Recently, the self-supervised pre-training paradigm has shown great potential in leveraging large-scale unlabeled data to improve downstream task performance. However, increasing the scale of unlabeled pre-training data in real-world scenarios requires prohibitive computational costs and faces the challenge of uncurated samples. To address these issues, we build a task-specific self-supervised pre-training framework from a data selection perspective based on a simple hypothesis that pre-training on the unlabeled samples with similar distribution to the target task can bring substantial performance gains. Buttressed by the hypothesis, we propose the first yet novel framework for Scalable and Efficient visual Pre-Training (SEPT) by introducing a retrieval pipeline for data selection. SEPT first leverage a self-supervised pre-trained model to extract the features of the entire unlabeled dataset for retrieval pipeline initialization. Then, for a specific target task, SEPT retrievals the most similar samples from the unlabeled dataset based on feature similarity for each target instance for pre-training. Finally, SEPT pre-trains the target model with the selected unlabeled samples in a self-supervised manner for target data finetuning. By decoupling the scale of pre-training and available upstream data for a target task, SEPT achieves high scalability of the upstream dataset and high efficiency of pre-training, resulting in high model architecture flexibility. Results on various downstream tasks demonstrate that SEPT can achieve competitive or even better performance compared with ImageNet pre-training while reducing the size of training samples by one magnitude without resorting to any extra annotations.

与单峰数据相比，多模式数据可以提供更多功能来帮助模型分析数据的情感。先前的研究作品很少考虑令牌级的功能融合，很少有工作探索学习与多模式数据中情感相关的共同特征，以帮助模型融合多模式功能。在本文中，我们提出了一种对比度学习和多层融合（CLMLF）方法，用于多模式情感检测。具体来说，我们首先编码文本和图像以获取隐藏的表示形式，然后使用多层融合模块来对齐和融合文本和图像的令牌级特征。除了情感分析任务外，我们还设计了两个对比学习任务，基于标签的对比度学习和基于数据的对比学习任务，这将帮助该模型学习与多模式数据中情感相关的共同特征。与现有方法相比，对三个公开多模式数据集进行的广泛实验证明了我们对多模式情感检测的有效性。这些代码可在https://github.com/link-li/clmlf上使用

本文介绍了Omnicity，这是一种从多层次和多视图图像中了解无所不能的城市理解的新数据集。更确切地说，Omnicity包含多视图的卫星图像以及街道级全景图和单视图图像，构成了超过100k像素的注释图像，这些图像是从纽约市的25k Geo-Locations中良好的一致性和收集的。为了减轻大量像素的注释努力，我们提出了一个有效的街景图像注释管道，该管道利用了卫星视图的现有标签地图以及不同观点之间的转换关系（卫星，Panorama和Mono-View）。有了新的Omnicity数据集，我们为各种任务提供基准，包括构建足迹提取，高度估计以及构建平面/实例/细粒细分。我们还分析了视图对每个任务的影响，不同模型的性能，现有方法的局限性等。与现有的多层次和多视图基准相比，我们的Omnicity包含更多具有更丰富注释类型和更丰富的图像更多的视图，提供了从最先进的模型获得的更多基线结果，并为街道级全景图像中的细粒度建筑实例细分介绍了一项新颖的任务。此外，Omnicity为现有任务提供了新的问题设置，例如跨视图匹配，合成，分割，检测等，并促进开发新方法，以了解大规模的城市理解，重建和仿真。 Omnicity数据集以及基准将在https://city-super.github.io/omnicity上找到。

当前有监督的跨域图像检索方法可以实现出色的性能。但是，数据收集和标签的成本施加了在实际应用程序中实践部署的棘手障碍。在本文中，我们研究了无监督的跨域图像检索任务，其中类标签和配对注释不再是训练的先决条件。这是一项极具挑战性的任务，因为没有对内域特征表示学习和跨域对准的监督。我们通过引入：1）一种新的群体对比度学习机制来应对这两个挑战，以帮助提取班级语义感知特征，以及2）新的距离距离损失，以有效地测量并最大程度地减少域差异而无需任何外部监督。在办公室和域名数据集上进行的实验始终显示出与最先进方法相比，我们的框架的出色图像检索精度。我们的源代码可以在https://github.com/conghuihu/ucdir上找到。

最近已经提出了3D车道检测的方法，以解决许多自动驾驶场景（上坡/下坡，颠簸等）中不准确的车道布局问题。先前的工作在复杂的情况下苦苦挣扎，因为它们对前视图和鸟类视图（BEV）之间的空间转换以及缺乏现实数据集的简单设计。在这些问题上，我们介绍了Persformer：具有新型基于变压器的空间特征变换模块的端到端单眼3D车道检测器。我们的模型通过参考摄像头参数来参与相关的前视本地区域来生成BEV功能。 Persformer采用统一的2D/3D锚设计和辅助任务，以同时检测2D/3D车道，从而提高功能一致性并分享多任务学习的好处。此外，我们发布了第一个大型现实世界3D车道数据集之一：OpenLane，具有高质量的注释和场景多样性。 OpenLane包含200,000帧，超过880,000个实例级别的车道，14个车道类别，以及场景标签和封闭式对象注释，以鼓励开发车道检测和更多与工业相关的自动驾驶方法。我们表明，在新的OpenLane数据集和Apollo 3D Lane合成数据集中，Persformer在3D车道检测任务中的表现明显优于竞争基线，并且在OpenLane上的2D任务中也与最新的算法相当。该项目页面可在https://github.com/openperceptionx/persformer_3dlane上找到，OpenLane数据集可在https://github.com/openperceptionx/openlane上提供。

过去几年的技术创新的巨大浪潮，标志着AI技术的进展，是深刻的重塑行业和社会。然而，在路上，一个关键的挑战等待着我们，即我们满足快速增长的情景的能力的能力受到收购培训数据的成本的严重限制。由于主流学习范式的局限性，这一困难的局面是基于主流学习范式的局限性：我们需要根据大量注释的数据以及通常从头来训练每个新场景的新模型。在解决这一基本问题时，我们超越并开发一个名为实习生的新学习范式。通过在多个阶段的来自多个来源的监控信号学习，培训的模型将产生强大的相互性。我们在26个众所周知的数据集中评估我们的模型，该数据集涵盖计算机视觉中的四类任务。在大多数情况下，我们的模型仅适用于目标域中的培训数据的10％，始终以完整的数据培训的对应物，通常由显着的边距。这是一个重要前景的重要一步，其中具有一般视觉能力的这种模型可以大大降低对数据的依赖，从而加速通过AI技术的采用。此外，围绕我们的新范式旋转，我们还介绍了一个新的数据系统，新的架构和新的基准，以及一起形成一般愿景生态系统，以开放和包容性的方式支持其未来的发展。

在本文中，我们提出了第一次尝试无监督的SBIR来删除常规培训所需的标签成本（类别注释和素描 - 光配对）。由于该问题的独特跨域（草图和照片）性质，现有的单域无监督表示学习方法在本应用程序中的性能很差。因此，我们介绍了一个新型框架，该框架同时执行了无监督的表示学习和素描域的对准。从技术上讲，这是通过利用联合分配最佳运输（JDOT）来对齐的，以使来自不同领域的数据在表示过程中对齐，我们将其扩展到可训练的群集原型和功能记忆库以进一步提高可扩展性和功效。广泛的实验表明，我们的框架在新的无监督环境中取得了出色的性能，并且在零拍设置中的性能比最先进的表现相当或更好。

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.