Video-language pre-training has advanced the performance of various downstream video-language tasks. However, most previous methods directly inherit or adapt typical image-language pre-training paradigms to video-language pre-training, thus not fully exploiting the unique characteristic of video, i.e., temporal. In this paper, we propose a Hierarchical Temporal-Aware video-language pre-training framework, HiTeA, with two novel pre-training tasks for modeling cross-modal alignment between moments and texts as well as the temporal relations of video-text pairs. Specifically, we propose a cross-modal moment exploration task to explore moments in videos, which results in detailed video moment representation. Besides, the inherent temporal relations are captured by aligning video-text pairs as a whole in different time resolutions with multi-modal temporal relation exploration task. Furthermore, we introduce the shuffling test to evaluate the temporal reliance of datasets and video-language pre-training models. We achieve state-of-the-art results on 15 well-established video-language understanding and generation tasks, especially on temporal-oriented datasets (e.g., SSv2-Template and SSv2-Label) with 8.6% and 11.1% improvement respectively. HiTeA also demonstrates strong generalization ability when directly transferred to downstream tasks in a zero-shot manner. Models and demo will be available on ModelScope.

数字医学图像的机器学习和流行的最新进展已经开辟了通过使用深卷积神经网络来解决挑战性脑肿瘤细分（BTS）任务的机会。然而，与非常广泛的RGB图像数据不同，在脑肿瘤分割中使用的医学图像数据在数据刻度方面相对稀缺，但在模态属性方面包含更丰富的信息。为此，本文提出了一种新的跨模型深度学习框架，用于从多种方式MRI数据分段脑肿瘤。核心思想是通过多模态数据挖掘丰富的模式以弥补数据量表不足。所提出的跨型号深度学习框架包括两个学习过程：跨模型特征转换（CMFT）过程和跨模型特征融合（CMFF）过程，其目的是通过跨越不同模态的知识来学习丰富的特征表示数据和融合知识分别来自不同的模态数据。在Brats基准上进行了综合实验，表明，与基线方法和最先进的方法相比，所提出的跨模型深度学习框架可以有效地提高大脑肿瘤分割性能。

尽管预训练的语言模型（PLM）已经在各种自然语言处理（NLP）任务上实现了最先进的表现，但在处理知识驱动的任务时，它们被证明缺乏知识。尽管为PLM注入知识做出了许多努力，但此问题仍然开放。为了应对挑战，我们提出\ textbf {dictbert}，这是一种具有词典知识增强PLM的新方法，比知识图（kg）更容易获取。在预训练期间，我们通过对比度学习将两个新颖的预训练任务注入plms：\ textit {dictionary输入预测}和\ textit {entriT {输入描述歧视}。在微调中，我们使用预先训练的dictbert作为插件知识库（KB）来检索输入序列中确定的条目的隐式知识，并将检索到的知识注入输入中，以通过新颖的额外跳动来增强其表示形式注意机制。我们评估了我们的方法，包括各种知识驱动和语言理解任务，包括NER，关系提取，CommonSenseQA，OpenBookQa和Glue。实验结果表明，我们的模型可以显着改善典型的PLM：它的大幅提高了0.5 \％，2.9 \％，9.0 \％，7.1 \％\％和3.3 \％，分别对Roberta--也有效大的。

视频文本检索一直是多模式研究中的至关重要和基本任务。大型多模式对比预训练的发展，视频文本检索的开发已大大促进，这主要侧重于粗粒或细粒对比。然而，在先前的研究中很少探索过跨粒度的对比，这是粗粒表示和细粒度表示之间的对比。与细粒度或粗粒的对比相比，交叉粒度对比度计算了粗粒粒度特征与每个细粒特征之间的相关性，并且能够过滤出不必要的细颗粒特征，这些特征由粗粒度的特征引导相似性计算，从而提高了检索的准确性。为此，本文提出了一种新型的多透明对比模型，即X-CLIP，用于视频文本检索。但是，另一个挑战在于相似性聚集问题，该问题旨在将细粒度和跨粒度相似性矩阵与实例级别的相似性汇总。为了应对这一挑战，我们提出了对相似性矩阵（AOSM）模块的关注，以使模型重点放在基本帧和单词之间的对比度上，从而降低了不必要的帧和单词对检索结果的影响。 X-CLIP具有多透明的对比度和提议的AOSM模块，在五个广泛使用的视频文本检索数据集上取得了出色的性能，包括MSR-VTT（49.3 R@1），MSVD（50.4 R@1），LSMDC（26.11）（26.1 r@1），didemo（47.8 r@1）和ActivityNet（46.2 r@1）。它的表现优于先前的最先前， +6.3％， +6.6％， +11.1％， +6.7％， +3.8％的相对改善对这些基准测试，这表明了多透明的对比度和AOSM的优势。

This paper focuses on designing efficient models with low parameters and FLOPs for dense predictions. Even though CNN-based lightweight methods have achieved stunning results after years of research, trading-off model accuracy and constrained resources still need further improvements. This work rethinks the essential unity of efficient Inverted Residual Block in MobileNetv2 and effective Transformer in ViT, inductively abstracting a general concept of Meta-Mobile Block, and we argue that the specific instantiation is very important to model performance though sharing the same framework. Motivated by this phenomenon, we deduce a simple yet efficient modern \textbf{I}nverted \textbf{R}esidual \textbf{M}obile \textbf{B}lock (iRMB) for mobile applications, which absorbs CNN-like efficiency to model short-distance dependency and Transformer-like dynamic modeling capability to learn long-distance interactions. Furthermore, we design a ResNet-like 4-phase \textbf{E}fficient \textbf{MO}del (EMO) based only on a series of iRMBs for dense applications. Massive experiments on ImageNet-1K, COCO2017, and ADE20K benchmarks demonstrate the superiority of our EMO over state-of-the-art methods, \eg, our EMO-1M/2M/5M achieve 71.5, 75.1, and 78.4 Top-1 that surpass \textbf{SoTA} CNN-/Transformer-based models, while trading-off the model accuracy and efficiency well.

