在过去的几十年里，互联网用户在网上举办了实时事件并与现场，互动受众分享经历的日益增长的需求。像抽搐一样的在线流媒体服务吸引了数百万用户来流并窥视。关于抽搐对流动性普及的预测有很少的研究。在本文中，我们看起来可能有助于娱乐的潜在因素。在4周时段期间，通过使用Twitch的API一致的跟踪收集娱乐数据。收集每个用户的流信息，例如当前观看者和追随者的数量，流类型等。从结果中，我们发现流媒体会话的频率，内容的类型和流的长度是确定在会话期间可以获得多少观众和订户的垃圾媒体。

我们考虑了自动生成音乐文本描述的新颖任务。与其他完善的文本生成任务（例如图像标题）相比，富裕的音乐和文本数据集的稀缺性使其成为更具挑战性的任务。在本文中，我们利用众包音乐评论来构建一个新的数据集，并提出一个序列到序列模型以生成音乐的文本描述。更具体地说，我们将扩张的卷积层用作编码器的基本组成部分，基于内存的复发性神经网络作为解码器。为了增强生成文本的真实性和主题，我们进一步建议用歧视者和新的主题评估者微调模型。为了衡量生成的文本的质量，我们还提出了两个新的评估指标，它们比人类评估比传统指标（例如BLEU）更加一致。实验结果验证了我们的模型能够在包含原始音乐的主题和内容信息的同时产生流利而有意义的评论。

视频流连续交付，以节省存储和设备内存的成本。用户设备上通常采用实时denoising算法，以消除视频流的拍摄和传输过程中所涉及的噪声。但是，基于滑动窗口的方法为单个输出和缺乏计算效率的多个输入帧提供了多个输入帧。最近的多输出推荐工作可以通过平行或经常性的框架传播双向时间功能，这要么在剪辑的时间边缘上的性能下降，要么无法在线推断。在本文中，我们提出了一个双向流视频Denoisising（BSVD）框架，以实现具有过去和将来的暂时接收领域的流式传输视频的高保真实时DENOSISing。在线推理的双向时间融合被认为不适用于Movinet。但是，我们引入了一个新型的双向缓冲块作为我们BSVD的核心模块，这使我们在管道风格的推理过程中成为可能。此外，我们的方法简洁明了，可以在非盲和盲视频降解中使用。我们将模型与各种最先进的视频denoising模型在定性和定量上在合成和真实噪声上进行了比较。我们的方法在恢复保真度和运行时优于先前的方法。我们的源代码可在https://github.com/chenyangqiqi/bsvd上公开获得。

深度学习（DL）越来越多地应用于各种域。从传统系统到DL系统的编程范式转移在工程DL系统中造成独特的挑战。性能是DL系统中的挑战之一，性能错误（PBS）可能导致严重后果，例如资源消耗过多和财务损失。虽然DL系统中的错误已被广泛调查，但DL系统中的PBS几乎没有探讨。为了弥合这一差距，我们展示了第一个综合研究，以表征症状，根本原因和引入和引入PBS在Tensorflow和Cheras中的DL系统中的阶段，共收集来自225个Sackoverflow Post的238个PBS。我们的调查结果阐明了对开发高性能DL系统的影响，以及DL系统中的PBS检测和定位PBS。我们还在DL系统中建立了56个PBS的第一个基准，并评估了解决它们的现有方法的能力。此外，我们开发了一个静态检查器DeadPerf来检测三种类型的PBS，并在130个GitHub项目中识别488个新PBS.62和18分别被开发人员确认和修复。

A recent study has shown a phenomenon called neural collapse in that the within-class means of features and the classifier weight vectors converge to the vertices of a simplex equiangular tight frame at the terminal phase of training for classification. In this paper, we explore the corresponding structures of the last-layer feature centers and classifiers in semantic segmentation. Based on our empirical and theoretical analysis, we point out that semantic segmentation naturally brings contextual correlation and imbalanced distribution among classes, which breaks the equiangular and maximally separated structure of neural collapse for both feature centers and classifiers. However, such a symmetric structure is beneficial to discrimination for the minor classes. To preserve these advantages, we introduce a regularizer on feature centers to encourage the network to learn features closer to the appealing structure in imbalanced semantic segmentation. Experimental results show that our method can bring significant improvements on both 2D and 3D semantic segmentation benchmarks. Moreover, our method ranks 1st and sets a new record (+6.8% mIoU) on the ScanNet200 test leaderboard. Code will be available at https://github.com/dvlab-research/Imbalanced-Learning.

