Recently, attempts have been made to reduce annotation requirements in feature-based self-explanatory models for lung nodule diagnosis. As a representative, cRedAnno achieves competitive performance with considerably reduced annotation needs by introducing self-supervised contrastive learning to do unsupervised feature extraction. However, it exhibits unstable performance under scarce annotation conditions. To improve the accuracy and robustness of cRedAnno, we propose an annotation exploitation mechanism by conducting semi-supervised active learning with sparse seeding and training quenching in the learned semantically meaningful reasoning space to jointly utilise the extracted features, annotations, and unlabelled data. The proposed approach achieves comparable or even higher malignancy prediction accuracy with 10x fewer annotations, meanwhile showing better robustness and nodule attribute prediction accuracy under the condition of 1% annotations. Our complete code is open-source available: https://github.com/diku-dk/credanno.

Dynamic interaction graphs have been widely adopted to model the evolution of user-item interactions over time. There are two crucial factors when modelling user preferences for link prediction in dynamic interaction graphs: 1) collaborative relationship among users and 2) user personalized interaction patterns. Existing methods often implicitly consider these two factors together, which may lead to noisy user modelling when the two factors diverge. In addition, they usually require time-consuming parameter learning with back-propagation, which is prohibitive for real-time user preference modelling. To this end, this paper proposes FreeGEM, a parameter-free dynamic graph embedding method for link prediction. Firstly, to take advantage of the collaborative relationships, we propose an incremental graph embedding engine to obtain user/item embeddings, which is an Online-Monitor-Offline architecture consisting of an Online module to approximately embed users/items over time, a Monitor module to estimate the approximation error in real time and an Offline module to calibrate the user/item embeddings when the online approximation errors exceed a threshold. Meanwhile, we integrate attribute information into the model, which enables FreeGEM to better model users belonging to some under represented groups. Secondly, we design a personalized dynamic interaction pattern modeller, which combines dynamic time decay with attention mechanism to model user short-term interests. Experimental results on two link prediction tasks show that FreeGEM can outperform the state-of-the-art methods in accuracy while achieving over 36X improvement in efficiency. All code and datasets can be found in https://github.com/FudanCISL/FreeGEM.

过去，图像检索是用于跨视图地理位置和无人机视觉本地化任务的主流解决方案。简而言之，图像检索的方式是通过过渡角度获得最终所需的信息，例如GPS。但是，图像检索的方式并非完全端到端。并且有一些多余的操作，例如需要提前准备功能库以及画廊构造的抽样间隔问题，这使得很难实施大规模应用程序。在本文中，我们提出了一个端到端定位方案，使用图像（FPI）查找点，该方案旨在通过源A的图像（无人机 - - 看法）。为了验证我们的框架的可行性，我们构建了一个新的数据集（UL14），该数据集旨在解决无人机视觉自我定位任务。同时，我们还建立了一个基于变压器的基线以实现端到端培训。另外，先前的评估方法不再适用于FPI框架。因此，提出了米级准确性（MA）和相对距离评分（RDS）来评估无人机定位的准确性。同时，我们初步比较了FPI和图像检索方法，而FPI的结构在速度和效率方面都可以提高性能。特别是，由于不同观点与剧烈的空间量表转换之间的巨大差异，FPI的任务仍然是巨大的挑战。

对于神经视频编解码器，设计有效的熵模型至关重要但又具有挑战性，该模型可以准确预测量化潜在表示的概率分布。但是，大多数现有的视频编解码器直接使用图像编解码器的现成的熵模型来编码残差或运动，并且不会完全利用视频中的时空特性。为此，本文提出了一个强大的熵模型，该模型有效地捕获了空间和时间依赖性。特别是，我们介绍了潜在的先验，这些先验利用了潜在表示之间的相关性来挤压时间冗余。同时，提出了双重空间先验，以平行友好的方式降低空间冗余。此外，我们的熵模型也是通用的。除了估计概率分布外，我们的熵模型还在空间通道上生成量化步骤。这种内容自适应的量化机制不仅有助于我们的编解码器在单个模型中实现平滑的速率调整，而且还通过动态位分配来改善最终速率延伸性能。实验结果表明，与H.266（VTM）相比，使用最高的压缩率配置，我们的神经编解码器在提出的熵模型中，我们的神经编解码器可以在UVG数据集上节省18.2％的比特率。它在神经视频编解码器的开发中是一个新的里程碑。这些代码在https://github.com/microsoft/dcvc上。

