我们表明，在固定级和对称的阳性半明确矩阵上，Riemannian梯度下降算法几乎可以肯定地逃脱了歧管边界上的一些虚假关键点。我们的结果是第一个部分克服低级基质歧管的不完整而不改变香草riemannian梯度下降算法的不完整性。虚假的关键点是一些缺陷的矩阵，仅捕获地面真理的特征成分的一部分。与经典的严格鞍点不同，它们表现出非常奇异的行为。我们表明，使用动力学低级别近似和重新升级的梯度流，可以将某些伪造的临界点转换为参数化域中的经典严格鞍点，从而导致所需的结果。提供数值实验以支持我们的理论发现。

光学相干断层扫描（OCT）有助于眼科医生评估黄斑水肿，流体的积累以及微观分辨率的病变。视网膜流体的定量对于OCT引导的治疗管理是必需的，这取决于精确的图像分割步骤。由于对视网膜流体的手动分析是一项耗时，主观和容易出错的任务，因此对快速和健壮的自动解决方案的需求增加了。在这项研究中，提出了一种名为Retifluidnet的新型卷积神经结构，用于多级视网膜流体分割。该模型受益于层次表示使用新的自适应双重注意（SDA）模块的纹理，上下文和边缘特征的学习，多个基于自适应的Skip Connections（SASC）以及一种新颖的多尺度深度自我监督学习（DSL）方案。拟议的SDA模块中的注意机制使该模型能够自动提取不同级别的变形感知表示，并且引入的SASC路径进一步考虑了空间通道相互依存，以串联编码器和解码器单元，从而提高了表示能力。还使用包含加权版本的骰子重叠和基于边缘的连接损失的联合损失函数进行了优化的retifluidnet，其中将多尺度局部损失的几个分层阶段集成到优化过程中。该模型根据三个公开可用数据集进行验证：润饰，Optima和Duke，并与几个基线进行了比较。数据集的实验结果证明了在视网膜OCT分割中提出的模型的有效性，并揭示了建议的方法比现有的最新流体分割算法更有效，以适应各种图像扫描仪器记录的视网膜OCT扫描。

新颖的类发现（NCD）的目的是在一个未标记的数据集中推断出新的类别，该数据集利用了包含不相交但相关类别的标签集的先验知识。现有的研究主要侧重于利用方法学层面的标签集，而不太强调标记集合本身的分析。因此，在本文中，我们从标记的集合中重新考虑了小说类发现，并关注两个核心问题：（i）给定特定的未标记集，什么样的标签集可以最好地支持新颖的类发现？ （ii）NCD的基本前提是标记的集合必须与未标记的集合有关，但是我们如何衡量这种关系？对于（i），我们提出并证实了这样的假设，即NCD可以从具有与未标记集的标签相似性的标签集中受益更多。具体而言，我们通过利用其层次结构结构来建立一个广泛而大规模的基准，在Imagenet上标记/未标记的数据集之间具有不同程度的语义相似性。作为鲜明的对比，现有的NCD基准是根据具有不同类别和图像的标签集开发的，并且完全忽略了语义关系。对于（ii），我们引入了一个数学定义，用于量化标记和未标记集之间的语义相似性。此外，我们使用此指标来确认我们提出的基准测试的有效性，并证明它与NCD性能高度相关。此外，在没有定量分析的情况下，以前的工作通常认为标签信息总是有益的。但是，违反直觉，我们的实验结果表明，使用标签可能会导致低相似性设置中的次级优势。

无示例性课程学习（CIL）是一个具有挑战性的问题，因为严格禁止从先前阶段进行排练数据，从而导致灾难性忘记深度神经网络（DNNS）。在本文中，我们提出了ivoro，这是CIL的整体框架，源自计算几何形状。我们发现Voronoi图（VD）是一个用于空间细分的经典模型，对于解决CIL问题特别有力，因为VD本身可以以增量的方式构建好 - 新添加的站点（类）只会影响接近的类别，使非连续课程几乎无法忘记。此外，为了找到用于VD构建的更好的中心，我们使用功率图与VD串联DNN，并证明可以通过使用除法和争议算法集成本地DNN模型来优化VD结构。此外，我们的VD结构不限于深度特征空间，而是适用于多个中间特征空间，将VD推广为多中心VD（CIVD），可有效捕获DNN的多元元素特征。重要的是，Ivoro还能够处理不确定性感知的测试时间Voronoi细胞分配，并且在几何不确定性和预测精度之间表现出很高的相关性（高达〜0.9）。与最先进的非exemememplar CIL方法相比，Ivoro将所有内容汇总在一起，分别在CIFAR-100，Tinyimagenet和Imagenet-Subsset上获得了高达25.26％，37.09％和33.21％的改善。总之，Ivoro可以实现高度准确，保护隐私和几何解释的CIL，当禁止使用跨相数据共享时，这特别有用，例如在医疗应用中。我们的代码可在https://machunwei.github.io/ivoro上找到。

低功率边缘-AI功能对于支持元视野的设备扩展现实（XR）应用至关重要。在这项工作中，我们研究了两个代表性的XR工作负载：（i）手动检测和（ii）眼睛分割，用于硬件设计空间探索。对于这两种应用，我们都会训练深层神经网络，并分析量化和硬件特定瓶颈的影响。通过模拟，我们评估了CPU和两个收缩推理加速器实现。接下来，我们将这些硬件解决方案与先进的技术节点进行比较。评估了将最新的新兴非易失性记忆技术（STT/SOT/VGSOT MRAM）集成到XR-AI推论管道中的影响。我们发现，可以通过在7nm节点的设计中引入非挥发性记忆来实现手部检测（IPS = 40）和眼部分割（IPS = 6）的显着能源益处（IPS = 40）（IPS = 6）。 （推断每秒）。此外，由于MRAM与传统的SRAM相比，由于MRAM的较小形式，我们可以大大减少面积（> = 30％）。

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.