Visual place recognition (VPR) is usually considered as a specific image retrieval problem. Limited by existing training frameworks, most deep learning-based works cannot extract sufficiently stable global features from RGB images and rely on a time-consuming re-ranking step to exploit spatial structural information for better performance. In this paper, we propose StructVPR, a novel training architecture for VPR, to enhance structural knowledge in RGB global features and thus improve feature stability in a constantly changing environment. Specifically, StructVPR uses segmentation images as a more definitive source of structural knowledge input into a CNN network and applies knowledge distillation to avoid online segmentation and inference of seg-branch in testing. Considering that not all samples contain high-quality and helpful knowledge, and some even hurt the performance of distillation, we partition samples and weigh each sample's distillation loss to enhance the expected knowledge precisely. Finally, StructVPR achieves impressive performance on several benchmarks using only global retrieval and even outperforms many two-stage approaches by a large margin. After adding additional re-ranking, ours achieves state-of-the-art performance while maintaining a low computational cost.

时间动作本地化的主要挑战是在未修剪的视频中从各种共同出现的成分（例如上下文和背景）中获取细微的人类行为。尽管先前的方法通过设计高级动作探测器取得了重大进展，但它们仍然遭受这些共发生的成分，这些成分通常占据视频中实际动作内容。在本文中，我们探讨了视频片段的两个正交但互补的方面，即动作功能和共存功能。尤其是，我们通过在视频片段中解开这两种功能并重新组合它们来生成具有更明显的动作信息以进行准确的动作本地化的新功能表示形式，从而开发了一项新颖的辅助任务。我们称我们的方法重新处理，该方法首先显式将动作内容分解并正规化其共发生的特征，然后合成新的动作主导的视频表示形式。对Thumos14和ActivityNet V1.3的广泛实验结果和消融研究表明，我们的新表示形式与简单的动作检测器相结合可以显着改善动作定位性能。

视觉地位识别是自主驾驶导航和移动机器人定位等应用的具有挑战性的任务。分散注意力在复杂的场景中呈现的元素经常导致视觉场所的感知偏差。为了解决这个问题，必须将信息与任务相关区域中的信息集成到图像表示中至关重要。在本文中，我们介绍了一种基于视觉变压器的新型整体地点识别模型，TransVPR。它受益于变形金刚的自我关注操作的理想性能，这可以自然地聚合任务相关的特征。从多个级别的变压器的关注，重点关注不同的感兴趣区域，以产生全球图像表示。另外，由熔融注意掩模过滤的变压器层的输出令牌被认为是密钥贴片描述符，用于执行空间匹配以重新排名通过全局图像特征检索的候选。整个模型允许具有单个目标和图像级监控的端到端培训。 TransVPR在几个现实世界基准上实现最先进的性能，同时保持低计算时间和存储要求。

通过纳入缺失区域的先验知识，通常用于图像染色的辅助损失导致更好的重建性能。但是，它通常需要充分利用辅助损失的潜力需要很多努力，因为加权辅助损失不当会分散模型从染色任务中的注意力，并且辅助损失的有效性可能在培训过程中变化。此外，辅助损失的设计需要域专业知识。在这项工作中，我们介绍了辅助损耗适应（Adaption）算法动态调整辅助丢失的参数，以更好地帮助主要任务。我们的算法基于更好的辅助损耗的原理是通过梯度下降的几个步骤提高主要损失性能的原理。然后，我们检查了两个常用的辅助损失，以适应\ ac {ala}来调整它们的参数。实验结果表明，ALA诱导比固定辅助损失更具竞争力的耐受效果。特别是，只需用\ ac {ALA}结合辅助损耗，现有的染色方法可以在未经明确地结合精密网络设计或结构知识的情况下实现增加的性能。

了解代理之间的复杂社交互动是轨迹预测的关键挑战。大多数现有方法考虑成对交通代理或在局域之间的相互作用，而相互作用的性质是无限的，涉及同时不确定的代理和非局部区域。此外，它们对不同类别的代理商来说，它们同样对待异质的交通代理，同时忽视人们在IFFerent类别的交通代理中的多种反应模式。为了解决这些问题，我们提出了一个简单但有效的无限邻域交互网络（UNIN），其预测多个类别中异构代理的轨迹。具体地，所提出的无限邻域交互模块同时产生相互作用涉及的所有代理的融合特征，其适用于任何数量的代理和任何范围的交互区域。同时，提出了一个分层图注意模块，以获取类别到类别的交互和代理到代理交互。最后，估计高斯混合模型的参数用于产生未来轨迹。基准数据集的广泛实验结果表明，通过最先进的方法对我们的方法进行了显着改进。

Current deep neural networks (DNNs) can easily overfit to biased training data with corrupted labels or class imbalance. Sample re-weighting strategy is commonly used to alleviate this issue by designing a weighting function mapping from training loss to sample weight, and then iterating between weight recalculating and classifier updating. Current approaches, however, need manually pre-specify the weighting function as well as its additional hyper-parameters. It makes them fairly hard to be generally applied in practice due to the significant variation of proper weighting schemes relying on the investigated problem and training data. To address this issue, we propose a method capable of adaptively learning an explicit weighting function directly from data. The weighting function is an MLP with one hidden layer, constituting a universal approximator to almost any continuous functions, making the method able to fit a wide range of weighting functions including those assumed in conventional research. Guided by a small amount of unbiased meta-data, the parameters of the weighting function can be finely updated simultaneously with the learning process of the classifiers. Synthetic and real experiments substantiate the capability of our method for achieving proper weighting functions in class imbalance and noisy label cases, fully complying with the common settings in traditional methods, and more complicated scenarios beyond conventional cases. This naturally leads to its better accuracy than other state-of-the-art methods. Source code is available at https://github.com/xjtushujun/meta-weight-net. * Corresponding author. 1 We call the training data biased when they are generated from a joint sample-label distribution deviating from the distribution of evaluation/test set [1].