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.

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps.

Dataset Distillation (DD), a newly emerging field, aims at generating much smaller and high-quality synthetic datasets from large ones. Existing DD methods based on gradient matching achieve leading performance; however, they are extremely computationally intensive as they require continuously optimizing a dataset among thousands of randomly initialized models. In this paper, we assume that training the synthetic data with diverse models leads to better generalization performance. Thus we propose two \textbf{model augmentation} techniques, ~\ie using \textbf{early-stage models} and \textbf{weight perturbation} to learn an informative synthetic set with significantly reduced training cost. Extensive experiments demonstrate that our method achieves up to 20$\times$ speedup and comparable performance on par with state-of-the-art baseline methods.

Recent advances on text-to-image generation have witnessed the rise of diffusion models which act as powerful generative models. Nevertheless, it is not trivial to exploit such latent variable models to capture the dependency among discrete words and meanwhile pursue complex visual-language alignment in image captioning. In this paper, we break the deeply rooted conventions in learning Transformer-based encoder-decoder, and propose a new diffusion model based paradigm tailored for image captioning, namely Semantic-Conditional Diffusion Networks (SCD-Net). Technically, for each input image, we first search the semantically relevant sentences via cross-modal retrieval model to convey the comprehensive semantic information. The rich semantics are further regarded as semantic prior to trigger the learning of Diffusion Transformer, which produces the output sentence in a diffusion process. In SCD-Net, multiple Diffusion Transformer structures are stacked to progressively strengthen the output sentence with better visional-language alignment and linguistical coherence in a cascaded manner. Furthermore, to stabilize the diffusion process, a new self-critical sequence training strategy is designed to guide the learning of SCD-Net with the knowledge of a standard autoregressive Transformer model. Extensive experiments on COCO dataset demonstrate the promising potential of using diffusion models in the challenging image captioning task. Source code is available at \url{https://github.com/YehLi/xmodaler/tree/master/configs/image_caption/scdnet}.

Knowledge base completion (KBC) aims to predict the missing links in knowledge graphs. Previous KBC tasks and approaches mainly focus on the setting where all test entities and relations have appeared in the training set. However, there has been limited research on the zero-shot KBC settings, where we need to deal with unseen entities and relations that emerge in a constantly growing knowledge base. In this work, we systematically examine different possible scenarios of zero-shot KBC and develop a comprehensive benchmark, ZeroKBC, that covers these scenarios with diverse types of knowledge sources. Our systematic analysis reveals several missing yet important zero-shot KBC settings. Experimental results show that canonical and state-of-the-art KBC systems cannot achieve satisfactory performance on this challenging benchmark. By analyzing the strength and weaknesses of these systems on solving ZeroKBC, we further present several important observations and promising future directions.

In recent years, the field of intelligent transportation systems (ITS) has achieved remarkable success, which is mainly due to the large amount of available annotation data. However, obtaining these annotated data has to afford expensive costs in reality. Therefore, a more realistic strategy is to leverage semi-supervised learning (SSL) with a small amount of labeled data and a large amount of unlabeled data. Typically, semantic consistency regularization and the two-stage learning methods of decoupling feature extraction and classification have been proven effective. Nevertheless, representation learning only limited to semantic consistency regularization may not guarantee the separation or discriminability of representations of samples with different semantics; due to the inherent limitations of the two-stage learning methods, the extracted features may not match the specific downstream tasks. In order to deal with the above drawbacks, this paper proposes an end-to-end deep semi-supervised learning double contrast of semantic and feature, which extracts effective tasks specific discriminative features by contrasting the semantics/features of positive and negative augmented samples pairs. Moreover, we leverage information theory to explain the rationality of double contrast of semantics and features and slack mutual information to contrastive loss in a simpler way. Finally, the effectiveness of our method is verified in benchmark datasets.

