总生存时间（OS）时间是神经胶质瘤情况最重要的评估指数之一。多模式磁共振成像（MRI）扫描在神经胶质瘤预后OS时间的研究中起重要作用。为多模式MRI问题的OS时间预测提出了几种基于学习的方法。但是，这些方法通常在深度学习网络开始或结束时融合多模式信息，并且缺乏来自不同尺度的特征。此外，网络末尾的融合始终适应全球（例如，在全球平均池输出串联后完全连接）或与局部（例如，双线性池）的融合，这会失去与全球局部的局部信息。在本文中，我们提出了一种用于对脑肿瘤患者的多模式OS时间预测的新方法，该方法包含在不同尺度上引入的改进的非局部特征融合模块。我们的方法比当前最新方法获得了相对8.76％的改善（0.6989 vs. 0.6426的精度）。广泛的测试表明，我们的方法可以适应缺失方式的情况。该代码可在https://github.com/tangwen920812/mmmna-net上找到。

在临床实践中，由于较短的获取时间和较低的存储成本，通常使用了平面分辨率低的各向异性体积医学图像。然而，粗分辨率可能导致医生或计算机辅助诊断算法的医学诊断困难。基于深度学习的体积超分辨率（SR）方法是改善分辨率的可行方法，其核心是卷积神经网络（CNN）。尽管进展最近，但这些方法受到卷积运算符的固有属性的限制，卷积运算符忽略内容相关性，无法有效地对远程依赖性进行建模。此外，大多数现有方法都使用伪配合的体积进行训练和评估，其中伪低分辨率（LR）体积是通过简单的高分辨率（HR）对应物的简单降解而产生的。但是，伪和现实LR之间的域间隙导致这些方法在实践中的性能不佳。在本文中，我们构建了第一个公共实用数据集RPLHR-CT作为体积SR的基准，并通过重新实现四种基于CNN的最先进的方法来提供基线结果。考虑到CNN的固有缺点，我们还提出了基于注意力机制的变压器体积超分辨率网络（TVSRN），完全与卷积分配。这是首次将纯变压器用于CT体积SR的研究。实验结果表明，TVSRN在PSNR和SSIM上的所有基准都显着胜过。此外，TVSRN方法在图像质量，参数数量和运行时间之间取得了更好的权衡。数据和代码可在https://github.com/smilenaxx/rplhr-ct上找到。

通过纵向病变跟踪评估病变进展和治疗反应在临床实践中起着至关重要的作用。当手动进行病变匹配时，该任务的自动化方法是由劳动力成本和时间消耗的促进的。以前的方法通常缺乏本地和全球信息的集成。在这项工作中，我们提出了一种基于变压器的方法，称为变压器病变跟踪器（TLT）。具体而言，我们设计了一个基于注意力的变压器（CAT），以捕获和组合全球和本地信息以增强特征提取。我们还开发了一个基于注册的解剖注意模块（RAAM），以向CAT介绍解剖信息，以便它可以专注于有用的特征知识。提出了一种稀疏选择策略（SSS），用于选择特征和减少变压器训练中的内存足迹。此外，我们使用全球回归来进一步提高模型性能。我们在公共数据集上进行实验，以显示我们方法的优势，并发现我们的模型性能使欧几里得中心的平均误差至少提高了至少14.3％（6mm vs. 7mm），而不是先进的ART（SOTA） ）。代码可在https://github.com/tangwen920812/tlt上找到。

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.

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.