常规的几杆分类（FSC）旨在识别出有限标记的数据的新课程中的样本。最近，已经提出了域泛化FSC（DG-FSC），目的是识别来自看不见的域的新型类样品。 DG-FSC由于基础类（用于培训）和新颖类（评估中遇到）之间的域移位，对许多模型构成了巨大的挑战。在这项工作中，我们为解决DG-FSC做出了两个新颖的贡献。我们的首要贡献是提出重生网络（BAN）情节培训，并全面研究其对DG-FSC的有效性。作为一种特定的知识蒸馏形式，已证明禁令可以通过封闭式设置来改善常规监督分类的概括。这种改善的概括促使我们研究了DG-FSC的禁令，我们表明禁令有望解决DG-FSC中遇到的域转移。在令人鼓舞的发现的基础上，我们的第二个（主要）贡献是提出很少的禁令，FS-Ban，这是DG-FSC的新型禁令方法。我们提出的FS-BAN包括新颖的多任务学习目标：相互正则化，不匹配的老师和元控制温度，这些目标都是专门设计的，旨在克服DG-FSC中的中心和独特挑战，即过度拟合和领域差异。我们分析了这些技术的不同设计选择。我们使用六个数据集和三个基线模型进行全面的定量和定性分析和评估。结果表明，我们提出的FS-BAN始终提高基线模型的概括性能，并达到DG-FSC的最先进的准确性。

这项工作研究了标签平滑（LS）和知识蒸馏（KD）之间的兼容性。解决这一论文陈述的当代发现采取二分法的观点：Muller等。 （2019）和Shen等。 （2021b）。至关重要的是，没有努力理解和解决这些矛盾的发现，留下了原始问题 - 顺利还是不平稳教师网络？ - 未得到答复。我们工作的主要贡献是对系统扩散的发现，分析和验证是缺失的概念，这在理解和解决这些矛盾的发现方面具有重要作用。这种系统的扩散基本上削减了从LS训练的老师蒸馏的好处，从而使KD在升高的温度无效时使KD呈现。我们的发现得到了大规模实验，分析和案例研究的全面支持，包括图像分类，神经机器翻译和紧凑的学生蒸馏任务，这些任务跨越了多个数据集和教师 - 学生架构。根据我们的分析，我们建议从业者使用具有低温转移的LS训练的老师来实现高性能学生。代码和型号可在https://keshik6.github.io/revisiting-ls-kd-compatibility/

显着对象检测（SOD）是一个流行而重要的主题，旨在精确检测和分割图像中有趣的区域。我们将语言信息集成到专为显着对象检测任务的基于视觉的U结构网络中。实验基于新创建的DUTS Cross Modal（DUTS-CM）数据集，该数据集包含视觉和语言标签。我们提出了一个称为高效跨模式自我注意力（ECMSA）的新模块，以结合视觉和语言特征并提高原始U结构网络的性能。同时，为了减轻标签的沉重负担，我们通过训练基于DUTS-CM数据集的图像标题模型来采用半监督的学习方法，该模型可以自动标记其他数据集（如Dut-omron和HKU-IS）。综合实验表明，通过自然语言输入可以提高SOD的性能，并且与其他SOD方法相比具有竞争力。

在本文中，我们提出了两种技术，即联合建模和数据增强，以改善视听场景分类（AVSC）的系统性能。我们采用仅在图像数据集中培训的预训练网络来提取视频嵌入；而对于音频嵌入模型，我们决定从头开始训练它们。我们探索不同的神经网络体系结构，以有效地结合视频和音频方式。此外，研究了数据增强策略以增加视听训练设置的规模。对于视频方式，验证了兰德金几个操作的有效性。提出了Audio-Video关节混合方案，以进一步改善AVSC的性能。在Tau Urban Audio Visual Spacees 2021的开发集中，我们的最终系统可以在提交给Dcase 2021 Task 1B的所有单个AVSC系统中达到94.2％的最佳准确性。

卷积神经网络（CNN）已在许多物联网（IoT）设备中应用于多种下游任务。但是，随着边缘设备上的数据量的增加，CNN几乎无法及时完成某些任务，而计算和存储资源有限。最近，过滤器修剪被认为是压缩和加速CNN的有效技术，但是从压缩高维张量的角度来看，现有的方法很少是修剪CNN。在本文中，我们提出了一种新颖的理论，可以在三维张量中找到冗余信息，即量化特征图（QSFM）之间的相似性，并利用该理论来指导滤波器修剪过程。我们在数据集（CIFAR-10，CIFAR-100和ILSVRC-12）上执行QSFM和Edge设备，证明所提出的方法可以在神经网络中找到冗余信息，具有可比的压缩和可耐受的准确性下降。没有任何微调操作，QSFM可以显着压缩CIFAR-56（48.7％的Flops和57.9％的参数），而TOP-1的准确性仅损失0.54％。对于边缘设备的实际应用，QSFM可以将Mobilenet-V2推理速度加速1.53倍，而ILSVRC-12 TOP-1的精度仅损失1.23％。

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.

Nowadays, time-stamped web documents related to a general news query floods spread throughout the Internet, and timeline summarization targets concisely summarizing the evolution trajectory of events along the timeline. Unlike traditional document summarization, timeline summarization needs to model the time series information of the input events and summarize important events in chronological order. To tackle this challenge, in this paper, we propose a Unified Timeline Summarizer (UTS) that can generate abstractive and extractive timeline summaries in time order. Concretely, in the encoder part, we propose a graph-based event encoder that relates multiple events according to their content dependency and learns a global representation of each event. In the decoder part, to ensure the chronological order of the abstractive summary, we propose to extract the feature of event-level attention in its generation process with sequential information remained and use it to simulate the evolutionary attention of the ground truth summary. The event-level attention can also be used to assist in extracting summary, where the extracted summary also comes in time sequence. We augment the previous Chinese large-scale timeline summarization dataset and collect a new English timeline dataset. Extensive experiments conducted on these datasets and on the out-of-domain Timeline 17 dataset show that UTS achieves state-of-the-art performance in terms of both automatic and human evaluations.