Generating realistic motions for digital humans is a core but challenging part of computer animations and games, as human motions are both diverse in content and rich in styles. While the latest deep learning approaches have made significant advancements in this domain, they mostly consider motion synthesis and style manipulation as two separate problems. This is mainly due to the challenge of learning both motion contents that account for the inter-class behaviour and styles that account for the intra-class behaviour effectively in a common representation. To tackle this challenge, we propose a denoising diffusion probabilistic model solution for styled motion synthesis. As diffusion models have a high capacity brought by the injection of stochasticity, we can represent both inter-class motion content and intra-class style behaviour in the same latent. This results in an integrated, end-to-end trained pipeline that facilitates the generation of optimal motion and exploration of content-style coupled latent space. To achieve high-quality results, we design a multi-task architecture of diffusion model that strategically generates aspects of human motions for local guidance. We also design adversarial and physical regulations for global guidance. We demonstrate superior performance with quantitative and qualitative results and validate the effectiveness of our multi-task architecture.

早期预测在临床上被认为是脑瘫（CP）治疗的重要部分之一。我们建议实施一个基于一般运动评估（GMA）的CP预测的低成本和可解释的分类系统。我们设计了一个基于Pytorch的注意力图形卷积网络，以识别从RGB视频中提取的骨骼数据中有CP风险的早期婴儿。我们还设计了一个频率模块，用于在过滤噪声时学习频域中的CP运动。我们的系统仅需要消费级RGB视频进行培训，以通过提供可解释的CP分类结果来支持交互式时间CP预测。

帕金森氏病（PD）是一种进行性神经退行性疾病，导致各种运动功能障碍症状，包括震颤，胸肌，僵硬和姿势不稳定。 PD的诊断主要取决于临床经验，而不是确定的医学测试，诊断准确性仅为73-84％，因为它受到不同医学专家的主观意见或经验的挑战。因此，有效且可解释的自动PD诊断系统对于支持更强大的诊断决策的临床医生很有价值。为此，我们建议对帕金森的震颤进行分类，因为它是PD的最主要症状之一，具有强烈的普遍性。与其他计算机辅助时间和资源消耗的帕金森震颤（PT）分类系统不同，我们提出了SPAPNET，该系统仅需要消费者级的非侵入性视频记录人类面向摄像机的动作作为输入，以提供无诊断低成本分类的患者作为PD警告标志。我们首次提议使用带有轻质金字塔通道 - 融合式结构的新型注意模块来提取相关的PT信息并有效地过滤噪声。这种设计有助于提高分类性能和系统的解释性。实验结果表明，我们的系统在将PT与非PT类别分类中的平衡精度达到90.9％和90.6％的F1得分来胜过最先进的。

在这项工作中，我们在分配强化学习方面建立了最新的进步，以基于IQN提供模型的最新分配变体。我们通过使用GAN模型的生成器和鉴别器功能与分位数回归来实现这一目标，从而近似于状态返回分布的完整分位数。我们证明了基线数据集的性能提高-57 Atari 2600游戏。此外，我们使用算法来显示Atari游戏中风险敏感政策的最新培训表现，并通过政策优化和评估。

在高维度中，大多数机器学习方法即使有一些异常值也要脆弱。为了解决这个问题，我们希望与基础学习者一起引入一种新方法，例如贝叶斯回归或随机梯度下降，以解决模型中脆弱性的问题。由于迷你批次梯度下降允许比批处理梯度下降具有更强的收敛性，因此我们使用Mini Batch梯度下降（称为Mini Batch梯度下降）使用Trimming（MBGDT）来使用一种方法。当我们在设计数据集中应用方法时，我们的方法显示出最先进的性能，并且比几个基线具有更大的鲁棒性。

Masked image modeling (MIM) performs strongly in pre-training large vision Transformers (ViTs). However, small models that are critical for real-world applications cannot or only marginally benefit from this pre-training approach. In this paper, we explore distillation techniques to transfer the success of large MIM-based pre-trained models to smaller ones. We systematically study different options in the distillation framework, including distilling targets, losses, input, network regularization, sequential distillation, etc, revealing that: 1) Distilling token relations is more effective than CLS token- and feature-based distillation; 2) An intermediate layer of the teacher network as target perform better than that using the last layer when the depth of the student mismatches that of the teacher; 3) Weak regularization is preferred; etc. With these findings, we achieve significant fine-tuning accuracy improvements over the scratch MIM pre-training on ImageNet-1K classification, using all the ViT-Tiny, ViT-Small, and ViT-base models, with +4.2%/+2.4%/+1.4% gains, respectively. Our TinyMIM model of base size achieves 52.2 mIoU in AE20K semantic segmentation, which is +4.1 higher than the MAE baseline. Our TinyMIM model of tiny size achieves 79.6% top-1 accuracy on ImageNet-1K image classification, which sets a new record for small vision models of the same size and computation budget. This strong performance suggests an alternative way for developing small vision Transformer models, that is, by exploring better training methods rather than introducing inductive biases into architectures as in most previous works. Code is available at https://github.com/OliverRensu/TinyMIM.

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.

Blind image quality assessment (BIQA) remains challenging due to the diversity of distortion and image content variation, which complicate the distortion patterns crossing different scales and aggravate the difficulty of the regression problem for BIQA. However, existing BIQA methods often fail to consider multi-scale distortion patterns and image content, and little research has been done on learning strategies to make the regression model produce better performance. In this paper, we propose a simple yet effective Progressive Multi-Task Image Quality Assessment (PMT-IQA) model, which contains a multi-scale feature extraction module (MS) and a progressive multi-task learning module (PMT), to help the model learn complex distortion patterns and better optimize the regression issue to align with the law of human learning process from easy to hard. To verify the effectiveness of the proposed PMT-IQA model, we conduct experiments on four widely used public datasets, and the experimental results indicate that the performance of PMT-IQA is superior to the comparison approaches, and both MS and PMT modules improve the model's performance.

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.