We explore the capability of plain Vision Transformers (ViTs) for semantic segmentation and propose the SegVit. Previous ViT-based segmentation networks usually learn a pixel-level representation from the output of the ViT. Differently, we make use of the fundamental component -- attention mechanism, to generate masks for semantic segmentation. Specifically, we propose the Attention-to-Mask (ATM) module, in which the similarity maps between a set of learnable class tokens and the spatial feature maps are transferred to the segmentation masks. Experiments show that our proposed SegVit using the ATM module outperforms its counterparts using the plain ViT backbone on the ADE20K dataset and achieves new state-of-the-art performance on COCO-Stuff-10K and PASCAL-Context datasets. Furthermore, to reduce the computational cost of the ViT backbone, we propose query-based down-sampling (QD) and query-based up-sampling (QU) to build a Shrunk structure. With the proposed Shrunk structure, the model can save up to $40\%$ computations while maintaining competitive performance.

二进制代码相似性检测（BCSD）方法测量了两个二进制可执行代码的相似性。最近，基于学习的BCSD方法取得了巨大的成功，在检测准确性和效率方面表现优于传统的BCSD。但是，现有的研究在基于学习的BCSD方法的对抗脆弱性上相当稀疏，这会导致与安全相关的应用程序危害。为了评估对抗性的鲁棒性，本文设计了一种高效且黑色的对抗代码生成算法，即FuncFooler。 FuncFooler限制了对抗代码1）保持程序的控制流程图（CFG）和2）保持相同的语义含义。具体而言，funcfooler连续1）在恶意代码中确定脆弱的候选人，2）从良性代码中选择和插入对抗性指令，以及3）纠正对抗代码的语义副作用以满足约束。从经验上讲，我们的FuncFooler可以成功攻击包括Safe，ASM2VEC和JTRAN在内的三种基于学习的BCSD模型，它们质疑是否需要基于学习的BCSD。

由于文件传达了丰富的人类知识，并且通常存在于企业中，因此建筑文档的对话系统已经越来越兴趣。其中，如何理解和从文档中检索信息是一个具有挑战性的研究问题。先前的工作忽略了文档的视觉属性，并将其视为纯文本，从而导致不完整的方式。在本文中，我们提出了一个布局感知文档级信息提取数据集，以促进从视觉上丰富文档（VRD）中提取结构和语义知识的研究，以在对话系统中产生准确的响应。 Lie包含来自4,061页的产品和官方文件的三个提取任务的62K注释，成为我们最大的知识，成为最大的基于VRD的信息提取数据集。我们还开发了扩展基于令牌的语言模型的基准方法，以考虑像人类这样的布局功能。经验结果表明，布局对于基于VRD的提取至关重要，系统演示还验证了提取的知识可以帮助找到用户关心的答案。

符号知识图（kgs）是通过昂贵的人众包或特定于域特异性的复杂信息提取管道来构建的。诸如BERT之类的新兴大型语言模型（LMS）已显示出隐式编码的大量知识，可以使用正确设计的提示来查询。但是，与明确的公斤相比，黑盒LMS中的知识通常很难访问或编辑，并且缺乏解释性。在这项工作中，我们旨在从LMS收获符号KG，这是一个由神经LMS的灵活性和可扩展性增强的自动kg构造的新框架。与通常依赖大型人类注释的数据或现有大量KG的先前作品相比，我们的方法仅需要对关系的最小定义作为输入，因此适合于以前无法提取有关丰富新关系的知识。该方法会自动生成多样化的提示，并在给定的LM内执行有效的知识搜索，以进行一致和广泛的输出。与以前的方法相比，使用我们的方法收获的知识要准确得多，如自动和人类评估所示。结果，我们源于多元化的LMS，一个新的KG家族（例如Bertnet和Robertanet），其中包含一套更丰富的常识关系，包括复杂的关系（例如，A对B的能力，但不擅长B”）人类注销的kg（例如概念网）。此外，由此产生的kg也是解释各自的源LMS的工具，从而导致对不同LMS不同知识能力的新见解。

在嘈杂的互联网规模数据集上进行了预测，已对具有广泛的文本，图像和其他模式能力的培训模型进行了大量研究。但是，对于许多顺序决策域，例如机器人技术，视频游戏和计算机使用，公开可用的数据不包含以相同方式训练行为先验所需的标签。我们通过半监督的模仿学习将互联网规模的预处理扩展到顺序的决策域，其中代理通过观看在线未标记的视频来学习行动。具体而言，我们表明，使用少量标记的数据，我们可以训练一个足够准确的反向动力学模型，可以标记一个巨大的未标记在线数据来源 - 在这里，在线播放Minecraft的在线视频 - 然后我们可以从中训练一般行为先验。尽管使用了本地人类界面（鼠标和键盘为20Hz），但我们表明，这种行为先验具有非平凡的零射击功能，并且可以通过模仿学习和加强学习，可以对其进行微调，以进行硬探索任务。不可能通过增强学习从头开始学习。对于许多任务，我们的模型都表现出人类水平的性能，我们是第一个报告可以制作钻石工具的计算机代理，这些工具可以花费超过20分钟（24,000个环境动作）的游戏玩法来实现。

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.

