When reading a story, humans can rapidly understand new fictional characters with a few observations, mainly by drawing analogy to fictional and real people they met before in their lives. This reflects the few-shot and meta-learning essence of humans' inference of characters' mental states, i.e., humans' theory-of-mind (ToM), which is largely ignored in existing research. We fill this gap with a novel NLP benchmark, TOM-IN-AMC, the first assessment of models' ability of meta-learning of ToM in a realistic narrative understanding scenario. Our benchmark consists of $\sim$1,000 parsed movie scripts for this purpose, each corresponding to a few-shot character understanding task; and requires models to mimic humans' ability of fast digesting characters with a few starting scenes in a new movie. Our human study verified that humans can solve our problem by inferring characters' mental states based on their previously seen movies; while the state-of-the-art metric-learning and meta-learning approaches adapted to our task lags 30% behind.

随着生成模型的快速发展，基于AI的面部操纵技术，称为DeepFakes，已经变得越来越真实。这种脸部伪造的方法可以攻击任何目标，这对个人隐私和财产安全构成了新的威胁。此外，滥用合成视频在许多领域都显示出潜在的危险，例如身份骚扰，色情和新闻谣言。受到生理信号中的空间相干性和时间一致性在所生物的内容中被破坏的事实，我们试图找到可以区分真实视频和合成视频的不一致模式，从面部像素的变化是与生理信息高度相关的。我们的方法首先将多个高斯级别的eulerian视频放大倍数（EVM）应用于原始视频，以扩大面部血容量的变化引起的生理变化，然后将原始视频和放大的视频转换为多尺度欧拉宽度的空间 - 时间地图（MemstMap），其可以代表不同八度的时变的生理增强序列。然后，这些地图以列为单位重新装入帧修补程序，并发送到视觉变压器以学习帧级别的时空描述符。最后，我们整理了嵌入功能并输出判断视频是真实还是假的概率。我们在面部框架++和DeepFake检测数据集上验证了我们的方法。结果表明，我们的模型在伪造检测中实现了出色的性能，并在交叉数据域中显示出出色的泛化能力。

侯马联盟书是中国山西博物馆小镇博物馆的国家宝藏之一。它在研究古老的历史方面具有重要的历史意义。迄今为止，关于霍玛联盟书籍的研究一直留在纸质文件的识别中，这是无法识别和难以显示，学习和宣传的纸质文件。因此，霍玛联盟公认的古代角色的数字化可以有效提高识别古代角色并提供更可靠的技术支持和文本数据的效率。本文提出了一个新的Houma Alliance书籍的新数据库。在数据库中，从原始书籍收藏和人类的模仿写作中收集了297个班级和3,547个Houma Alliance古代手写字符样本。此外，决策级分类器融合策略用于融合三个众所周知的深神网络体系结构，以供古代手写角色识别。实验是在我们的新数据库上执行的。实验结果首先为研究界提供了新数据库的基线结果，然后证明了我们提出的方法的效率。

最近，蒙面图像建模（MIM）由于其能力从大量未标记的数据中学习而引起了人们的关注，并且已被证明对涉及自然图像的各种视觉任务有效。同时，由于未标记的图像的数量高，预计3D医学图像中的自我监督学习的潜力预计将是巨大的，以及质量标签的费用和困难。但是，MIM对医学图像的适用性仍然不确定。在本文中，我们证明了掩盖的图像建模方法还可以推进3D医学图像分析，除了自然图像。我们研究掩盖图像建模策略如何从3D医学图像分割的角度利用性能作为代表性的下游任务：i）与天真的对比度学习相比，蒙版的图像建模方法可以加快监督培训的收敛性，甚至更快（1.40美元$ \ times $ \ times $ $ $ ）并最终产生更高的骰子分数； ii）预测具有较高掩盖比和相对较小的贴片大小的原始体素值是用于医学图像建模的非平凡的自我监督借口任务； iii）重建的轻质解码器或投影头设计对于3D医学图像上的掩盖图像建模非常有力，该图像加快了训练并降低成本； iv）最后，我们还研究了在不同的实际情况下使用不同图像分辨率和标记的数据比率的MIM方法的有效性。

In this paper, we propose a robust 3D detector, named Cross Modal Transformer (CMT), for end-to-end 3D multi-modal detection. Without explicit view transformation, CMT takes the image and point clouds tokens as inputs and directly outputs accurate 3D bounding boxes. The spatial alignment of multi-modal tokens is performed implicitly, by encoding the 3D points into multi-modal features. The core design of CMT is quite simple while its performance is impressive. CMT obtains 73.0% NDS on nuScenes benchmark. Moreover, CMT has a strong robustness even if the LiDAR is missing. Code will be released at https://github.com/junjie18/CMT.