Supervised Question Answering systems (QA systems) rely on domain-specific human-labeled data for training. Unsupervised QA systems generate their own question-answer training pairs, typically using secondary knowledge sources to achieve this outcome. Our approach (called PIE-QG) uses Open Information Extraction (OpenIE) to generate synthetic training questions from paraphrased passages and uses the question-answer pairs as training data for a language model for a state-of-the-art QA system based on BERT. Triples in the form of <subject, predicate, object> are extracted from each passage, and questions are formed with subjects (or objects) and predicates while objects (or subjects) are considered as answers. Experimenting on five extractive QA datasets demonstrates that our technique achieves on-par performance with existing state-of-the-art QA systems with the benefit of being trained on an order of magnitude fewer documents and without any recourse to external reference data sources.

Transformer has achieved impressive successes for various computer vision tasks. However, most of existing studies require to pretrain the Transformer backbone on a large-scale labeled dataset (e.g., ImageNet) for achieving satisfactory performance, which is usually unavailable for medical images. Additionally, due to the gap between medical and natural images, the improvement generated by the ImageNet pretrained weights significantly degrades while transferring the weights to medical image processing tasks. In this paper, we propose Bootstrap Own Latent of Transformer (BOLT), a self-supervised learning approach specifically for medical image classification with the Transformer backbone. Our BOLT consists of two networks, namely online and target branches, for self-supervised representation learning. Concretely, the online network is trained to predict the target network representation of the same patch embedding tokens with a different perturbation. To maximally excavate the impact of Transformer from limited medical data, we propose an auxiliary difficulty ranking task. The Transformer is enforced to identify which branch (i.e., online/target) is processing the more difficult perturbed tokens. Overall, the Transformer endeavours itself to distill the transformation-invariant features from the perturbed tokens to simultaneously achieve difficulty measurement and maintain the consistency of self-supervised representations. The proposed BOLT is evaluated on three medical image processing tasks, i.e., skin lesion classification, knee fatigue fracture grading and diabetic retinopathy grading. The experimental results validate the superiority of our BOLT for medical image classification, compared to ImageNet pretrained weights and state-of-the-art self-supervised learning approaches.

Knowledge graph embedding (KGE), which maps entities and relations in a knowledge graph into continuous vector spaces, has achieved great success in predicting missing links in knowledge graphs. However, knowledge graphs often contain incomplete triples that are difficult to inductively infer by KGEs. To address this challenge, we resort to analogical inference and propose a novel and general self-supervised framework AnKGE to enhance KGE models with analogical inference capability. We propose an analogical object retriever that retrieves appropriate analogical objects from entity-level, relation-level, and triple-level. And in AnKGE, we train an analogy function for each level of analogical inference with the original element embedding from a well-trained KGE model as input, which outputs the analogical object embedding. In order to combine inductive inference capability from the original KGE model and analogical inference capability enhanced by AnKGE, we interpolate the analogy score with the base model score and introduce the adaptive weights in the score function for prediction. Through extensive experiments on FB15k-237 and WN18RR datasets, we show that AnKGE achieves competitive results on link prediction task and well performs analogical inference.

Digital engineering transformation is a crucial process for the engineering paradigm shifts in the fourth industrial revolution (4IR), and artificial intelligence (AI) is a critical enabling technology in digital engineering transformation. This article discusses the following research questions: What are the fundamental changes in the 4IR? More specifically, what are the fundamental changes in engineering? What is digital engineering? What are the main uncertainties there? What is trustworthy AI? Why is it important today? What are emerging engineering paradigm shifts in the 4IR? What is the relationship between the data-intensive paradigm and digital engineering transformation? What should we do for digitalization? From investigating the pattern of industrial revolutions, this article argues that ubiquitous machine intelligence (uMI) is the defining power brought by the 4IR. Digitalization is a condition to leverage ubiquitous machine intelligence. Digital engineering transformation towards Industry 4.0 has three essential building blocks: digitalization of engineering, leveraging ubiquitous machine intelligence, and building digital trust and security. The engineering design community at large is facing an excellent opportunity to bring the new capabilities of ubiquitous machine intelligence and trustworthy AI principles, as well as digital trust, together in various engineering systems design to ensure the trustworthiness of systems in Industry 4.0.

Surgical robot automation has attracted increasing research interest over the past decade, expecting its huge potential to benefit surgeons, nurses and patients. Recently, the learning paradigm of embodied AI has demonstrated promising ability to learn good control policies for various complex tasks, where embodied AI simulators play an essential role to facilitate relevant researchers. However, existing open-sourced simulators for surgical robot are still not sufficiently supporting human interactions through physical input devices, which further limits effective investigations on how human demonstrations would affect policy learning. In this paper, we study human-in-the-loop embodied intelligence with a new interactive simulation platform for surgical robot learning. Specifically, we establish our platform based on our previously released SurRoL simulator with several new features co-developed to allow high-quality human interaction via an input device. With these, we further propose to collect human demonstrations and imitate the action patterns to achieve more effective policy learning. We showcase the improvement of our simulation environment with the designed new features and tasks, and validate state-of-the-art reinforcement learning algorithms using the interactive environment. Promising results are obtained, with which we hope to pave the way for future research on surgical embodied intelligence. Our platform is released and will be continuously updated in the website: https://med-air.github.io/SurRoL/