对于黑盒攻击，替代模型和受害者模型之间的差距通常很大，这表现为弱攻击性能。通过观察到，可以通过同时攻击多样的模型来提高对抗性示例的可传递性，并提出模型增强方法，这些模型通过使用转换图像模拟不同的模型。但是，空间域的现有转换不会转化为显着多样化的增强模型。为了解决这个问题，我们提出了一种新型的频谱模拟攻击，以针对正常训练和防御模型制作更容易转移的对抗性例子。具体而言，我们将频谱转换应用于输入，从而在频域中执行模型增强。从理论上讲，我们证明了从频域中得出的转换导致不同的频谱显着图，这是我们提出的指标，以反映替代模型的多样性。值得注意的是，我们的方法通常可以与现有攻击结合使用。 Imagenet数据集的广泛实验证明了我们方法的有效性，\ textit {e.g。}，攻击了九个最先进的防御模型，其平均成功率为\ textbf {95.4 \％}。我们的代码可在\ url {https://github.com/yuyang-long/ssa}中获得。

我们专注于在不同情况下在车道检测中桥接域差异，以大大降低自动驾驶的额外注释和重新训练成本。关键因素阻碍了跨域车道检测的性能改善，即常规方法仅着眼于像素损失，同时忽略了泳道的形状和位置验证阶段。为了解决该问题，我们提出了多级域Adaptation（MLDA）框架，这是一种在三个互补语义级别的像素，实例和类别的互补语义级别处理跨域车道检测的新观点。具体而言，在像素级别上，我们建议在自我训练中应用跨级置信度限制，以应对车道和背景的不平衡置信分布。在实例层面上，我们超越像素，将分段车道视为实例，并通过三胞胎学习促进目标域中的判别特征，这有效地重建了车道的语义环境，并有助于减轻特征混乱。在类别级别，我们提出了一个自适应域间嵌入模块，以在自适应过程中利用泳道的先验位置。在两个具有挑战性的数据集（即Tusimple和Culane）中，我们的方法将车道检测性能提高了很大的利润率，与先进的领域适应算法相比，精度分别提高了8.8％和F1级的7.4％。

模糊文物可以严重降低图像的视觉质量，并且已经提出了许多用于特定场景的脱模方法。然而，在大多数现实世界的图像中，模糊是由不同因素引起的，例如运动和散焦。在本文中，我们解决了不同的去纹身方法如何在一般类型的模糊上进行。对于深入的性能评估，我们构建一个名为（MC-Blur）的新型大规模的多个原因图像去孔数据集，包括现实世界和合成模糊图像，具有模糊的混合因素。采用不同的技术收集所提出的MC-Blur数据集中的图像：卷积超高清（UHD）具有大核的锐利图像，平均由1000 FPS高速摄像头捕获的清晰图像，向图像添加Defocus，而且真实-world模糊的图像由各种相机型号捕获。这些结果概述了当前的去纹理方法的优缺点。此外，我们提出了一种新的基线模型，适应多种模糊的原因。通过包括对不同程度的特征的不同重量，所提出的网络导出更强大的特征，重量分配给更重要的水平，从而增强了特征表示。新数据集上的广泛实验结果展示了多原因模糊情景所提出的模型的有效性。

当使用星座协同作用来对较大的区域进行侦察成像时，需要以最少的观察资源消耗来达到覆盖能力要求，以获得最佳的星座观测方案。本文以最少数量的卫星数量作为优化目标满足实时地面覆盖要求，提出了对卫星星座配置的优化设计，以通过使用改进的模拟退火算法结合实际覆盖大型区域成像。 - 六边形离散化的时间覆盖评估方法。该算法可以适应实验条件，具有良好的效率，并且可以满足工业准确性要求。在模拟应用中测试了算法的有效性和适应性。

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.

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.