事件摄像机是受到生物启发的视觉传感器，异步代表像素级亮度随着事件流而变化。基于事件的单眼多视图立体声（EMV）是一种利用事件流以估算具有已知轨迹的半密度3D结构的技术。对于基于事件的单眼大满贯，这是一项关键任务。但是，所需的密集计算工作负载使其对于嵌入式平台上的实时部署而具有挑战性。在本文中，通过实现最关键和最耗时的阶段，包括事件反向预测和FPGA上的体积射线计数，提出Eventor作为快速有效的EMV加速器。高度平行且完全管道的处理元素是通过FPGA专门设计的，并与嵌入式臂集成为异质系统，以改善吞吐量并减少记忆足迹。同时，通过重新安排，近似计算和混合数据量化，将EMVS算法重新制定为更硬件的方式。戴维斯数据集的评估结果表明，与英特尔i5 CPU平台相比，Eventor的能源效率最高可提高$ 24 \ times $。

心肌活力的评估对于患有心肌梗塞的患者的诊断和治疗管理是必不可少的，并且心肌病理学的分类是本评估的关键。这项工作定义了医学图像分析的新任务，即进行心肌病理分割（MYOPS）结合三个序列的心脏磁共振（CMR）图像，该图像首次与Mycai 2020一起在Myops挑战中提出的。挑战提供了45个配对和预对准的CMR图像，允许算法将互补信息与三个CMR序列组合到病理分割。在本文中，我们提供了挑战的详细信息，从十五个参与者的作品调查，并根据五个方面解释他们的方法，即预处理，数据增强，学习策略，模型架构和后处理。此外，我们对不同因素的结果分析了结果，以检查关键障碍和探索解决方案的潜力，以及为未来的研究提供基准。我们得出结论，虽然报告了有前途的结果，但研究仍处于早期阶段，在成功应用于诊所之前需要更深入的探索。请注意，MyOPS数据和评估工具继续通过其主页（www.sdspeople.fudan.edu.cn/zhuangxiahai/0/myops20 /）注册注册。

基于拉曼扩增的物理特征，我们提出了一个基于神经网络（NN）和线性回归的三步建模方案。与基于纯NN的方法相比，通过模拟证明了更高的精度，较少的数据需求和较低的计算复杂性。

我们分析了实时对象检测模型的网络结构，发现功能串联阶段中的特征非常丰富。在此处应用注意模块可以有效提高模型的检测准确性。但是，常用的注意模块或自我发项模块在检测准确性和推理效率方面的性能差。因此，我们提出了一个新型的自我发场模块，称为颈部网络的特征串联阶段，称为2D局部特征叠加的自我注意。这个自我发场模块通过局部特征和本地接收场反映了全球特征。我们还建议并优化有效的脱钩头和AB-OTA，并实现SOTA结果。对于使用我们建议的改进，获得了49.0 \％（66.2 fps），46.1 \％（80.6 fps）和39.1 \％（100 fps）的平均精度。我们的模型平均精度超过了Yolov5 0.8 \％-3.1 \％。

从稀疏的LIDAR扫描中恢复密集的深度图像是一个具有挑战性的任务。尽管对稀疏密集深度完成的颜色引导方法的普及，但它们在优化期间平等地处理了像素，忽略了稀疏深度图中的不均匀分布特性和合成的地面真理中的累积异常值。在这项工作中，我们引入了不确定性驱动的损失功能，以提高深度完成的鲁棒性，并处理深度完成的不确定性。具体而言，我们提出了一个明确的不确定性制定，用于与Jeffrey之前的强大深度完成。将参数不确定驱动的损耗引入并转换为对嘈杂或缺少数据的强大的新损耗函数。同时，我们提出了一种多尺度联合预测模型，可以同时预测深度和不确定性地图。估计的不确定性图还用于对具有高不确定性的像素对像素对的自适应预测，导致剩余地图以改进完成结果。我们的方法已经在基蒂深度完成基准上进行了测试，并在Mae，Imae和Irmse指标方面取得了最先进的鲁棒性能。

