选择第一次到达的Prestack收集时间被称为首次到达时间（FAT）采摘，这是地震数据处理中必不可少的一步，并且主要是手动解决的。随着当前地震数据收集密度的增加，手动采摘效率无法满足实际需求。因此，近几十年来，自动采摘方法已经大大开发出来，尤其是基于深度学习的方法。但是，当前有监督的基于深度学习的方法很少可以避免对标记样品的依赖。此外，由于收集数据是一组与自然图像大不相同的信号，因此当前方法在低信号与噪声比（SNR）的情况下很难解决脂肪拾取问题。在本文中，对于Hard Rock地震收集数据，我们提出了一个多阶段分割拾取网络（MSSPN），该网络解决了跨工作地点的概括问题以及在低SNR的情况下的采摘问题。在MSSPN中，有四个子模型可以模拟手动采摘处理，从而将其假定为从粗糙到细的四个阶段。具有不同质量的七个现场数据集的实验表明，我们的MSSPN的表现优于大幅度的基准。尤其是，在中等和高snrs的情况下，我们的方法可以实现超过90 \％的精确拾取，甚至精细模型也可以使用低SNR实现88 \％精确的数据集。

Pansharpening是一种广泛使用的图像增强技术，用于遥感。其原理是熔断输入的高分辨率单通道平面（PAN）图像和低分辨率多光谱图像，并获得高分辨率多光谱（HRMS）图像。现有的深度学习泛散歌方法有两个缺点。首先，需要沿信道维度连接两个输入图像的特征以重建HRMS图像，这使得PAN图像的重要性不突出，并且还导致高计算成本。其次，通过手动设计的损耗功能难以提取特征的隐式信息。为此，我们通过用于粉彩的快速引导滤波器（FGF）提出一种生成的对抗性网络。在发电机中，传统的信道级联被FGF替换，以更好地保留空间信息，同时减少参数的数量。同时，融合对象可以通过空间注意模块突出显示。此外，通过对抗性训练可以有效地保存特征的潜在信息。许多实验说明我们的网络生成了可以超越现有方法的高质量HRMS图像，以及更少的参数。

We present a novel neural surface reconstruction method called NeuralRoom for reconstructing room-sized indoor scenes directly from a set of 2D images. Recently, implicit neural representations have become a promising way to reconstruct surfaces from multiview images due to their high-quality results and simplicity. However, implicit neural representations usually cannot reconstruct indoor scenes well because they suffer severe shape-radiance ambiguity. We assume that the indoor scene consists of texture-rich and flat texture-less regions. In texture-rich regions, the multiview stereo can obtain accurate results. In the flat area, normal estimation networks usually obtain a good normal estimation. Based on the above observations, we reduce the possible spatial variation range of implicit neural surfaces by reliable geometric priors to alleviate shape-radiance ambiguity. Specifically, we use multiview stereo results to limit the NeuralRoom optimization space and then use reliable geometric priors to guide NeuralRoom training. Then the NeuralRoom would produce a neural scene representation that can render an image consistent with the input training images. In addition, we propose a smoothing method called perturbation-residual restrictions to improve the accuracy and completeness of the flat region, which assumes that the sampling points in a local surface should have the same normal and similar distance to the observation center. Experiments on the ScanNet dataset show that our method can reconstruct the texture-less area of indoor scenes while maintaining the accuracy of detail. We also apply NeuralRoom to more advanced multiview reconstruction algorithms and significantly improve their reconstruction quality.

注释大规模数据集以进行监督的视频阴影检测方法是一项挑战。直接使用在标记的图像上训练的模型直接导致高概括错误和时间不一致的结果。在本文中，我们通过提出一个时空插值一致性训练（Stict）框架来解决这些挑战，以合理地将未标记的视频框架以及标记的图像以及图像阴影检测网络训练中进行合理地馈送。具体而言，我们提出了空间和时间ICT，其中定义了两个新的插值方案，\ textit {i.e。}，空间插值和时间插值。然后，我们相应地得出了相应的空间和时间插值一致性约束，以增强像素智能分类任务中的概括和分别鼓励时间一致的预测。此外，我们设计了一个量表感知网络，用于图像中的多尺度阴影知识学习，并提出了比例一致性约束，以最大程度地减少不同尺度上预测之间的差异。我们提出的方法在VISHA数据集和自称数据集上得到了广泛的验证。实验结果表明，即使没有视频标签，我们的方法也比大多数最新的监督，半监督或无监督的图像/视频阴影检测方法以及相关任务中的其他方法更好。代码和数据集可在\ url {https://github.com/yihong-97/stict}上获得。

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.