Although many studies have successfully applied transfer learning to medical image segmentation, very few of them have investigated the selection strategy when multiple source tasks are available for transfer. In this paper, we propose a prior knowledge guided and transferability based framework to select the best source tasks among a collection of brain image segmentation tasks, to improve the transfer learning performance on the given target task. The framework consists of modality analysis, RoI (region of interest) analysis, and transferability estimation, such that the source task selection can be refined step by step. Specifically, we adapt the state-of-the-art analytical transferability estimation metrics to medical image segmentation tasks and further show that their performance can be significantly boosted by filtering candidate source tasks based on modality and RoI characteristics. Our experiments on brain matter, brain tumor, and white matter hyperintensities segmentation datasets reveal that transferring from different tasks under the same modality is often more successful than transferring from the same task under different modalities. Furthermore, within the same modality, transferring from the source task that has stronger RoI shape similarity with the target task can significantly improve the final transfer performance. And such similarity can be captured using the Structural Similarity index in the label space.

The accurate detection and grasping of transparent objects are challenging but of significance to robots. Here, a visual-tactile fusion framework for transparent object grasping under complex backgrounds and variant light conditions is proposed, including the grasping position detection, tactile calibration, and visual-tactile fusion based classification. First, a multi-scene synthetic grasping dataset generation method with a Gaussian distribution based data annotation is proposed. Besides, a novel grasping network named TGCNN is proposed for grasping position detection, showing good results in both synthetic and real scenes. In tactile calibration, inspired by human grasping, a fully convolutional network based tactile feature extraction method and a central location based adaptive grasping strategy are designed, improving the success rate by 36.7% compared to direct grasping. Furthermore, a visual-tactile fusion method is proposed for transparent objects classification, which improves the classification accuracy by 34%. The proposed framework synergizes the advantages of vision and touch, and greatly improves the grasping efficiency of transparent objects.

我们提出了两个新颖的可传递性指标F-OTCE（基于快速最佳运输的条件熵）和JC-otce（联合通信OTCE），以评估源模型（任务）可以使目标任务的学习受益多少，并学习更可转移的表示形式。用于跨域交叉任务转移学习。与需要评估辅助任务的经验可转让性的现有指标不同，我们的指标是无辅助的，以便可以更有效地计算它们。具体而言，F-otce通过首先求解源和目标分布之间的最佳传输（OT）问题来估计可转移性，然后使用最佳耦合来计算源和目标标签之间的负条件熵。它还可以用作损失函数，以最大化目标任务填充源模型的可传递性。同时，JC-OTCE通过在OT问题中包含标签距离来提高F-otce的可转移性鲁棒性，尽管它可能会产生额外的计算成本。广泛的实验表明，F-otce和JC-otce优于最先进的无辅助指标，分别为18.85％和28.88％，与基础真相转移精度相关系数。通过消除辅助任务的训练成本，两个指标将前一个方法的总计算时间从43分钟减少到9.32s和10.78，用于一对任务。当用作损失函数时，F-otce在几个射击分类实验中显示出源模型的传输精度的一致性提高，精度增益高达4.41％。

先前的工作提出了几种策略，以降低自我发挥机制的计算成本。这些作品中的许多作品都考虑将自我关注程序分解为区域和局部特征提取程序，这些程序都会产生较小的计算复杂性。但是，区域信息通常仅以损失的不良信息为代价，原因是由于下采样而丢失。在本文中，我们提出了一种新颖的变压器体系结构，旨在减轻成本问题，称为双视觉变压器（双击）。新的体系结构结合了一个关键的语义途径，可以更有效地将代币向量压缩到具有降低的复杂性顺序的全球语义中。然后，这种压缩的全局语义是通过另一个构造的像素途径在学习更精细的像素级详细信息中作为有用的先前信息。然后将语义途径和像素途径集成在一起并进行联合训练，从而通过这两个途径并行传播增强的自我运动信息。此后，双攻击能够降低计算复杂性，而不会损害很大的准确性。我们从经验上证明，双重射击比SOTA变压器体系结构具有较高的训练复杂性。源代码可在\ url {https://github.com/yehli/imagenetmodel}中获得。

视频内容是多方面的，由对象，场景，交互或操作组成。现有数据集主要标记为模型培训的一个方面，导致视频表示根据训练数据集仅偏置为一个小平面。目前还没有研究如何学习来自多方面标签的视频表示，以及多方面的信息是否有助于视频表示学习。在本文中，我们提出了一种新的学习框架，多朝向集成（MUFI），以聚合来自不同数据集的面部，以学习可以反映视频内容的全频谱的表示。从技术上讲，MUFI将问题交流为视觉语义嵌入学习，该问题将视频表示映射到丰富的语义嵌入空间中，并从两个角度联合优化视频表示。一个是利用每个视频和自己的标签描述之间的小型内部监督，第二个是从其他数据集的小平面预测每个视频的“语义表示”作为刻面监控。广泛的实验表明，通过我们的MUFI框架在四个大型视频数据集加上两个图像数据集的联盟上学习3D CNN，导致视频表示的优异能力。具有MUFI的预先学习的3D CNN还显示出在几个下游视频应用上的其他方法的清晰改进。更值得注意的是，MUFI在UCF101 / HMDB51上实现98.1％/ 80.9％，用于行动识别和101.5％，在MSVD上的浏览器D得分为视频字幕。

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.