现代方法主要将车道检测视为像素细分的问题，该问题正在努力解决效率问题和诸如严重闭塞和极端照明条件之类的挑战性情况。受到人类感知的启发，在严重的阻塞和极端照明条件下对车道的认识主要基于上下文和全球信息。在这一观察结果的推动下，我们提出了一种针对超快速速度的新颖，简单而有效的配方，以及具有挑战性的场景问题。具体而言，我们将车道检测过程视为使用全局特征的锚定序列分类问题。首先，我们在一系列混合（行和列）锚点上代表具有稀疏坐标的车道。借助锚驱动的代表，我们随后将车道检测任务重新制定为序数分类问题，以获取车道的坐标。我们的方法可以通过锚驱动的表示可以大大降低计算成本。使用顺序分类公式的大型接受场特性，我们还可以处理具有挑战性的情况。在四个车道检测数据集上进行的广泛实验表明，我们的方法可以在速度和准确性方面达到最先进的性能。轻量级版本甚至可以每秒达到300帧（FPS）。我们的代码在https://github.com/cfzd/ultra-fast-lane-detection-v2上。

学习协作对于多机构增强学习（MARL）至关重要。以前的作品通过最大化代理行为的相关性来促进协作，该行为的相关性通常以不同形式的相互信息（MI）为特征。但是，我们揭示了次最佳的协作行为，也出现了强烈的相关性，并且简单地最大化MI可以阻碍学习的学习能力。为了解决这个问题，我们提出了一个新颖的MARL框架，称为“渐进式信息协作（PMIC）”，以进行更有效的MI驱动协作。 PMIC使用全球国家和联合行动之间MI测量的新协作标准。基于此标准，PMIC的关键思想是最大程度地提高与优越的协作行为相关的MI，并最大程度地减少与下等方面相关的MI。这两个MI目标通过促进更好的合作，同时避免陷入次级优势，从而扮演互补的角色。与其他算法相比，在各种MARL基准测试的实验表明，PMIC的表现出色。

由于其在不同领域的应用继续扩大和多样化，因此机器学习的公平正在越来越越来越受到关注。为了减轻不同人口组之间的区分模型行为，我们介绍了一种新的后处理方法来通过组感知阈值适应优化多个公平性约束。我们建议通过优化从分类模型输出的概率分布估计的混淆矩阵来学习每个人口统计组的自适应分类阈值。由于我们仅需要模型输出的估计概率分布而不是分类模型结构，我们的后处理模型可以应用于各种分类模型，并以模型 - 不可知方式提高公平性并确保隐私。这甚至允许我们在后处理现有的公平方法，以进一步提高准确性和公平性之间的权衡。此外，我们的模型具有低计算成本。我们为我们的优化算法的收敛性提供严格的理论分析和我们方法的准确性和公平性之间的权衡。我们的方法理论上使得能够在与现有方法相同的情况下的近最优性的更好的上限。实验结果表明，我们的方法优于最先进的方法，并获得最接近理论精度公平折衷边界的结果。

本文考虑了具有一般非线性约束的随机线性匪徒。目标是通过每轮$ \ Tau \ Leq T $的一组限制来最大化预期的累计奖励。我们提出了一种悲观的乐观乐观算法，其在两个方面有效。首先，算法产生$ \ tilde {\ cal o} \ left（\ left（\ frac {k ^ {0.75}} {\ delta}} {\ delta} + d \ over）\ sqrt {\ tau} \右）$（伪）在圆形$ \ tau \ leq t，$ k $的遗憾，$ k $是约束的数量，$ d $是奖励功能空间的尺寸，$ \ delta $ in是slater的常数;在任何圆形$ \ tau> \ tau'中的零限制违规，$ \ tau' $独立于地平线$ t. $ the $秒，算法是计算效率的。我们的算法基于优化中的原始方法，包括两个组件。原始分量类似于无约束的随机线性匪徒（我们的算法使用线性上置信界限算法（Linucb））。双组分的计算复杂性取决于约束的数量，而是与上下文空间，动作空间和特征空间的大小无关。因此，我们的算法的整体计算复杂性类似于线性UCB的线性UCB，用于无约束随机线性匪徒。

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.