Explaining machine learning models is an important and increasingly popular area of research interest. The Shapley value from game theory has been proposed as a prime approach to compute feature importance towards model predictions on images, text, tabular data, and recently graph neural networks (GNNs) on graphs. In this work, we revisit the appropriateness of the Shapley value for GNN explanation, where the task is to identify the most important subgraph and constituent nodes for GNN predictions. We claim that the Shapley value is a non-ideal choice for graph data because it is by definition not structure-aware. We propose a Graph Structure-aware eXplanation (GStarX) method to leverage the critical graph structure information to improve the explanation. Specifically, we define a scoring function based on a new structure-aware value from the cooperative game theory proposed by Hamiache and Navarro (HN). When used to score node importance, the HN value utilizes graph structures to attribute cooperation surplus between neighbor nodes, resembling message passing in GNNs, so that node importance scores reflect not only the node feature importance, but also the node structural roles. We demonstrate that GStarX produces qualitatively more intuitive explanations, and quantitatively improves explanation fidelity over strong baselines on chemical graph property prediction and text graph sentiment classification.

近年来，提出了基于培训数据中毒的许多后门攻击。然而，在实践中，这些后门攻击容易受到图像压缩的影响。当压缩后门实例时，将销毁特定后门触发器的特征，这可能导致后门攻击性能恶化。在本文中，我们提出了一种基于特征一致性培训的压缩后门攻击。据我们所知，这是第一个对图像压缩强大的后门攻击。首先，将返回码图像及其压缩版本输入深神经网络（DNN）进行培训。然后，通过DNN的内部层提取每个图像的特征。接下来，最小化后门图像和其压缩版本之间的特征差异。结果，DNN将压缩图像的特征视为特征空间中的后门图像的特征。培训后，对抗DNN的后门攻击是对图像压缩的强大。此外，我们考虑了三种不同的图像按压（即，JPEG，JPEG2000，WEBP），使得后门攻击对多个图像压缩算法具有鲁棒性。实验结果表明了拟议的后门攻击的有效性和稳健性。当后门实例被压缩时，常见后攻击攻击的攻击成功率低于10％，而我们压缩后门的攻击成功率大于97％。即使在低压缩质量压缩后，压缩攻击也仍然是坚固的。此外，广泛的实验表明，我们的压缩后卫攻击具有抗拒未在训练过程中使用的图像压缩的泛化能力。

基于经典的细粒度测量的IP地理位置算法通常依赖于某些特定的线性延迟距离规则。这可能导致延迟距离关系非线性的实际网络环境中的不可靠的地理位置。最近，研究人员开始注意基于学习的IP地理位置算法。这些数据驱动算法利用多层Perceptron（MLP）来模拟网络环境。它们不需要关于线性延迟距离规则的强烈假设，并且能够学习非线性关系。理论上，他们应该提高不同网络的IP地理定位的泛化能力。但是，网络从根本上表示为图。 MLP不适合模拟以图形所结构的信息。基于MLP的IP地理位置方法将目标IP地址视为孤立的数据实例，忽略目标之间的连接信息。这将导致次优表示和限制地理定位性能。图表卷积网络（GCN）是一种用于图形数据呈现的新兴深度学习方法。在这项工作中，我们研究如何使用GCN模拟计算机网络进行细粒度IP地理位置。首先，我们将IP地理位置任务制定为归属图节点回归问题。然后，提出了名为GCN-Geo的基于GCN的IP地理位置框架以预测每个IP地址的位置。最后，在三个现实世界数据集（纽约州，香港和上海）的实验结果表明，拟议的GCN-Geo框架明显优于平均误差距离的最先进的规则和基于学习的基础基础，误差距离和最大误差距离。这验证了GCN在细粒度IP地理位置中的潜力。

鉴于在现实世界应用中大规模图的流行率，训练神经模型的存储和时间引起了人们的关注。为了减轻关注点，我们提出和研究图形神经网络（GNNS）的图形凝结问题。具体而言，我们旨在将大型原始图凝结成一个小的，合成的和高度信息的图，以便在小图和大图上训练的GNN具有可比性的性能。我们通过优化梯度匹配损失并设计一种凝结节点期货和结构信息的策略来模仿原始图上的GNN训练轨迹，以解决凝结问题。广泛的实验证明了所提出的框架在将不同的图形数据集凝结成信息较小的较小图中的有效性。特别是，我们能够在REDDIT上近似于95.3％的原始测试准确性，Flickr的99.8％和CiteSeer的99.0％，同时将其图形尺寸降低了99.9％以上，并且可以使用冷凝图来训练各种GNN架构Code在https://github.com/chandlerbang/gcond上发布。

微观场景上的对象检测是一项流行的任务。由于显微镜始终具有可变的宏伟速度，因此对象的规模可能有很大差异，从而负担探测器的优化。此外，相机聚焦的不同情况会带来模糊的图像，这导致了区分物体和背景之间边界的巨大挑战。为了解决上述两个问题，我们提供了有关Chula-Parasiteegg-11数据集的一系列有用的培训策略和广泛的实验，在ICIP 2022挑战上带来不可忽略的结果：微观图像中的寄生卵检测和分类新的盒子选择策略和改进的多模型合奏的融合方法，因此，我们的方法赢得了第一名（MIOU 95.28％，MF1Score 99.62％），这也是Chula-的最新方法Parasiteegg-11数据集。

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.