Colonoscopy, currently the most efficient and recognized colon polyp detection technology, is necessary for early screening and prevention of colorectal cancer. However, due to the varying size and complex morphological features of colonic polyps as well as the indistinct boundary between polyps and mucosa, accurate segmentation of polyps is still challenging. Deep learning has become popular for accurate polyp segmentation tasks with excellent results. However, due to the structure of polyps image and the varying shapes of polyps, it is easy for existing deep learning models to overfit the current dataset. As a result, the model may not process unseen colonoscopy data. To address this, we propose a new state-of-the-art model for medical image segmentation, the SSFormer, which uses a pyramid Transformer encoder to improve the generalization ability of models. Specifically, our proposed Progressive Locality Decoder can be adapted to the pyramid Transformer backbone to emphasize local features and restrict attention dispersion. The SSFormer achieves stateof-the-art performance in both learning and generalization assessment.

Color fundus photography and Optical Coherence Tomography (OCT) are the two most cost-effective tools for glaucoma screening. Both two modalities of images have prominent biomarkers to indicate glaucoma suspected. Clinically, it is often recommended to take both of the screenings for a more accurate and reliable diagnosis. However, although numerous algorithms are proposed based on fundus images or OCT volumes in computer-aided diagnosis, there are still few methods leveraging both of the modalities for the glaucoma assessment. Inspired by the success of Retinal Fundus Glaucoma Challenge (REFUGE) we held previously, we set up the Glaucoma grAding from Multi-Modality imAges (GAMMA) Challenge to encourage the development of fundus \& OCT-based glaucoma grading. The primary task of the challenge is to grade glaucoma from both the 2D fundus images and 3D OCT scanning volumes. As part of GAMMA, we have publicly released a glaucoma annotated dataset with both 2D fundus color photography and 3D OCT volumes, which is the first multi-modality dataset for glaucoma grading. In addition, an evaluation framework is also established to evaluate the performance of the submitted methods. During the challenge, 1272 results were submitted, and finally, top-10 teams were selected to the final stage. We analysis their results and summarize their methods in the paper. Since all these teams submitted their source code in the challenge, a detailed ablation study is also conducted to verify the effectiveness of the particular modules proposed. We find many of the proposed techniques are practical for the clinical diagnosis of glaucoma. As the first in-depth study of fundus \& OCT multi-modality glaucoma grading, we believe the GAMMA Challenge will be an essential starting point for future research.

Recent studies on semi-supervised semantic segmentation (SSS) have seen fast progress. Despite their promising performance, current state-of-the-art methods tend to increasingly complex designs at the cost of introducing more network components and additional training procedures. Differently, in this work, we follow a standard teacher-student framework and propose AugSeg, a simple and clean approach that focuses mainly on data perturbations to boost the SSS performance. We argue that various data augmentations should be adjusted to better adapt to the semi-supervised scenarios instead of directly applying these techniques from supervised learning. Specifically, we adopt a simplified intensity-based augmentation that selects a random number of data transformations with uniformly sampling distortion strengths from a continuous space. Based on the estimated confidence of the model on different unlabeled samples, we also randomly inject labelled information to augment the unlabeled samples in an adaptive manner. Without bells and whistles, our simple AugSeg can readily achieve new state-of-the-art performance on SSS benchmarks under different partition protocols.

基于激光雷达的3D单一对象跟踪是机器人技术和自动驾驶中的一个具有挑战性的问题。当前，现有方法通常会遇到长距离对象通常具有非常稀疏或部分倾斜的点云的问题，这使得模型含糊不清。模棱两可的功能将很难找到目标对象，并最终导致不良跟踪结果。为了解决此问题，我们使用功能强大的变压器体系结构，并为基于点云的3D单一对象跟踪任务提出一个点轨转换器（PTT）模块。具体而言，PTT模块通过计算注意力重量来生成微调的注意力特征，该功能指导追踪器的重点关注目标的重要功能，并提高复杂场景中的跟踪能力。为了评估我们的PTT模块，我们将PTT嵌入主要方法中，并构建一个名为PTT-NET的新型3D SOT跟踪器。在PTT-NET中，我们分别将PTT嵌入了投票阶段和提案生成阶段。投票阶段中的PTT模块可以模拟点斑块之间的交互作用，该点贴片学习上下文依赖于上下文。同时，提案生成阶段中的PTT模块可以捕获对象和背景之间的上下文信息。我们在Kitti和Nuscenes数据集上评估了PTT-NET。实验结果证明了PTT模块的有效性和PTT-NET的优越性，PTT-NET的优势超过了基线，在CAR类别中〜10％。同时，我们的方法在稀疏场景中也具有显着的性能提高。通常，变压器和跟踪管道的组合使我们的PTT-NET能够在两个数据集上实现最先进的性能。此外，PTT-NET可以在NVIDIA 1080TI GPU上实时以40fps实时运行。我们的代码是为研究社区开源的，网址为https://github.com/shanjiayao/ptt。