基于特征的自我解释方法解释了他们的分类，从人类可行的特征方面。在医学成像社区中，这种临床知识的语义匹配大大增加了AI的可信度。但是，功能附加注释的成本仍然是一个紧迫的问题。我们通过提出Credanno来解决这个问题，这是一种用于肺结核诊断的数据/注释有效的自我解释方法。 Credanno通过引入自我保护的对比学习来大大减少注释需求，以减轻从注释中学习大多数参数的负担，从而通过两阶段的培训代替端到端的培训。当使用数百个结节样本和仅1％的注释训练时，Credanno在预测恶性肿瘤方面取得了竞争力的准确性，同时，在预测结节属性方面大多数以前的作品都显着超过了。学习空间的可视化进一步表明，恶性肿瘤和结节属性的聚类与临床知识一致。我们的完整代码可用：https：//github.com/ludles/credanno。

联合学习框架通常需要协作者共享共同模型的本地渐变更新，而不是共享培训数据以保留隐私。但是，在梯度泄漏攻击的事先工作表明，可以从梯度揭示私人培训数据。到目前为止，几乎所有相关工程都基于完全连接或卷积神经网络的攻击。鉴于近期适应变压器以解决多种愿景任务的绝大多大浪潮，调查视觉变压器的隐私风险是非常有价值的。在本文中，我们分析了基于自我关注机制的渐变泄漏风险，以理论和实用的方式。特别是，我们提出了4月 - 注意隐私泄漏，这对自我关注的博览会造成了强烈的威胁，如vit。展示视觉变压器如何通过梯度泄露隐私泄漏的风险，我们敦促设计隐私更安全的变压器模型和防守方案的重要性。

我们地址结束学习视频压缩，特别关注更好地学习和利用时间上下文。对于时间上下文挖掘，我们建议不仅存储先前重建的帧，还可以存储到广义解码图像缓冲器中的传播功能。从存储的传播功能中，我们建议学习多尺度的时间上下文，并将学习的时间上下文重新填充到压缩方案的模块中，包括上下文编码器 - 解码器，帧生成器和时间上下文编码器。我们的计划丢弃了并行化 - 不友好的自动回归熵模型，以追求更实用的解码时间。我们将我们的计划与X264和X265（分别代表H.264和H.265的工业软件）以及H.264，H.265和H.266（JM，HM和VTM的官方参考软件（JM，HM和VTM）进行比较， 分别）。当周期为32次并定向为PSNR时，我们的方案优于H.265 - HM以14.4％的比特率储蓄;当取向MS-SSIM时，我们的方案优于21.1％比特率保存的H.266 - VTM。

大多数现有的神经视频压缩方法采用预测编码框架，该预测编码框架首先生成预测帧，然后用当前帧编码其残差。然而，对于压缩比，预测编码只是子最优解，因为它使用简单的减法操作来消除跨越帧的冗余。在本文中，我们提出了一种深度上下文视频压缩框架，以使从预测编码转换到条件编码。特别是，我们尝试回答以下问题：如何在深度视频压缩框架下定义，使用和学习条件。要点击条件编码的可能性，我们将使用要素域上下文提出为条件。这使我们能够利用高维上下文来对编码器和解码器携带丰富的信息，这有助于重建高频内容以获得更高的视频质量。我们的框架也是可扩展的，其中条件可以灵活设计。实验表明，我们的方法可以显着优于先前的最先进（SOTA）深度视频压缩方法。与使用SifeSlow预设相比，我们可以为1080p标准测试视频达到26.0％的比特率保存。

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.

Robust prediction of citywide traffic flows at different time periods plays a crucial role in intelligent transportation systems. While previous work has made great efforts to model spatio-temporal correlations, existing methods still suffer from two key limitations: i) Most models collectively predict all regions' flows without accounting for spatial heterogeneity, i.e., different regions may have skewed traffic flow distributions. ii) These models fail to capture the temporal heterogeneity induced by time-varying traffic patterns, as they typically model temporal correlations with a shared parameterized space for all time periods. To tackle these challenges, we propose a novel Spatio-Temporal Self-Supervised Learning (ST-SSL) traffic prediction framework which enhances the traffic pattern representations to be reflective of both spatial and temporal heterogeneity, with auxiliary self-supervised learning paradigms. Specifically, our ST-SSL is built over an integrated module with temporal and spatial convolutions for encoding the information across space and time. To achieve the adaptive spatio-temporal self-supervised learning, our ST-SSL first performs the adaptive augmentation over the traffic flow graph data at both attribute- and structure-levels. On top of the augmented traffic graph, two SSL auxiliary tasks are constructed to supplement the main traffic prediction task with spatial and temporal heterogeneity-aware augmentation. Experiments on four benchmark datasets demonstrate that ST-SSL consistently outperforms various state-of-the-art baselines. Since spatio-temporal heterogeneity widely exists in practical datasets, the proposed framework may also cast light on other spatial-temporal applications. Model implementation is available at https://github.com/Echo-Ji/ST-SSL.