于联邦深度学习和非侵入性负载监测（NILM）的家庭负载预测方法。就我们所知，这是基于尼尔姆的家庭负载预测中有关联合学习（FL）的首次研究。在这种方法中，通过非侵入性负载监控将集成功率分解为单个设备功率，并且使用联合深度学习模型分别预测单个设备的功率。最后，将单个设备的预测功率值聚合以形成总功率预测。具体而言，通过单独预测电气设备以获得预测的功率，它可以避免由于单个设备的功率信号的强烈依赖性而造成的误差。在联邦深度学习预测模型中，具有权力数据的家主共享本地模型的参数，而不是本地电源数据，从而保证了家庭用户数据的隐私。案例结果表明，所提出的方法比直接预测整个汇总信号的传统方法提供了更好的预测效果。此外，设计和实施了各种联合学习环境中的实验，以验证该方法的有效性。

自上而下的实例分割框架与自下而上的框架相比，它在对象检测方面表现出了优越性。虽然它有效地解决了过度细分，但自上而下的实例分割却遭受了过度处理问题。然而，完整的分割掩模对于生物图像分析至关重要，因为它具有重要的形态特性，例如形状和体积。在本文中，我们提出了一个区域建议纠正（RPR）模块，以解决这个具有挑战性的分割问题。特别是，我们提供了一个渐进式皇家模块，以逐渐将邻居信息引入一系列ROI。 ROI功能被馈入专门的进料网络（FFN）以进行提案框回归。有了其他邻居信息，提出的RPR模块显示了区域建议位置的校正显着改善，因此与最先进的基线方法相比，在三个生物图像数据集上表现出有利的实例分割性能。实验结果表明，所提出的RPR模块在基于锚固的和无锚的自上而下实例分割方法中有效，这表明该方法可以应用于生物学图像的一般自上而下实例分割。代码可用。

尽管近期长尾对象检测成功，但几乎所有长尾对象探测器都是基于两级范式开发的。在实践中，一阶段探测器在行业中更为普遍，因为它们具有简单而快速的管道，易于部署。然而，在长尾情景中，到目前为止，这项工作尚未探讨。在本文中，我们调查了在这种情况下是否可以良好的单级探测器表现良好。我们发现预防一步检测器实现优异性能的主要障碍是：在长尾数据分布下，类别遭受不同程度的正负不平衡问题。传统的焦点损失与所有类别的调制因子相同的调节因子平衡，因此未能处理长尾问题。为了解决这个问题，我们提出了根据其不平衡程度独立地重新平衡不同类别的正面和负样本的损失贡献的均等的联络损失（EFL）。具体而言，EFL采用类别相关调制因子，可以通过不同类别的培训状态来动态调整。对挑战性的LVIS V1基准进行的广泛实验表明了我们提出的方法的有效性。通过端到端培训管道，EF​​L在整体AP方面实现了29.2％，并对稀有类别进行了显着的性能改进，超越了所有现有的最先进的方法。代码可在https://github.com/modeltc/eod上获得。

Object recognition techniques using convolutional neural networks (CNN) have achieved great success. However, state-of-the-art object detection methods still perform poorly on large vocabulary and long-tailed datasets, e.g. LVIS.In this work, we analyze this problem from a novel perspective: each positive sample of one category can be seen as a negative sample for other categories, making the tail categories receive more discouraging gradients. Based on it, we propose a simple but effective loss, named equalization loss, to tackle the problem of long-tailed rare categories by simply ignoring those gradients for rare categories. The equalization loss protects the learning of rare categories from being at a disadvantage during the network parameter updating. Thus the model is capable of learning better discriminative features for objects of rare classes. Without any bells and whistles, our method achieves AP gains of 4.1% and 4.8% for the rare and common categories on the challenging LVIS benchmark, compared to the Mask R-CNN baseline. With the utilization of the effective equalization loss, we finally won the 1st place in the LVIS Challenge 2019. Code has been made available at: https: //github.com/tztztztztz/eql.detectron2

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.

To generate high quality rendering images for real time applications, it is often to trace only a few samples-per-pixel (spp) at a lower resolution and then supersample to the high resolution. Based on the observation that the rendered pixels at a low resolution are typically highly aliased, we present a novel method for neural supersampling based on ray tracing 1/4-spp samples at the high resolution. Our key insight is that the ray-traced samples at the target resolution are accurate and reliable, which makes the supersampling an interpolation problem. We present a mask-reinforced neural network to reconstruct and interpolate high-quality image sequences. First, a novel temporal accumulation network is introduced to compute the correlation between current and previous features to significantly improve their temporal stability. Then a reconstruct network based on a multi-scale U-Net with skip connections is adopted for reconstruction and generation of the desired high-resolution image. Experimental results and comparisons have shown that our proposed method can generate higher quality results of supersampling, without increasing the total number of ray-tracing samples, over current state-of-the-art methods.

Temporal sentence grounding (TSG) aims to identify the temporal boundary of a specific segment from an untrimmed video by a sentence query. All existing works first utilize a sparse sampling strategy to extract a fixed number of video frames and then conduct multi-modal interactions with query sentence for reasoning. However, we argue that these methods have overlooked two indispensable issues: 1) Boundary-bias: The annotated target segment generally refers to two specific frames as corresponding start and end timestamps. The video downsampling process may lose these two frames and take the adjacent irrelevant frames as new boundaries. 2) Reasoning-bias: Such incorrect new boundary frames also lead to the reasoning bias during frame-query interaction, reducing the generalization ability of model. To alleviate above limitations, in this paper, we propose a novel Siamese Sampling and Reasoning Network (SSRN) for TSG, which introduces a siamese sampling mechanism to generate additional contextual frames to enrich and refine the new boundaries. Specifically, a reasoning strategy is developed to learn the inter-relationship among these frames and generate soft labels on boundaries for more accurate frame-query reasoning. Such mechanism is also able to supplement the absent consecutive visual semantics to the sampled sparse frames for fine-grained activity understanding. Extensive experiments demonstrate the effectiveness of SSRN on three challenging datasets.