本文旨在解决多个对象跟踪（MOT），这是计算机视觉中的一个重要问题，但由于许多实际问题，尤其是阻塞，因此仍然具有挑战性。确实，我们提出了一种新的实时深度透视图 - 了解多个对象跟踪（DP-MOT）方法，以解决MOT中的闭塞问题。首先提出了一个简单但有效的主题深度估计（SODE），以在2D场景中自动以无监督的方式自动订购检测到的受试者的深度位置。使用SODE的输出，提出了一个新的活动伪3D KALMAN滤波器，即具有动态控制变量的Kalman滤波器的简单但有效的扩展，以动态更新对象的运动。此外，在数据关联步骤中提出了一种新的高阶关联方法，以合并检测到的对象之间的一阶和二阶关系。与标准MOT基准的最新MOT方法相比，提出的方法始终达到最先进的性能。

我们研究逆增强学习（IRL）和模仿学习（IM），这是从专家所证明的轨迹中恢复奖励或政策功能的问题。我们提出了一种新的方法来通过在最大的熵框架中添加权重功能来改善学习过程，并具有学习和恢复专家政策的随机性（或有限理性）的动机。我们的框架和算法允许学习奖励（或政策）功能以及添加到马尔可夫决策过程中的熵条款的结构，从而增强了学习过程。我们使用人类和模拟演示以及通过离散和连续的IRL/IM任务进行的数值实验表明，我们的方法表现优于先前的算法。

分解表示形式通常被用于年龄不变的面部识别（AIFR）任务。但是，这些方法已经达到了一些局限性，（1）具有年龄标签的大规模面部识别（FR）培训数据的要求，这在实践中受到限制； （2）高性能的重型深网架构； （3）他们的评估通常是在与年龄相关的面部数据库上进行的，同时忽略了标准的大规模FR数据库以确保鲁棒性。这项工作提出了一种新颖的轻巧的角度蒸馏（LIAAD）方法，用于克服这些限制的大规模轻量级AIFR。鉴于两个具有不同专业知识的教师，LIAAD引入了学习范式，以有效地提炼老年人的专注和棱角分明的知识，从这些老师到轻量级的学生网络，使其更强大，以更高的fr准确性和稳健的年龄，从而有效地提炼了一个学习范式因素。因此，LIAAD方法能够采用带有和不具有年龄标签的两个FR数据集的优势来训练AIFR模型。除了先前的蒸馏方法主要关注封闭设置问题中的准确性和压缩比，我们的LIAAD旨在解决开放式问题，即大规模的面部识别。对LFW，IJB-B和IJB-C Janus，AgeDB和Megaface-Fgnet的评估证明了拟议方法在轻重量结构上的效率。这项工作还提出了一个新的纵向面部衰老（Logiface）数据库\ footNote {将提供该数据库}，以进一步研究未来与年龄相关的面部问题。

In this paper, we propose a novel technique, namely INVALIDATOR, to automatically assess the correctness of APR-generated patches via semantic and syntactic reasoning. INVALIDATOR reasons about program semantic via program invariants while it also captures program syntax via language semantic learned from large code corpus using the pre-trained language model. Given a buggy program and the developer-patched program, INVALIDATOR infers likely invariants on both programs. Then, INVALIDATOR determines that a APR-generated patch overfits if: (1) it violates correct specifications or (2) maintains errors behaviors of the original buggy program. In case our approach fails to determine an overfitting patch based on invariants, INVALIDATOR utilizes a trained model from labeled patches to assess patch correctness based on program syntax. The benefit of INVALIDATOR is three-fold. First, INVALIDATOR is able to leverage both semantic and syntactic reasoning to enhance its discriminant capability. Second, INVALIDATOR does not require new test cases to be generated but instead only relies on the current test suite and uses invariant inference to generalize the behaviors of a program. Third, INVALIDATOR is fully automated. We have conducted our experiments on a dataset of 885 patches generated on real-world programs in Defects4J. Experiment results show that INVALIDATOR correctly classified 79% overfitting patches, accounting for 23% more overfitting patches being detected by the best baseline. INVALIDATOR also substantially outperforms the best baselines by 14% and 19% in terms of Accuracy and F-Measure, respectively.

