光学相干断层扫描（OCT）对指纹成像的技术为捕获皮肤层深度信息的能力而为指纹识别开辟了新的研究潜力。如果可以充分利用深度信息，则可以开发健壮和高安全性自动指纹识别系统（AFRSS）。然而，在现有的研究中，基于深度信息的表现攻击检测（PAD）和地下指纹重建被视为两个独立的分支，从而导致AFRS构建的高计算和复杂性。因此，本文提出了一个基于OCT的统一表示模型指纹垫和地下指纹重建。首先，我们设计了一个新型的语义分割网络，该网络仅通过基于OCT的指纹的真实手指切片训练，以从这些切片（也称为B扫描）中提取多个地下结构。从网络中得出的潜在代码直接用于有效检测PA，因为它们包含丰富的地下生物学信息，该信息与PA材料独立，并且对未知PA具有强大的鲁棒性。同时，采用了分段的地下结构来重建多个地下2D指纹。通过使用基于传统2D指纹的现有成熟技术，可以轻松实现识别。广泛的实验是在我们自己已建立的数据库上进行的，该数据库是最大的基于OCT的指纹数据库，具有2449卷。在PAD任务中，我们的方法可以从最先进的方法中提高0.33％的ACC。对于重建性能，我们的方法以0.834 miou和0.937 pa的形式达到了最佳性能。通过与表面2D指纹的识别性能进行比较，我们提出的方法对高质量地下指纹重建的有效性得到了进一步证明。

呈现攻击检测（PAD）方法的稳健性和泛化能力至关重要，以确保面部识别系统（FRSS）的安全性。但是，在真实的场景中，呈现攻击（PAS）是各种各样的且难以收集的。现有焊盘方法高度依赖于有限的训练集，并且不能概括到未知的PAS。与PAD任务不同，可以有效地采用其他与真实面（例如面部识别和属性编辑）训练的其他与面部识别和属性编辑）培训的其他相关任务。灵感来自于此，我们建议从其他与面部相关的任务应用任务（任务分类）来解决面板，以改善检测PAS的泛化能力。所提出的方法，首先从其他面部相关任务引入任务特定功能，然后，我们使用曲线图注意网络（GAT）来设计跨模型适配器来重新绘制此类功能以适应焊盘任务。最后，通过使用基于CNN的PA检测器和重新映射特征的分层特征来实现面板。实验结果表明，与最先进的方法相比，该方法可以在复杂和混合数据集中实现显着的改进。特别是，当使用Oulu-NPU，Casia-Fasd和IDIAP重放攻击训练时，我们在MSU-MFSD中获得了5.48％的HTET（半总错误率），优于基准7.39％。代码将公开可用。

由于攻击材料的多样性，指纹识别系统（AFRSS）容易受到恶意攻击的影响。为AFRSS的安全性和可靠性提出有效的指纹介绍攻击检测（PAD）方法是非常重要的。然而，当前焊盘方法通常在新攻击材料或传感器设置下具有差的鲁棒性。因此，本文通过考虑处理先前作品中忽略的冗余“噪声”信息，提出了一种新的通道 - 方向特征去噪焊盘（CFD-PAD）方法。所提出的方法通过加权每个信道的重要性并找到这些鉴别性信道和“噪声”通道来学习指纹图像的重要特征。然后，在特征图中抑制了“噪声”通道的传播以减少干扰。具体地，设计了PA-Adaption损耗来限制特征分布，以使实时指纹的特征分布更具聚合和欺骗指纹更多的分散。我们在Livdet 2017上评估的实验结果表明，当假检出率等于1.0％（TDR @FDR = 1％）时，我们所提出的CFD-PAD可以达到2.53％的ace和93.83％的真实检测率，并且优于基于最佳的单一模型在ACE（2.53％与4.56％）和TDR @FDR方面的方法明显显着（93.83％，93.83％\％），这证明了该方法的有效性。虽然我们已经实现了与最先进的基于多模型的方法相比的可比结果，但是通过我们的方法仍然可以实现TDR @ FDR增加到91.19％的1％至93.83％。此外，与基于多模型的多模型的方法相比，我们的模型更简单，更轻，更高效，更高效地实现了74.76％的耗时减少。代码将公开。

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.