Reading comprehension of legal text can be a particularly challenging task due to the length and complexity of legal clauses and a shortage of expert-annotated datasets. To address this challenge, we introduce the Merger Agreement Understanding Dataset (MAUD), an expert-annotated reading comprehension dataset based on the American Bar Association's 2021 Public Target Deal Points Study, with over 39,000 examples and over 47,000 total annotations. Our fine-tuned Transformer baselines show promising results, with models performing well above random on most questions. However, on a large subset of questions, there is still room for significant improvement. As the only expert-annotated merger agreement dataset, MAUD is valuable as a benchmark for both the legal profession and the NLP community.

An increasing number of public datasets have shown a marked clinical impact on assessing anatomical structures. However, each of the datasets is small, partially labeled, and rarely investigates severe tumor subjects. Moreover, current models are limited to segmenting specific organs/tumors, which can not be extended to novel domains and classes. To tackle these limitations, we introduce embedding learned from Contrastive Language-Image Pre-training (CLIP) to segmentation models, dubbed the CLIP-Driven Universal Model. The Universal Model can better segment 25 organs and 6 types of tumors by exploiting the semantic relationship between abdominal structures. The model is developed from an assembly of 14 datasets with 3,410 CT scans and evaluated on 6,162 external CT scans from 3 datasets. We rank first on the public leaderboard of the Medical Segmentation Decathlon (MSD) and achieve the state-of-the-art results on Beyond The Cranial Vault (BTCV). Compared with dataset-specific models, the Universal Model is computationally more efficient (6x faster), generalizes better to CT scans from varying sites, and shows stronger transfer learning performance on novel tasks. The design of CLIP embedding enables the Universal Model to be easily extended to new classes without catastrophically forgetting the previously learned classes.

In recent years, the Transformer architecture has shown its superiority in the video-based person re-identification task. Inspired by video representation learning, these methods mainly focus on designing modules to extract informative spatial and temporal features. However, they are still limited in extracting local attributes and global identity information, which are critical for the person re-identification task. In this paper, we propose a novel Multi-Stage Spatial-Temporal Aggregation Transformer (MSTAT) with two novel designed proxy embedding modules to address the above issue. Specifically, MSTAT consists of three stages to encode the attribute-associated, the identity-associated, and the attribute-identity-associated information from the video clips, respectively, achieving the holistic perception of the input person. We combine the outputs of all the stages for the final identification. In practice, to save the computational cost, the Spatial-Temporal Aggregation (STA) modules are first adopted in each stage to conduct the self-attention operations along the spatial and temporal dimensions separately. We further introduce the Attribute-Aware and Identity-Aware Proxy embedding modules (AAP and IAP) to extract the informative and discriminative feature representations at different stages. All of them are realized by employing newly designed self-attention operations with specific meanings. Moreover, temporal patch shuffling is also introduced to further improve the robustness of the model. Extensive experimental results demonstrate the effectiveness of the proposed modules in extracting the informative and discriminative information from the videos, and illustrate the MSTAT can achieve state-of-the-art accuracies on various standard benchmarks.

We introduce Argoverse 2 (AV2) - a collection of three datasets for perception and forecasting research in the self-driving domain. The annotated Sensor Dataset contains 1,000 sequences of multimodal data, encompassing high-resolution imagery from seven ring cameras, and two stereo cameras in addition to lidar point clouds, and 6-DOF map-aligned pose. Sequences contain 3D cuboid annotations for 26 object categories, all of which are sufficiently-sampled to support training and evaluation of 3D perception models. The Lidar Dataset contains 20,000 sequences of unlabeled lidar point clouds and map-aligned pose. This dataset is the largest ever collection of lidar sensor data and supports self-supervised learning and the emerging task of point cloud forecasting. Finally, the Motion Forecasting Dataset contains 250,000 scenarios mined for interesting and challenging interactions between the autonomous vehicle and other actors in each local scene. Models are tasked with the prediction of future motion for "scored actors" in each scenario and are provided with track histories that capture object location, heading, velocity, and category. In all three datasets, each scenario contains its own HD Map with 3D lane and crosswalk geometry - sourced from data captured in six distinct cities. We believe these datasets will support new and existing machine learning research problems in ways that existing datasets do not. All datasets are released under the CC BY-NC-SA 4.0 license.