对看不见的环境变化的深入强化学习的概括通常需要对大量各种培训变化进行政策学习。我们从经验上观察到，接受过许多变化的代理商（通才）倾向于在一开始就更快地学习，但是长期以来其最佳水平的性能高原。相比之下，只接受一些变体培训的代理商（专家）通常可以在有限的计算预算下获得高回报。为了两全其美，我们提出了一个新颖的通才特权训练框架。具体来说，我们首先培训一名通才的所有环境变化。当它无法改善时，我们会推出大量的专家，并从通才克隆过重量，每个人都接受了训练，以掌握选定的一小部分变化子集。我们终于通过所有专家的示范引起的辅助奖励恢复了通才的培训。特别是，我们调查了开始专业培训的时机，并在专家的帮助下比较策略以学习通才。我们表明，该框架将政策学习的信封推向了包括Procgen，Meta-World和Maniskill在内的几个具有挑战性和流行的基准。

3D视觉输入的对象操纵对构建可宽大的感知和政策模型构成了许多挑战。然而，现有基准中的3D资产主要缺乏与拓扑和几何中的现实世界内复杂的3D形状的多样性。在这里，我们提出了Sapien操纵技能基准（Manishill）以在全物理模拟器中的各种物体上基准操纵技巧。 Manishill中的3D资产包括大型课堂内拓扑和几何变化。仔细选择任务以涵盖不同类型的操纵挑战。 3D Vision的最新进展也使我们认为我们应该定制基准，以便挑战旨在邀请研究3D深入学习的研究人员。为此，我们模拟了一个移动的全景摄像头，返回以自我为中心的点云或RGB-D图像。此外，我们希望Manishill是为一个对操纵研究感兴趣的广泛研究人员提供服务。除了支持从互动的政策学习，我们还支持学习 - 从演示（LFD）方法，通过提供大量的高质量演示（〜36,000个成功的轨迹，总共〜1.5米点云/ RGB-D帧）。我们提供使用3D深度学习和LFD算法的基线。我们的基准（模拟器，环境，SDK和基线）的所有代码都是开放的，并且将基于基准举办跨学科研究人员面临的挑战。

由于其令人鼓舞的性能，在各种控制任务中的令人鼓舞的表现，深增强学习（Deep RL）一直在受到更高的关注。然而，在训练神经网络中的常规正则化技术（例如，$ L_2 $正则化，辍学）已经在RL方法中被忽略，可能是因为代理通常在相同的环境中进行培训和评估，因为Deep RL社区重点关注更多-Level算法设计。在这项工作中，我们在连续控制任务中提出了具有多种策略优化算法的正则化技术的第一综合研究。有趣的是，我们发现策略网络上的传统正则化技术通常可以带来大量改进，特别是在更难的任务上。我们的研究结果显示在训练HyperParameter变化方面是强大的。我们还将这些技术与更广泛使用的熵正则化进行了比较。此外，我们还研究正规化不同的组件，并发现策略网络通常是最佳的。我们进一步分析了为什么正则化可能有助于从四个观点来帮助推广 - 样本复杂性，奖励分配，重量规范和噪音鲁棒性。我们希望我们的研究为未来的规则策略优化算法提供指导。我们的代码可在https://github.com/xuanlinli17/ICLRR2021_RLREG上获得。

无人驾驶飞机（UAV）的实时对象检测是一个具有挑战性的问题，因为Edge GPU设备作为物联网（IoT）节点的计算资源有限。为了解决这个问题，在本文中，我们提出了一种基于Yolox模型的新型轻型深度学习体系结构，用于Edge GPU上的实时对象检测。首先，我们设计了一个有效且轻巧的PixSF头，以更换Yolox的原始头部以更好地检测小物体，可以将其进一步嵌入深度可分离的卷积（DS Conv）中，以达到更轻的头。然后，开发为减少网络参数的颈层中的较小结构，这是精度和速度之间的权衡。此外，我们将注意模块嵌入头层中，以改善预测头的特征提取效果。同时，我们还改进了标签分配策略和损失功能，以减轻UAV数据集的类别不平衡和盒子优化问题。最后，提出了辅助头进行在线蒸馏，以提高PIXSF Head中嵌入位置嵌入和特征提取的能力。在NVIDIA Jetson NX和Jetson Nano GPU嵌入平台上，我们的轻质模型的性能得到了实验验证。扩展的实验表明，与目前的模型相比，Fasterx模型在Visdrone2021数据集中实现了更好的折衷和延迟之间的折衷。

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.

Automatic music generation with artificial intelligence typically requires a large amount of data which is hard to obtain for many less common genres and musical instruments. To tackle this issue, we present ongoing work and preliminary findings on the possibility for deep models to transfer knowledge from language to music, by finetuning large language models pre-trained on a massive text corpus on only hundreds of MIDI files of drum performances. We show that by doing so, one of the largest, state-of-the-art models (GPT3) is capable of generating reasonable drum grooves, while models that are not pre-trained (Transformer) shows no such ability beyond naive repetition. Evaluating generated music is a challenging task, more so is evaluating drum grooves with little precedence in literature. Hence, we propose a tailored structural evaluation method and analyze drum grooves produced by GPT3 compared to those played by human professionals, exposing the strengths and weaknesses of such generation by language-to-music transfer. Our findings suggest that language-to-music transfer learning with large language models is viable and promising.

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.