Weakly-supervised object localization aims to indicate the category as well as the scope of an object in an image given only the image-level labels. Most of the existing works are based on Class Activation Mapping (CAM) and endeavor to enlarge the discriminative area inside the activation map to perceive the whole object, yet ignore the co-occurrence confounder of the object and context (e.g., fish and water), which makes the model inspection hard to distinguish object boundaries. Besides, the use of CAM also brings a dilemma problem that the classification and localization always suffer from a performance gap and can not reach their highest accuracy simultaneously. In this paper, we propose a casual knowledge distillation method, dubbed KD-CI-CAM, to address these two under-explored issues in one go. More specifically, we tackle the co-occurrence context confounder problem via causal intervention (CI), which explores the causalities among image features, contexts, and categories to eliminate the biased object-context entanglement in the class activation maps. Based on the de-biased object feature, we additionally propose a multi-teacher causal distillation framework to balance the absorption of classification knowledge and localization knowledge during model training. Extensive experiments on several benchmarks demonstrate the effectiveness of KD-CI-CAM in learning clear object boundaries from confounding contexts and addressing the dilemma problem between classification and localization performance.

Witnessing the impressive achievements of pre-training techniques on large-scale data in the field of computer vision and natural language processing, we wonder whether this idea could be adapted in a grab-and-go spirit, and mitigate the sample inefficiency problem for visuomotor driving. Given the highly dynamic and variant nature of the input, the visuomotor driving task inherently lacks view and translation invariance, and the visual input contains massive irrelevant information for decision making, resulting in predominant pre-training approaches from general vision less suitable for the autonomous driving task. To this end, we propose PPGeo (Policy Pre-training via Geometric modeling), an intuitive and straightforward fully self-supervised framework curated for the policy pretraining in visuomotor driving. We aim at learning policy representations as a powerful abstraction by modeling 3D geometric scenes on large-scale unlabeled and uncalibrated YouTube driving videos. The proposed PPGeo is performed in two stages to support effective self-supervised training. In the first stage, the geometric modeling framework generates pose and depth predictions simultaneously, with two consecutive frames as input. In the second stage, the visual encoder learns driving policy representation by predicting the future ego-motion and optimizing with the photometric error based on current visual observation only. As such, the pre-trained visual encoder is equipped with rich driving policy related representations and thereby competent for multiple visuomotor driving tasks. Extensive experiments covering a wide span of challenging scenarios have demonstrated the superiority of our proposed approach, where improvements range from 2% to even over 100% with very limited data. Code and models will be available at https://github.com/OpenDriveLab/PPGeo.

In this work, we focus on instance-level open vocabulary segmentation, intending to expand a segmenter for instance-wise novel categories without mask annotations. We investigate a simple yet effective framework with the help of image captions, focusing on exploiting thousands of object nouns in captions to discover instances of novel classes. Rather than adopting pretrained caption models or using massive caption datasets with complex pipelines, we propose an end-to-end solution from two aspects: caption grounding and caption generation. In particular, we devise a joint Caption Grounding and Generation (CGG) framework based on a Mask Transformer baseline. The framework has a novel grounding loss that performs explicit and implicit multi-modal feature alignments. We further design a lightweight caption generation head to allow for additional caption supervision. We find that grounding and generation complement each other, significantly enhancing the segmentation performance for novel categories. We conduct extensive experiments on the COCO dataset with two settings: Open Vocabulary Instance Segmentation (OVIS) and Open Set Panoptic Segmentation (OSPS). The results demonstrate the superiority of our CGG framework over previous OVIS methods, achieving a large improvement of 6.8% mAP on novel classes without extra caption data. Our method also achieves over 15% PQ improvements for novel classes on the OSPS benchmark under various settings.

Nearest-Neighbor (NN) classification has been proven as a simple and effective approach for few-shot learning. The query data can be classified efficiently by finding the nearest support class based on features extracted by pretrained deep models. However, NN-based methods are sensitive to the data distribution and may produce false prediction if the samples in the support set happen to lie around the distribution boundary of different classes. To solve this issue, we present P3DC-Shot, an improved nearest-neighbor based few-shot classification method empowered by prior-driven data calibration. Inspired by the distribution calibration technique which utilizes the distribution or statistics of the base classes to calibrate the data for few-shot tasks, we propose a novel discrete data calibration operation which is more suitable for NN-based few-shot classification. Specifically, we treat the prototypes representing each base class as priors and calibrate each support data based on its similarity to different base prototypes. Then, we perform NN classification using these discretely calibrated support data. Results from extensive experiments on various datasets show our efficient non-learning based method can outperform or at least comparable to SOTA methods which need additional learning steps.

In this tutorial paper, we look into the evolution and prospect of network architecture and propose a novel conceptual architecture for the 6th generation (6G) networks. The proposed architecture has two key elements, i.e., holistic network virtualization and pervasive artificial intelligence (AI). The holistic network virtualization consists of network slicing and digital twin, from the aspects of service provision and service demand, respectively, to incorporate service-centric and user-centric networking. The pervasive network intelligence integrates AI into future networks from the perspectives of networking for AI and AI for networking, respectively. Building on holistic network virtualization and pervasive network intelligence, the proposed architecture can facilitate three types of interplay, i.e., the interplay between digital twin and network slicing paradigms, between model-driven and data-driven methods for network management, and between virtualization and AI, to maximize the flexibility, scalability, adaptivity, and intelligence for 6G networks. We also identify challenges and open issues related to the proposed architecture. By providing our vision, we aim to inspire further discussions and developments on the potential architecture of 6G.