Perceiving and manipulating objects in a generalizable way has been actively studied by the computer vision and robotics communities, where cross-category generalizable manipulation skills are highly desired yet underexplored. In this work, we propose to learn such generalizable perception and manipulation via Generalizable and Actionable Parts (GAParts). By identifying and defining 9 GAPart classes (e.g. buttons, handles, etc), we show that our part-centric approach allows our method to learn object perception and manipulation skills from seen object categories and directly generalize to unseen categories. Following the GAPart definition, we construct a large-scale part-centric interactive dataset, GAPartNet, where rich, part-level annotations (semantics, poses) are provided for 1166 objects and 8489 part instances. Based on GAPartNet, we investigate three cross-category tasks: part segmentation, part pose estimation, and part-based object manipulation. Given the large domain gaps between seen and unseen object categories, we propose a strong 3D segmentation method from the perspective of domain generalization by integrating adversarial learning techniques. Our method outperforms all existing methods by a large margin, no matter on seen or unseen categories. Furthermore, with part segmentation and pose estimation results, we leverage the GAPart pose definition to design part-based manipulation heuristics that can generalize well to unseen object categories in both simulation and real world. The dataset and code will be released.

产生人类想要的声音效果是一个重要的话题。但是，在这一领域，很少有研究声音发电。在这项研究中，我们调查了以文本提示为条件的声音，并提出了一个新型的文本对生成框架，该框架由文本编码器组成，矢量量化了变异自动编码器（VQ-VAE），解码器和歌手。该框架首先使用解码器将从文本编码器提取的文本特征传递到借助VQ-VAE的MEL光谱图中，然后使用Vocoder将生成的MEL光谱图转换为波形。我们发现，解码器显着影响发电性能。因此，我们专注于在这项研究中设计一个好的解码器。我们从传统的自动回解码器开始，该解码器已被证明是以前的Sound Generation Works中的最先进方法。但是，AR解码器始终按顺序预测MEL-SPECTROGIN图令牌，这引入了单向偏见和错误问题的积累。此外，使用AR解码器，声音生成时间随着声音持续时间线性增加。为了克服AR解码器引入的缺点，我们提出了一个基于离散扩散模型的非自动回形解码器，称为DiffSound。具体而言，DIFFSOUND可以在一个步骤中预测所有MEL光谱图令牌，然后在下一步中完善预测的令牌，因此可以在几个步骤后获得最优于预测的结果。我们的实验表明，与AR解码器相比，我们提出的差异不仅产生更好的文本到单一生成结果，而且还具有更快的生成速度，例如MOS：3.56 \ textit {v.s} 2.786，并且生成速度为五个比AR解码器快的时间。

语音助手等对话用户界面非常受欢迎。然而，它们被设计为默认情况下是单语的，缺乏对双语对话体验的支持或敏感性。在此挑衅论文中，我们强调了双语用户VA互动中面临的语言生产挑战。我们认为，通过促进双语互动中看到的现象，例如代码转换，我们可以为双语用户提供更具包容性和改进的用户体验。我们还通过支持多种语言识别，并对语音输出中代码转换的偏好敏感，探索可以实现这一目标的方法。

我们考虑统计逆学习问题，任务是根据$ AF $的嘈杂点评估估算函数$ F $，其中$ a $是一个线性运算符。函数$ AF $在I.I.D评估。随机设计点$ u_n $，$ n = 1，...，n $由未知的一般概率分布生成。我们认为Tikhonov正规用一般凸起和$ P $-Homenecous罚款功能，并在由惩罚功能引起的对称BREGMAN距离中测量的地面真理的正则化解决方案的集中率。我们获得了Besov Norm处罚的具体率，并在数值上展示了与X射线断层扫描的背景下的观察到的率的对应。

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.