Single Image Super-Resolution (SISR) tasks have achieved significant performance with deep neural networks. However, the large number of parameters in CNN-based met-hods for SISR tasks require heavy computations. Although several efficient SISR models have been recently proposed, most are handcrafted and thus lack flexibility. In this work, we propose a novel differentiable Neural Architecture Search (NAS) approach on both the cell-level and network-level to search for lightweight SISR models. Specifically, the cell-level search space is designed based on an information distillation mechanism, focusing on the combinations of lightweight operations and aiming to build a more lightweight and accurate SR structure. The network-level search space is designed to consider the feature connections among the cells and aims to find which information flow benefits the cell most to boost the performance. Unlike the existing Reinforcement Learning (RL) or Evolutionary Algorithm (EA) based NAS methods for SISR tasks, our search pipeline is fully differentiable, and the lightweight SISR models can be efficiently searched on both the cell-level and network-level jointly on a single GPU. Experiments show that our methods can achieve state-of-the-art performance on the benchmark datasets in terms of PSNR, SSIM, and model complexity with merely 68G Multi-Adds for $\times 2$ and 18G Multi-Adds for $\times 4$ SR tasks.

Compressed videos often exhibit visually annoying artifacts, known as Perceivable Encoding Artifacts (PEAs), which dramatically degrade video visual quality. Subjective and objective measures capable of identifying and quantifying various types of PEAs are critical in improving visual quality. In this paper, we investigate the influence of four spatial PEAs (i.e. blurring, blocking, bleeding, and ringing) and two temporal PEAs (i.e. flickering and floating) on video quality. For spatial artifacts, we propose a visual saliency model with a low computational cost and higher consistency with human visual perception. In terms of temporal artifacts, self-attention based TimeSFormer is improved to detect temporal artifacts. Based on the six types of PEAs, a quality metric called Saliency-Aware Spatio-Temporal Artifacts Measurement (SSTAM) is proposed. Experimental results demonstrate that the proposed method outperforms state-of-the-art metrics. We believe that SSTAM will be beneficial for optimizing video coding techniques.

As one of the most important psychic stress reactions, micro-expressions (MEs), are spontaneous and transient facial expressions that can reveal the genuine emotions of human beings. Thus, recognizing MEs (MER) automatically is becoming increasingly crucial in the field of affective computing, and provides essential technical support in lie detection, psychological analysis and other areas. However, the lack of abundant ME data seriously restricts the development of cutting-edge data-driven MER models. Despite the recent efforts of several spontaneous ME datasets to alleviate this problem, it is still a tiny amount of work. To solve the problem of ME data hunger, we construct a dynamic spontaneous ME dataset with the largest current ME data scale, called DFME (Dynamic Facial Micro-expressions), which includes 7,526 well-labeled ME videos induced by 671 participants and annotated by more than 20 annotators throughout three years. Afterwards, we adopt four classical spatiotemporal feature learning models on DFME to perform MER experiments to objectively verify the validity of DFME dataset. In addition, we explore different solutions to the class imbalance and key-frame sequence sampling problems in dynamic MER respectively on DFME, so as to provide a valuable reference for future research. The comprehensive experimental results show that our DFME dataset can facilitate the research of automatic MER, and provide a new benchmark for MER. DFME will be published via https://mea-lab-421.github.io.

Face Anti-spoofing (FAS) is essential to secure face recognition systems from various physical attacks. However, recent research generally focuses on short-distance applications (i.e., phone unlocking) while lacking consideration of long-distance scenes (i.e., surveillance security checks). In order to promote relevant research and fill this gap in the community, we collect a large-scale Surveillance High-Fidelity Mask (SuHiFiMask) dataset captured under 40 surveillance scenes, which has 101 subjects from different age groups with 232 3D attacks (high-fidelity masks), 200 2D attacks (posters, portraits, and screens), and 2 adversarial attacks. In this scene, low image resolution and noise interference are new challenges faced in surveillance FAS. Together with the SuHiFiMask dataset, we propose a Contrastive Quality-Invariance Learning (CQIL) network to alleviate the performance degradation caused by image quality from three aspects: (1) An Image Quality Variable module (IQV) is introduced to recover image information associated with discrimination by combining the super-resolution network. (2) Using generated sample pairs to simulate quality variance distributions to help contrastive learning strategies obtain robust feature representation under quality variation. (3) A Separate Quality Network (SQN) is designed to learn discriminative features independent of image quality. Finally, a large number of experiments verify the quality of the SuHiFiMask dataset and the superiority of the proposed CQIL.

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.