几乎没有学习的学习是一种新兴的学习范式，它试图以低样本复杂性学习以模仿人类仅基于几个示例学习，概括和推断的方式。尽管FSL试图模仿这些人类特征，但从根本上讲，FSL的任务常规描述和建模，并使用基于情节的训练进行元学习并不完全与人类与知识的获取和理性的方式完全吻合。 FSL进行了情节培训，虽然仅使用每个测试课的$ K $实例，但仍需要大量的分离培训课程中的标记实例。在本文中，我们介绍了限制几次学习的新颖任务（CFSL），这是FSL的特殊情况，其中每个班级的培训实例数量被限制为小于某些值$ m $，从而在期间应用了类似的限制培训和测试。我们提出了一种使用一种受认知理论（例如模糊痕迹理论和原型理论）启发的新型分类对比损失来利用CFSL利用CFSL的方法。

将深度学习与象征性逻辑推理相结合旨在利用这两个领域的成功，并引起越来越多的关注。受到深度循环的启发，这是一种端到端的模型，该模型训练了逻辑程序的推理，我们引入了Ima-Glove-GA，这是一种以自然语言表达的多步推理的迭代神经推理网络。在我们的模型中，推理是使用基于带门注意机制的RNN的迭代记忆神经网络进行的。我们在三个数据集上评估了iMa-glove-ga：副本，Conceptrules V1和Conceptrules V2。实验结果表明，与DeepLo​​gic和其他RNN基线模型相比，深沟和栅极注意可以达到更高的测试精度。当规则被淘汰时，我们的模型比罗伯塔·洛尔格（Roberta-Large）实现了更好的分布概括。此外，为了解决当前多步推理数据集中推理深度分布不平衡分布的问题，我们开发了Pararule-Plus，这是一个大型数据集，其中包含更多需要更深入推理步骤的示例。实验结果表明，添加Pararule-Plus可以在需要更深层次深度的示例中提高模型的性能。源代码和数据可在https://github.com/strong-ai-lab/multi-step-deductive-reasoning-over-natural语言中获得。

自动问题质量评级（AQQR）旨在通过计算手段来评估问题质量，从而解决在线学习者的问题存储库中的出现挑战。 AQQR的现有方法仅依赖于明确定义的标准，如可读性和字数，同时不充分利用最先进的深度学习技术。我们提出DeepQR，这是一个用于AQQR的新型神经网络模型，该模型是使用从PEERWOSE，一个广泛使用的学习者平台收集的多项选择问题（MCQ）数据集进行培训的AQQR。除了设计DeepQR之外，我们还研究了基于明确定义的功能或语义功能或两者的模型。我们还引入了一种自我关注机制来捕获MCQ组件之间的语义相关性，以及使用质量评级获取问题表示的对比学习方法。从八个大学级课程中收集的数据集进行了广泛的实验，说明DeepQR在六种比较模型方面具有卓越的性能。

平均野外游戏（MFGS）提供了一个可在数学上拖动的框架，用于通过利用平均场理论来简化代理之间的相互作用来建模大规模多代理系统。它使应用逆增强学习（IRL）能够通过从展示的行为中恢复奖励信号来预测大人群的行为。但是，现有的MFG的IRL方法无能为力，无法确定各个代理的行为中的不确定性。本文提出了一个新颖的框架，平均场对抗IRL（MF-AIRL），该框架能够解决示范中的不确定性。我们在最大熵IRL和新的平衡概念上建立MF-AIRL。我们通过不完美的演示评估了对模拟任务的方法。实验结果证明了MF-AIRL比奖励恢复中现有方法的优越性。

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.

Dataset distillation has emerged as a prominent technique to improve data efficiency when training machine learning models. It encapsulates the knowledge from a large dataset into a smaller synthetic dataset. A model trained on this smaller distilled dataset can attain comparable performance to a model trained on the original training dataset. However, the existing dataset distillation techniques mainly aim at achieving the best trade-off between resource usage efficiency and model utility. The security risks stemming from them have not been explored. This study performs the first backdoor attack against the models trained on the data distilled by dataset distillation models in the image domain. Concretely, we inject triggers into the synthetic data during the distillation procedure rather than during the model training stage, where all previous attacks are performed. We propose two types of backdoor attacks, namely NAIVEATTACK and DOORPING. NAIVEATTACK simply adds triggers to the raw data at the initial distillation phase, while DOORPING iteratively updates the triggers during the entire distillation procedure. We conduct extensive evaluations on multiple datasets, architectures, and dataset distillation techniques. Empirical evaluation shows that NAIVEATTACK achieves decent attack success rate (ASR) scores in some cases, while DOORPING reaches higher ASR scores (close to 1.0) in all cases. Furthermore, we conduct a comprehensive ablation study to analyze the factors that may affect the attack performance. Finally, we evaluate multiple defense mechanisms against our backdoor attacks and show that our attacks can practically circumvent these defense mechanisms.

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.

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.

Benefiting from the intrinsic supervision information exploitation capability, contrastive learning has achieved promising performance in the field of deep graph clustering recently. However, we observe that two drawbacks of the positive and negative sample construction mechanisms limit the performance of existing algorithms from further improvement. 1) The quality of positive samples heavily depends on the carefully designed data augmentations, while inappropriate data augmentations would easily lead to the semantic drift and indiscriminative positive samples. 2) The constructed negative samples are not reliable for ignoring important clustering information. To solve these problems, we propose a Cluster-guided Contrastive deep Graph Clustering network (CCGC) by mining the intrinsic supervision information in the high-confidence clustering results. Specifically, instead of conducting complex node or edge perturbation, we construct two views of the graph by designing special Siamese encoders whose weights are not shared between the sibling sub-networks. Then, guided by the high-confidence clustering information, we carefully select and construct the positive samples from the same high-confidence cluster in two views. Moreover, to construct semantic meaningful negative sample pairs, we regard the centers of different high-confidence clusters as negative samples, thus improving the discriminative capability and reliability of the constructed sample pairs. Lastly, we design an objective function to pull close the samples from the same cluster while pushing away those from other clusters by maximizing and minimizing the cross-view cosine similarity between positive and negative samples. Extensive experimental results on six datasets demonstrate the effectiveness of CCGC compared with the existing state-of-the-art algorithms.

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.