We present a novel camera path optimization framework for the task of online video stabilization. Typically, a stabilization pipeline consists of three steps: motion estimating, path smoothing, and novel view rendering. Most previous methods concentrate on motion estimation, proposing various global or local motion models. In contrast, path optimization receives relatively less attention, especially in the important online setting, where no future frames are available. In this work, we adopt recent off-the-shelf high-quality deep motion models for the motion estimation to recover the camera trajectory and focus on the latter two steps. Our network takes a short 2D camera path in a sliding window as input and outputs the stabilizing warp field of the last frame in the window, which warps the coming frame to its stabilized position. A hybrid loss is well-defined to constrain the spatial and temporal consistency. In addition, we build a motion dataset that contains stable and unstable motion pairs for the training. Extensive experiments demonstrate that our approach significantly outperforms state-of-the-art online methods both qualitatively and quantitatively and achieves comparable performance to offline methods.

姿势估计准确性的提高是目前移动机器人中的基本问题。本研究旨在改善观察的使用以提高准确性。选择要点的选择会影响姿势估计的准确性，导致观察贡献如何影响系统的问题。因此，分析了信息对姿势估计过程的贡献。此外，配制了不确定性模型，灵敏度模型和贡献理论，提供了一种计算每种残留项的贡献的方法。所提出的选择方法已经理解证明能够实现全局统计最优。所提出的方法在人工数据模拟上进行测试，与基特基准进行比较。该实验揭示了与Aloam和Mloam对比的优异结果。所提出的算法在LIDAR Idomatry和LIDAR惯性内径术中使用不同的LIDAR传感器，使用不同的扫描模式，展示其提高姿态估计精度的有效性。随后推断出两个激光扫描传感器的新配置。该配置对于先前地图中的三维姿态定位是有效的，并且产生厘米级的结果。

许多过去的作品旨在通过监督特征重要性（通过模型解释技术估算）通过人类注释（例如重要图像区域的亮点）来改善模型中的视觉推理。但是，最近的工作表明，即使在随机的监督下，对视觉问题答案（VQA）任务的特征重要性（FI）监督的绩效收益也会持续下去，这表明这些方法不会有意义地将模型FI与人类FI保持一致。在本文中，我们表明模型FI监督可以有意义地提高VQA模型的准确性，并通过优化四个关键模型目标来提高几个正确的右季节（RRR）指标的性能：（1）给出的准确预测有限。但是足够的信息（足够）； （2）没有重要信息（不确定性）的最大 - 凝集预测； （3）预测不重要的特征变化（不变性）的不变性； （4）模型FI解释与人类FI解释（合理性）之间的对齐。我们的最佳性能方法，视觉功能重要性监督（Visfis），就分布和分布的精度而言，在基准VQA数据集上优于基准VQA数据集的强大基准。尽管过去的工作表明，提高准确性的机制是通过改善解释的合理性，但我们表明这种关系取决于忠诚的解释（解释是否真的代表了模型的内部推理）。当解释是合理的和忠实的，而不是当它们是合理而不是忠实的时候，预测更为准确。最后，我们表明，令人惊讶的是，在控制模型的分布精度时，RRR指标不能预测分布模型的准确性，这使这些指标的价值质疑评估模型推理的价值。所有支持代码均可在https://github.com/zfying/disfis上获得

对象导航任务要求代理根据视觉信息在未知环境中找到特定对象。以前，图形卷积被用于隐式探索对象之间的关系。但是，由于对象之间可见性的差异，很容易在对象注意中产生偏见。因此，在本文中，我们提出了一个定向的对象注意（DOA）图，以指导代理显式地学习对象之间的注意力关系，从而减少对象的注意偏置。特别是，我们使用DOA图在原始图像上分别对对象特征和无偏的自适应图像注意（UAIA）进行无偏的自适应对象注意（UAOA）。为了区分不同分支的特征，提出了一种简洁的自适应分支分布（ABED）方法。我们在AI2-数据集上评估我们的方法。与最先进的方法（SOTA）方法相比，我们的方法报告了7.4％，8.1％和17.6％的成功率（SR），成功按路径长度（SPL）加权（SPL）并通过动作效率加权成功（SAE） ）， 分别。

变压器最近在各种视觉任务上表现出卓越的性能。大型有时甚至全球，接收领域赋予变换器模型，并通过其CNN对应物具有更高的表示功率。然而，简单地扩大接收领域也产生了几个问题。一方面，使用致密的注意，例如，在VIT中，导致过度的记忆和计算成本，并且特征可以受到超出兴趣区域的无关紧要的影响。另一方面，PVT或SWIN变压器采用的稀疏注意是数据不可知论，可能会限制模拟长距离关系的能力。为了缓解这些问题，我们提出了一种新型可变形的自我关注模块，其中以数据相关的方式选择密钥和值对中的密钥和值对的位置。这种灵活的方案使自我关注模块能够专注于相关区域并捕获更多的信息性功能。在此基础上，我们呈现可变形的关注变压器，一般骨干模型，具有可变形关注的图像分类和密集预测任务。广泛的实验表明，我们的模型在综合基准上实现了一致的改善结果。代码可在https://github.com/leaplabthu/dat上获得。

视觉变压器（VITS）已成为各种视觉任务的流行结构和优于卷积神经网络（CNNS）。然而，这种强大的变形金机带来了巨大的计算负担。而这背后的基本障碍是排气的令牌到令牌比较。为了缓解这一点，我们深入研究Vit的模型属性，观察到VITS表现出稀疏关注，具有高令牌相似性。这直观地向我们介绍了可行的结构不可知的尺寸，令牌编号，以降低计算成本。基于这一探索，我们为香草vits提出了一种通用的自我切片学习方法，即坐下。具体而言，我们首先设计一种新颖的令牌减肥模块（TSM），可以通过动态令牌聚集来提高VIT的推理效率。不同于令牌硬滴，我们的TSM轻轻地集成了冗余令牌变成了更少的信息，可以在不切断图像中的鉴别性令牌关系的情况下动态缩放视觉注意。此外，我们介绍了一种简洁的密集知识蒸馏（DKD）框架，其密集地以柔性自动编码器方式传送无组织的令牌信息。由于教师和学生之间的结构类似，我们的框架可以有效地利用结构知识以获得更好的收敛性。最后，我们进行了广泛的实验来评估我们的坐姿。它展示了我们的方法可以通过1.7倍加速VITS，其精度下降可忽略不计，甚至在3.6倍上加速VITS，同时保持其性能的97％。令人惊讶的是，通过简单地武装LV-VIT与我们的坐线，我们在想象中实现了新的最先进的表现，超过了最近文学中的所有CNN和VITS。

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.