Knowledge graphs (KG) have served as the key component of various natural language processing applications. Commonsense knowledge graphs (CKG) are a special type of KG, where entities and relations are composed of free-form text. However, previous works in KG completion and CKG completion suffer from long-tail relations and newly-added relations which do not have many know triples for training. In light of this, few-shot KG completion (FKGC), which requires the strengths of graph representation learning and few-shot learning, has been proposed to challenge the problem of limited annotated data. In this paper, we comprehensively survey previous attempts on such tasks in the form of a series of methods and applications. Specifically, we first introduce FKGC challenges, commonly used KGs, and CKGs. Then we systematically categorize and summarize existing works in terms of the type of KGs and the methods. Finally, we present applications of FKGC models on prediction tasks in different areas and share our thoughts on future research directions of FKGC.

Few Shot Instance Segmentation (FSIS) requires models to detect and segment novel classes with limited several support examples. In this work, we explore a simple yet unified solution for FSIS as well as its incremental variants, and introduce a new framework named Reference Twice (RefT) to fully explore the relationship between support/query features based on a Transformer-like framework. Our key insights are two folds: Firstly, with the aid of support masks, we can generate dynamic class centers more appropriately to re-weight query features. Secondly, we find that support object queries have already encoded key factors after base training. In this way, the query features can be enhanced twice from two aspects, i.e., feature-level and instance-level. In particular, we firstly design a mask-based dynamic weighting module to enhance support features and then propose to link object queries for better calibration via cross-attention. After the above steps, the novel classes can be improved significantly over our strong baseline. Additionally, our new framework can be easily extended to incremental FSIS with minor modification. When benchmarking results on the COCO dataset for FSIS, gFSIS, and iFSIS settings, our method achieves a competitive performance compared to existing approaches across different shots, e.g., we boost nAP by noticeable +8.2/+9.4 over the current state-of-the-art FSIS method for 10/30-shot. We further demonstrate the superiority of our approach on Few Shot Object Detection. Code and model will be available.

Graph Neural Networks (GNNs) have shown satisfying performance on various graph learning tasks. To achieve better fitting capability, most GNNs are with a large number of parameters, which makes these GNNs computationally expensive. Therefore, it is difficult to deploy them onto edge devices with scarce computational resources, e.g., mobile phones and wearable smart devices. Knowledge Distillation (KD) is a common solution to compress GNNs, where a light-weighted model (i.e., the student model) is encouraged to mimic the behavior of a computationally expensive GNN (i.e., the teacher GNN model). Nevertheless, most existing GNN-based KD methods lack fairness consideration. As a consequence, the student model usually inherits and even exaggerates the bias from the teacher GNN. To handle such a problem, we take initial steps towards fair knowledge distillation for GNNs. Specifically, we first formulate a novel problem of fair knowledge distillation for GNN-based teacher-student frameworks. Then we propose a principled framework named RELIANT to mitigate the bias exhibited by the student model. Notably, the design of RELIANT is decoupled from any specific teacher and student model structures, and thus can be easily adapted to various GNN-based KD frameworks. We perform extensive experiments on multiple real-world datasets, which corroborates that RELIANT achieves less biased GNN knowledge distillation while maintaining high prediction utility.

This paper focuses on designing efficient models with low parameters and FLOPs for dense predictions. Even though CNN-based lightweight methods have achieved stunning results after years of research, trading-off model accuracy and constrained resources still need further improvements. This work rethinks the essential unity of efficient Inverted Residual Block in MobileNetv2 and effective Transformer in ViT, inductively abstracting a general concept of Meta-Mobile Block, and we argue that the specific instantiation is very important to model performance though sharing the same framework. Motivated by this phenomenon, we deduce a simple yet efficient modern \textbf{I}nverted \textbf{R}esidual \textbf{M}obile \textbf{B}lock (iRMB) for mobile applications, which absorbs CNN-like efficiency to model short-distance dependency and Transformer-like dynamic modeling capability to learn long-distance interactions. Furthermore, we design a ResNet-like 4-phase \textbf{E}fficient \textbf{MO}del (EMO) based only on a series of iRMBs for dense applications. Massive experiments on ImageNet-1K, COCO2017, and ADE20K benchmarks demonstrate the superiority of our EMO over state-of-the-art methods, \eg, our EMO-1M/2M/5M achieve 71.5, 75.1, and 78.4 Top-1 that surpass \textbf{SoTA} CNN-/Transformer-based models, while trading-off the model accuracy and efficiency well.

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.