Prompt learning is one of the most effective and trending ways to adapt powerful vision-language foundation models like CLIP to downstream datasets by tuning learnable prompt vectors with very few samples. However, although prompt learning achieves excellent performance over in-domain data, it still faces the major challenge of generalizing to unseen classes and domains. Some existing prompt learning methods tackle this issue by adaptively generating different prompts for different tokens or domains but neglecting the ability of learned prompts to generalize to unseen domains. In this paper, we propose a novel prompt learning paradigm that directly generates domain invariant prompt generalizable to unseen domains, called MetaPrompt. Specifically, a dual-modality prompt tuning network is proposed to generate prompts for inputs from both image and text modalities. More importantly, we propose a meta-learning-based prompt tuning algorithm that explicitly constrains the prompt tuned on a specific domain or class also to achieve good performance on another domain or class. Extensive experiments on 11 datasets for base-to-new generalization and four datasets for domain generalization demonstrate that our method consistently and significantly outperforms existing methods.

带有嘈杂标签的训练深神经网络（DNN）实际上是具有挑战性的，因为不准确的标签严重降低了DNN的概括能力。以前的努力倾向于通过识别带有粗糙的小损失标准来减轻嘈杂标签的干扰的嘈杂数据来处理统一的denoising流中的零件或完整数据，而忽略了嘈杂样本的困难是不同的，因此是刚性和统一的。数据选择管道无法很好地解决此问题。在本文中，我们首先提出了一种称为CREMA的粗到精细的稳健学习方法，以分裂和串扰的方式处理嘈杂的数据。在粗糙水平中，干净和嘈杂的集合首先从统计意义上就可信度分开。由于实际上不可能正确对所有嘈杂样本进行分类，因此我们通过对每个样本的可信度进行建模来进一步处理它们。具体而言，对于清洁集，我们故意设计了一种基于内存的调制方案，以动态调整每个样本在训练过程中的历史可信度顺序方面的贡献，从而减轻了错误地分组为清洁集中的嘈杂样本的效果。同时，对于分类为嘈杂集的样品，提出了选择性标签更新策略，以纠正嘈杂的标签，同时减轻校正错误的问题。广泛的实验是基于不同方式的基准，包括图像分类（CIFAR，Clothing1M等）和文本识别（IMDB），具有合成或自然语义噪声，表明CREMA的优势和普遍性。

最近，人重新识别（REID）的隐私问题引起了越来越多的关注，并保留了REID方法使用的行人图像的隐私是必不可少的。去识别（DEID）方法通过删除与REID数据相关的身份来减轻隐私问题。但是，大多数现有的DEID方法倾向于删除所有与个人身份相关的信息，并损害REID任务上的识别数据的可用性。在本文中，我们旨在开发一种可以在REID人士的隐私保护和数据可用性之间实现良好权衡的技术。为了实现这一目标，我们提出了一种新颖的去识别方法，该方法是针对人雷德（Reid）明确设计的，命名人识别转移（PIS）。 PI在保留图像对之间的身份关系的同时，消除了行人图像中的绝对身份。通过利用变异自动编码器的插值属性，PI将每个行人图像从当前身份转移到具有新身份的另一个身份，从而导致图像仍然保留相对身份。实验结果表明，与现有的去识别方法相比，我们的方法在隐私保护和模型性能之间取决于更好的权衡，并且可以防御人类和模型攻击以确保数据隐私。

How to improve discriminative feature learning is central in classification. Existing works address this problem by explicitly increasing inter-class separability and intra-class similarity, whether by constructing positive and negative pairs for contrastive learning or posing tighter class separating margins. These methods do not exploit the similarity between different classes as they adhere to i.i.d. assumption in data. In this paper, we embrace the real-world data distribution setting that some classes share semantic overlaps due to their similar appearances or concepts. Regarding this hypothesis, we propose a novel regularization to improve discriminative learning. We first calibrate the estimated highest likelihood of one sample based on its semantically neighboring classes, then encourage the overall likelihood predictions to be deterministic by imposing an adaptive exponential penalty. As the gradient of the proposed method is roughly proportional to the uncertainty of the predicted likelihoods, we name it adaptive discriminative regularization (ADR), trained along with a standard cross entropy loss in classification. Extensive experiments demonstrate that it can yield consistent and non-trivial performance improvements in a variety of visual classification tasks (over 10 benchmarks). Furthermore, we find it is robust to long-tailed and noisy label data distribution. Its flexible design enables its compatibility with mainstream classification architectures and losses.

Zigzag flattening (ZF) is commonly utilized as a default option to get the image patches ordering in deep models, e.g. vision transformers (ViTs). Notably, when decomposing multi-scale images, ZF could not maintain the invariance of feature point positions.To this end, we investigate the Hilbert flattening (HF) as an alternative for sequence ordering in vision tasks. HF has proven to be superior to other flatten approaches in maintaining spatial locality, when performing multi-scale transformations of dimensional space. In applications, we design a position encoding method based on HF, beating absolute position encoding non-trivially in Transformer architecture. It also can be used to feature down-sampling and feature/image interpolation. Extensive experiments demonstrate that it can yield consistent performance boosts for several popular architectures and applications. The code will be released upon acceptance.

我们研究如何代表具有隐式神经表示（INRS）的视频。经典INRS方法通常利用MLP将输入坐标映射到输出像素。尽管最近的一些作品试图直接使用CNN重建整个图像。但是，我们认为，以上像素和图像策略都不利于视频数据。取而代之的是，我们提出了一个贴片解决方案PS-NERV，该解决方案将视频表示为贴片的函数和相应的补丁坐标。它自然继承了图像方法的优势，并以快速解码速度实现出色的重建性能。整个方法包括常规模块，例如位置嵌入，MLP和CNN，同时还引入了ADAIN以增强中间特征。这些简单而基本的更改可以帮助网络轻松拟合高频细节。广泛的实验证明了其在几个与视频有关的任务中的有效性，例如视频压缩和视频介绍。

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.