Comparing Bayesian neural networks (BNNs) with different widths is challenging because, as the width increases, multiple model properties change simultaneously, and, inference in the finite-width case is intractable. In this work, we empirically compare finite- and infinite-width BNNs, and provide quantitative and qualitative explanations for their performance difference. We find that when the model is mis-specified, increasing width can hurt BNN performance. In these cases, we provide evidence that finite-width BNNs generalize better partially due to the properties of their frequency spectrum that allows them to adapt under model mismatch.

异常检测任务在AI安全中起着至关重要的作用。处理这项任务存在巨大的挑战。观察结果表明，深度神经网络分类器通常倾向于以高信心将分布（OOD）输入分为分配类别。现有的工作试图通过在培训期间向分类器暴露于分类器时明确对分类器施加不确定性来解决问题。在本文中，我们提出了一种替代概率范式，该范式实际上对OOD检测任务既有用，又可行。特别是，我们在培训过程中施加了近距离和离群数据之间的统计独立性，以确保inlier数据在培训期间向深度估计器显示有关OOD数据的信息很少。具体而言，我们通过Hilbert-Schmidt独立标准（HSIC）估算了Inlier和离群数据之间的统计依赖性，并在培训期间对此类度量进行了惩罚。我们还将方法与推理期间的新型统计测试相关联，加上我们的原则动机。经验结果表明，我们的方法对各种基准测试的OOD检测是有效且可靠的。与SOTA模型相比，我们的方法在FPR95，AUROC和AUPR指标方面取得了重大改进。代码可用：\ url {https://github.com/jylins/hone}。

基于多模式方面的情感分类（MABSC）是一项新兴的分类任务，旨在将给定目标的情感分类，例如具有不同模式的数据中提到的实体。在带有文本和图像的典型多模式数据中，以前的方法不能充分利用图像的细颗粒语义，尤其是与文本的语义结合在一起，并且不完全考虑对细粒图像之间的关系进行建模信息和目标，这导致图像的使用不足和不足以识别细粒度的方面和意见。为了应对这些局限性，我们提出了一个新的框架SEQCSG，包括一种构建顺序跨模式语义图和编码器模型的方法。具体而言，我们从原始图像，图像标题和场景图中提取细粒度的信息，并将它们视为跨模式语义图的元素以及文本的令牌。跨模式语义图表示为具有多模式可见矩阵的序列，指示元素之间的关系。为了有效地利用跨模式语义图，我们建议使用目标提示模板的编码器解码器方法。实验结果表明，我们的方法优于现有方法，并在两个标准数据集MABSC上实现了最新方法。进一步的分析证明了每个组件的有效性，我们的模型可以隐含地学习图像的目标和细粒度信息之间的相关性。

手语制作（SLP）旨在将口语语言自动转化为符号序列。 SLP的核心过程是将符号光泽序列转换为其相应的标志姿势序列（G2P）。大多数现有的G2P模型通常以自回归方式执行这种条件的远程生成，这不可避免地导致错误的积累。为了解决这个问题，我们提出了一种量化量子序列序列的生成的矢量量化扩散方法，称为poseVQ扩散，这是一种迭代性非自动入学方法。具体而言，我们首先引入量化量化变量自动编码器（姿势VQVAE）模型，以表示姿势序列作为一系列潜在代码。然后，我们通过最近开发的扩散体系结构的扩展来对潜在离散空间进行建模。为了更好地利用时空信息，我们介绍了一种新颖的体系结构，即CodeUnet，以在离散空间中生成更高质量的姿势序列。此外，利用学习的代码，我们开发了一种新型的顺序k-nearest-neighbours方法，以预测相应的光泽序列的姿势序列的可变长度。因此，与自回旋G2P模型相比，我们的模型具有更快的采样速度，并产生明显更好的结果。与以前的非自动入学G2P方法相比，PoseVQ扩散通过迭代改进改善了预测的结果，从而在SLP评估基准上获得了最新的结果。