放射学报告在向医生宣传医学发现方面发挥着关键作用。在每次报告中，印象部分总结了基本放射学结果。在临床实践中，写入印象是非常需要的，耗时且易于放射科学家的错误。因此，自动印象生成被出现为有吸引力的研究方向，以促进这种临床实践。现有研究主要集中在将突出词信息引入普通文本摘要框架，以指导放射学发现中的关键内容的选择。但是，对于此任务，模型不仅需要捕获调查结果中的重要词语，而且还可以准确地描述它们的关系，以便产生高质量的印象。在本文中，我们提出了一种用于自动印象生成的新方法，其中单词图是从调查结果创建临界词汇的研究，然后设计了一个单词图引导摘要模型（WGSUM），旨在通过帮助生成印象字形图。两个数据集，OpenI和MIMIC-CXR的实验结果证实了我们所提出的方法的有效性和有效性，在两个数据集上实现了最先进的结果。还进行了进一步的实验，以分析不同图表设计对我们方法性能的影响。

图形广泛用于建模数据的关系结构，并且图形机器学习（ML）的研究具有广泛的应用，从分子图中的药物设计到社交网络中的友谊建议。图形ML的流行方法通常需要大量的标记实例来实现令人满意的结果，这在现实世界中通常是不可行的，因为在图形上标记了新出现的概念的数据（例如，在图形上的新分类）是有限的。尽管已将元学习应用于不同的几个图形学习问题，但大多数现有的努力主要假设所有所见类别的数据都是金标记的，而当这些方法弱标记时，这些方法可能会失去疗效严重的标签噪声。因此，我们旨在研究一个新的问题，即弱监督图元学习，以改善知识转移的模型鲁棒性。为了实现这一目标，我们提出了一个新的图形学习框架 - 本文中的图形幻觉网络（Meta-GHN）。基于一种新的鲁棒性增强的情节训练，元研究将从弱标记的数据中幻觉清洁节点表示，并提取高度可转移的元知识，这使该模型能够快速适应不见了的任务，几乎没有标记的实例。广泛的实验表明，元基因与现有图形学习研究的优越性有关弱监督的少数弹性分类的任务。

公正的学习排名（ULTR）旨在从有偏见的用户点击日志中训练公正的排名模型。当前的大多数超级方法基于检查假设（EH），该假设假设可以将点击概率分解为两个标量函数，一种与排名特征有关，另一个与偏见因素有关。不幸的是，在实践中，特征，偏见因素和点击之间的相互作用很复杂，通常不能以这种独立的方式分解。使用EH拟合点击数据可能会导致模型错误指定并带来近似错误。在本文中，我们提出了一个基于向量的EH，并将点击概率作为两个向量函数的点产物提出。该解决方案由于其在拟合任意点击功能方面的普遍性而完成。基于它，我们提出了一个名为Vectorization的新型模型，以通过将嵌入在基础向量上投射到基础向量上，以适应性地学习相关性嵌入和排序文档。广泛的实验表明，我们的方法在复杂的真实点击以及简单的模拟点击上大大优于最新的超级方法。

灵感来自深度学习的广泛成功，已经提出了图表神经网络（GNNS）来学习表达节点表示，并在各种图形学习任务中表现出有希望的性能。然而，现有的努力主要集中在提供相对丰富的金色标记节点的传统半监督设置。虽然数据标签是难以忍受的事实令人生畏的事实并且需要强化领域知识，但特别是在考虑图形结构数据的异质性时，它通常是不切实际的。在几次半监督的环境下，大多数现有GNN的性能不可避免地受到过度装备和过天际问题的破坏，在很大程度上由于标记数据的短缺。在本文中，我们提出了一种配备有新型元学习算法的解耦的网络架构来解决这个问题。从本质上讲，我们的框架META-PN通过META学习的标签传播策略在未标记节点上乘坐高质量的伪标签，这有效增强了稀缺标记的数据，同时在培训期间启用大型接受领域。广泛的实验表明，与各种基准数据集上的现有技术相比，我们的方法提供了简单且实质性的性能。

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.