物理知识的神经网络（PINN）已成为解决各种域中的部分微分方程（PDE）的强大工具。尽管PINNS的先前研究主要集中在训练期间构建和平衡损失功能以避免最小值，但采样搭配点对PINNS性能的影响很大程度上被忽略了。在这项工作中，我们发现PINN的性能可以随着不同的采样策略而显着变化，并且使用固定的搭配点可能对PINNS与正确解决方案的收敛性很小。特别是（1）我们假设对PINN的培训依赖于从初始和/或边界条件点到内部点的成功“传播”，而采样策略差的PINN可能会卡在琐事的解决方案上，如果有\ textit {传播失败}。 （2）我们证明，传播失败的特征是高度不平衡的PDE残留场，在非常狭窄的区域中观察到非常高的残留物。 （3）为了减轻传播失败，我们提出了一种新颖的\ textit {Evolutionary采样}（EVO）方法，该方法可以逐步积累高PDE残差区域中的搭配点。我们进一步提供EVO的扩展，以尊重因果关系原理，同时解决时间依赖性PDE。我们从经验上证明了我们提出的方法在各种PDE问题中的功效和效率。

人工智能的一种令人信服的应用是生成一个目标人执行任意所需运动的视频（来自来源的人）。虽然最新的方法能够合成一个视频，展示了类似的宽带运动细节，但它们通常缺乏纹理细节。相关的表现出现为扭曲的脸，脚和手，这种缺陷是人类观察者对人的非常敏感的。此外，当前的方法通常采用L2损失的GAN来评估生成的视频的真实性，固有地需要大量的培训样品来学习纹理细节以进行足够的视频生成。在这项工作中，我们从三个方面应对这些挑战：1）我们将每个视频框架分解为前景（人）和背景，重点是生成前景，以减少网络输出的基本维度。 2）我们提出了一种理论上动机的Gromov-Wasserstein损失，可促进从姿势到前景图像学习地图。 3）为了增强纹理细节，我们用几何指导编码面部特征，并使用当地甘斯来完善面部，脚和手。广泛的实验表明，我们的方法能够生成现实的目标人视频，忠实地从源人员那里复制复杂的动作。我们的代码和数据集在https://github.com/sifann/fakemotion上发布

The development of social media user stance detection and bot detection methods rely heavily on large-scale and high-quality benchmarks. However, in addition to low annotation quality, existing benchmarks generally have incomplete user relationships, suppressing graph-based account detection research. To address these issues, we propose a Multi-Relational Graph-Based Twitter Account Detection Benchmark (MGTAB), the first standardized graph-based benchmark for account detection. To our knowledge, MGTAB was built based on the largest original data in the field, with over 1.55 million users and 130 million tweets. MGTAB contains 10,199 expert-annotated users and 7 types of relationships, ensuring high-quality annotation and diversified relations. In MGTAB, we extracted the 20 user property features with the greatest information gain and user tweet features as the user features. In addition, we performed a thorough evaluation of MGTAB and other public datasets. Our experiments found that graph-based approaches are generally more effective than feature-based approaches and perform better when introducing multiple relations. By analyzing experiment results, we identify effective approaches for account detection and provide potential future research directions in this field. Our benchmark and standardized evaluation procedures are freely available at: https://github.com/GraphDetec/MGTAB.

Interview has been regarded as one of the most crucial step for recruitment. To fully prepare for the interview with the recruiters, job seekers usually practice with mock interviews between each other. However, such a mock interview with peers is generally far away from the real interview experience: the mock interviewers are not guaranteed to be professional and are not likely to behave like a real interviewer. Due to the rapid growth of online recruitment in recent years, recruiters tend to have online interviews, which makes it possible to collect real interview data from real interviewers. In this paper, we propose a novel application named EZInterviewer, which aims to learn from the online interview data and provides mock interview services to the job seekers. The task is challenging in two ways: (1) the interview data are now available but still of low-resource; (2) to generate meaningful and relevant interview dialogs requires thorough understanding of both resumes and job descriptions. To address the low-resource challenge, EZInterviewer is trained on a very small set of interview dialogs. The key idea is to reduce the number of parameters that rely on interview dialogs by disentangling the knowledge selector and dialog generator so that most parameters can be trained with ungrounded dialogs as well as the resume data that are not low-resource. Evaluation results on a real-world job interview dialog dataset indicate that we achieve promising results to generate mock interviews. With the help of EZInterviewer, we hope to make mock interview practice become easier for job seekers.

Dynamic treatment regimes assign personalized treatments to patients sequentially over time based on their baseline information and time-varying covariates. In mobile health applications, these covariates are typically collected at different frequencies over a long time horizon. In this paper, we propose a deep spectral Q-learning algorithm, which integrates principal component analysis (PCA) with deep Q-learning to handle the mixed frequency data. In theory, we prove that the mean return under the estimated optimal policy converges to that under the optimal one and establish its rate of convergence. The usefulness of our proposal is further illustrated via simulations and an application to a diabetes dataset.

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.