高质量的HDRI（高动态范围图像），通常是HDR Panoramas，是创建图形中3D场景的3D场景的最受欢迎的方法之一。考虑到捕获HDRI的困难，高度需要一种多功能和可控的生成模型，外行用户可以直观地控制生成过程。但是，现有的最新方法仍然难以合成复杂场景的高质量全景。在这项工作中，我们提出了一个零击文本驱动的框架Text2Light，以生成4K+分辨率HDRIS，而无需配对培训数据。给定一个自由形式的文本作为场景的描述，我们通过两个专用步骤合成相应的HDRI：1）在低动态范围（LDR）（LDR）和低分辨率的文本驱动全景生成，以及2）超分辨率逆音映射在分辨率和动态范围内扩大LDR Panorama。具体来说，为了获得零击文本驱动的全景生成，我们首先将双代码簿作为不同环境纹理的离散表示形式。然后，在预先训练的剪辑模型的驱动下，一个文本条件的全局采样器学会了根据输入文本从全局代码簿中采样整体语义。此外，一个结构感知的本地采样器学会了以整体语义为指导的LDR Panoramas逐个贴片。为了获得超分辨率的逆音映射，我们从LDR Panorama得出了360度成像的连续表示，作为一组固定在球体上的结构性潜在代码。这种连续表示可以使多功能模块同时提高分辨率和动态范围。广泛的实验证明了Text2light在产生高质量HDR全景方面具有卓越的能力。此外，我们还展示了我们在现实渲染和沉浸式VR中工作的可行性。

人类的重大是一项非常可取但具有挑战性的任务。现有作品要么需要使用光阶段捕获的昂贵的一亮（OLAT）捕获的数据，要么无法自由地改变渲染身体的观点。在这项工作中，我们提出了一个原则上的框架，即Relighting4D，该框架可以使自由观看点仅在未知的照明下从人类视频中获得重新拍摄。我们的关键见解是，可以将人体的几何形状和反射率分解为正常，遮挡，弥漫和镜头图的一组神经场。这些神经场进一步整合到反射性吸引物理的渲染中，其中神经场中的每个顶点吸收并反映了环境的光。可以以一种自我监督的方式从视频中学到整个框架，并采用专门的知识培训为正则化。对真实和合成数据集的广泛实验表明，我们的框架能够通过自由观看点重新确认动态人类参与者。

已知深度学习模型容易受到针对恶意目的设计的对抗性例子的影响，并且对人类感知系统是无法察觉的。自动编码器仅在良性示例上接受训练时，已广泛用于（自我监管的）对抗检测，基于以下假设，即对抗性示例会产生较大的重建误差。但是，由于其训练中缺乏对抗性示例和自动编码器的过于强大的概括能力，因此在实践中，这种假设并不总是成立的。为了减轻这个问题，我们探索如何在自动编码器结构下使用分离的标签/语义特征检测对抗性示例。具体而言，我们提出了基于删除表示的重建（DRR）。在DRR中，我们对正确配对的标签/语义功能和错误配对的标签/语义功能进行训练，以重建良性和反描述。这模仿了对抗性示例的行为，并可以降低自动编码器的不必要的概括能力。我们将我们的方法与不同的对抗性攻击和不同受害者模型下的最先进的自我监督检测方法进行了比较，并且在各种指标（ROC曲线下的区域，真实的正率和真实的负率）中表现出更好的性能）对于大多数攻击设置。尽管DRR最初是为视觉任务设计的，但我们证明它也可以轻松扩展到自然语言任务。值得注意的是，与其他基于自动编码器的检测器不同，我们的方法可以为自适应对手提供抗性。

Deep learning models can achieve high accuracy when trained on large amounts of labeled data. However, real-world scenarios often involve several challenges: Training data may become available in installments, may originate from multiple different domains, and may not contain labels for training. Certain settings, for instance medical applications, often involve further restrictions that prohibit retention of previously seen data due to privacy regulations. In this work, to address such challenges, we study unsupervised segmentation in continual learning scenarios that involve domain shift. To that end, we introduce GarDA (Generative Appearance Replay for continual Domain Adaptation), a generative-replay based approach that can adapt a segmentation model sequentially to new domains with unlabeled data. In contrast to single-step unsupervised domain adaptation (UDA), continual adaptation to a sequence of domains enables leveraging and consolidation of information from multiple domains. Unlike previous approaches in incremental UDA, our method does not require access to previously seen data, making it applicable in many practical scenarios. We evaluate GarDA on two datasets with different organs and modalities, where it substantially outperforms existing techniques.

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.

As one of the prevalent methods to achieve automation systems, Imitation Learning (IL) presents a promising performance in a wide range of domains. However, despite the considerable improvement in policy performance, the corresponding research on the explainability of IL models is still limited. Inspired by the recent approaches in explainable artificial intelligence methods, we proposed a model-agnostic explaining framework for IL models called R2RISE. R2RISE aims to explain the overall policy performance with respect to the frames in demonstrations. It iteratively retrains the black-box IL model from the randomized masked demonstrations and uses the conventional evaluation outcome environment returns as the coefficient to build an importance map. We also conducted experiments to investigate three major questions concerning frames' importance equality, the effectiveness of the importance map, and connections between importance maps from different IL models. The result shows that R2RISE successfully distinguishes important frames from the demonstrations.

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.

We propose a distributionally robust return-risk model for Markov decision processes (MDPs) under risk and reward ambiguity. The proposed model optimizes the weighted average of mean and percentile performances, and it covers the distributionally robust MDPs and the distributionally robust chance-constrained MDPs (both under reward ambiguity) as special cases. By considering that the unknown reward distribution lies in a Wasserstein ambiguity set, we derive the tractable reformulation for our model. In particular, we show that that the return-risk model can also account for risk from uncertain transition kernel when one only seeks deterministic policies, and that a distributionally robust MDP under the percentile criterion can be reformulated as its nominal counterpart at an adjusted risk level. A scalable first-order algorithm is designed to solve large-scale problems, and we demonstrate the advantages of our proposed model and algorithm through numerical experiments.

Witnessing the impressive achievements of pre-training techniques on large-scale data in the field of computer vision and natural language processing, we wonder whether this idea could be adapted in a grab-and-go spirit, and mitigate the sample inefficiency problem for visuomotor driving. Given the highly dynamic and variant nature of the input, the visuomotor driving task inherently lacks view and translation invariance, and the visual input contains massive irrelevant information for decision making, resulting in predominant pre-training approaches from general vision less suitable for the autonomous driving task. To this end, we propose PPGeo (Policy Pre-training via Geometric modeling), an intuitive and straightforward fully self-supervised framework curated for the policy pretraining in visuomotor driving. We aim at learning policy representations as a powerful abstraction by modeling 3D geometric scenes on large-scale unlabeled and uncalibrated YouTube driving videos. The proposed PPGeo is performed in two stages to support effective self-supervised training. In the first stage, the geometric modeling framework generates pose and depth predictions simultaneously, with two consecutive frames as input. In the second stage, the visual encoder learns driving policy representation by predicting the future ego-motion and optimizing with the photometric error based on current visual observation only. As such, the pre-trained visual encoder is equipped with rich driving policy related representations and thereby competent for multiple visuomotor driving tasks. Extensive experiments covering a wide span of challenging scenarios have demonstrated the superiority of our proposed approach, where improvements range from 2% to even over 100% with very limited data. Code and models will be available at https://github.com/OpenDriveLab/PPGeo.

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.