When using LiDAR semantic segmentation models for safety-critical applications such as autonomous driving, it is essential to understand and improve their robustness with respect to a large range of LiDAR corruptions. In this paper, we aim to comprehensively analyze the robustness of LiDAR semantic segmentation models under various corruptions. To rigorously evaluate the robustness and generalizability of current approaches, we propose a new benchmark called SemanticKITTI-C, which features 16 out-of-domain LiDAR corruptions in three groups, namely adverse weather, measurement noise and cross-device discrepancy. Then, we systematically investigate 11 LiDAR semantic segmentation models, especially spanning different input representations (e.g., point clouds, voxels, projected images, and etc.), network architectures and training schemes. Through this study, we obtain two insights: 1) We find out that the input representation plays a crucial role in robustness. Specifically, under specific corruptions, different representations perform variously. 2) Although state-of-the-art methods on LiDAR semantic segmentation achieve promising results on clean data, they are less robust when dealing with noisy data. Finally, based on the above observations, we design a robust LiDAR segmentation model (RLSeg) which greatly boosts the robustness with simple but effective modifications. It is promising that our benchmark, comprehensive analysis, and observations can boost future research in robust LiDAR semantic segmentation for safety-critical applications.

In recent years, arbitrary image style transfer has attracted more and more attention. Given a pair of content and style images, a stylized one is hoped that retains the content from the former while catching style patterns from the latter. However, it is difficult to simultaneously keep well the trade-off between the content details and the style features. To stylize the image with sufficient style patterns, the content details may be damaged and sometimes the objects of images can not be distinguished clearly. For this reason, we present a new transformer-based method named STT for image style transfer and an edge loss which can enhance the content details apparently to avoid generating blurred results for excessive rendering on style features. Qualitative and quantitative experiments demonstrate that STT achieves comparable performance to state-of-the-art image style transfer methods while alleviating the content leak problem.

With the increasing ability of large language models (LLMs), in-context learning (ICL) has become a new paradigm for natural language processing (NLP), where LLMs make predictions only based on contexts augmented with a few training examples. It has been a new trend exploring ICL to evaluate and extrapolate the ability of LLMs. In this paper, we aim to survey and summarize the progress, challenges, and future work in ICL. We first present a formal definition of ICL and clarify its correlation to related studies. Then, we organize and discuss advanced techniques of ICL, including training strategies, prompting strategies, and so on. Finally, we present the challenges of ICL and provide potential directions for further research. We hope our work can encourage more research on uncovering how ICL works and improving ICL in future work.

Gaze estimation is the fundamental basis for many visual tasks. Yet, the high cost of acquiring gaze datasets with 3D annotations hinders the optimization and application of gaze estimation models. In this work, we propose a novel Head-Eye redirection parametric model based on Neural Radiance Field, which allows dense gaze data generation with view consistency and accurate gaze direction. Moreover, our head-eye redirection parametric model can decouple the face and eyes for separate neural rendering, so it can achieve the purpose of separately controlling the attributes of the face, identity, illumination, and eye gaze direction. Thus diverse 3D-aware gaze datasets could be obtained by manipulating the latent code belonging to different face attributions in an unsupervised manner. Extensive experiments on several benchmarks demonstrate the effectiveness of our method in domain generalization and domain adaptation for gaze estimation tasks.

Generalizability to unseen forgery types is crucial for face forgery detectors. Recent works have made significant progress in terms of generalization by synthetic forgery data augmentation. In this work, we explore another path for improving the generalization. Our goal is to reduce the features that are easy to learn in the training phase, so as to reduce the risk of overfitting on specific forgery types. Specifically, in our method, a teacher network takes as input the face images and generates an attention map of the deep features by a diverse multihead attention ViT. The attention map is used to guide a student network to focus on the low-attended features by reducing the highly-attended deep features. A deep feature mixup strategy is also proposed to synthesize forgeries in the feature domain. Experiments demonstrate that, without data augmentation, our method is able to achieve promising performances on unseen forgeries and highly compressed data.

The development of deep learning models in medical image analysis is majorly limited by the lack of large-sized and well-annotated datasets. Unsupervised learning does not require labels and is more suitable for solving medical image analysis problems. However, most of the current unsupervised learning methods need to be applied to large datasets. To make unsupervised learning applicable to small datasets, we proposed Swin MAE, which is a masked autoencoder with Swin Transformer as its backbone. Even on a dataset of only a few thousand medical images and without using any pre-trained models, Swin MAE is still able to learn useful semantic features purely from images. It can equal or even slightly outperform the supervised model obtained by Swin Transformer trained on ImageNet in terms of the transfer learning results of downstream tasks. The code will be publicly available soon.