Cone beam computed tomography (CBCT) has been widely used in clinical practice, especially in dental clinics, while the radiation dose of X-rays when capturing has been a long concern in CBCT imaging. Several research works have been proposed to reconstruct high-quality CBCT images from sparse-view 2D projections, but the current state-of-the-arts suffer from artifacts and the lack of fine details. In this paper, we propose SNAF for sparse-view CBCT reconstruction by learning the neural attenuation fields, where we have invented a novel view augmentation strategy to overcome the challenges introduced by insufficient data from sparse input views. Our approach achieves superior performance in terms of high reconstruction quality (30+ PSNR) with only 20 input views (25 times fewer than clinical collections), which outperforms the state-of-the-arts. We have further conducted comprehensive experiments and ablation analysis to validate the effectiveness of our approach.

We propose an analysis in fair learning that preserves the utility of the data while reducing prediction disparities under the criteria of group sufficiency. We focus on the scenario where the data contains multiple or even many subgroups, each with limited number of samples. As a result, we present a principled method for learning a fair predictor for all subgroups via formulating it as a bilevel objective. Specifically, the subgroup specific predictors are learned in the lower-level through a small amount of data and the fair predictor. In the upper-level, the fair predictor is updated to be close to all subgroup specific predictors. We further prove that such a bilevel objective can effectively control the group sufficiency and generalization error. We evaluate the proposed framework on real-world datasets. Empirical evidence suggests the consistently improved fair predictions, as well as the comparable accuracy to the baselines.

伤口图像分割是伤口临床诊断和时间治疗的关键成分。最近，深度学习已成为伤口图像分割的主流方法。但是，在训练阶段之前，需要进行伤口图像的预处理，例如照明校正，因为可以大大提高性能。校正程序和深层模型的训练是彼此独立的，这导致了次优的分割性能，因为固定的照明校正可能不适合所有图像。为了解决上述问题，本文提出了一种端到端的双视分段方法，通过将可学习的照明校正模块纳入深度细分模型中。可以在训练阶段自动学习和更新模块的参数，而双视融合可以完全利用RAW图像和增强图像的功能。为了证明拟议框架的有效性和鲁棒性，在基准数据集上进行了广泛的实验。令人鼓舞的结果表明，与最先进的方法相比，我们的框架可以显着改善细分性能。

大型，注释的数据集在医学图像分析中不广泛使用，这是由于时间，成本和标记大型数据集相关的挑战。未标记的数据集更容易获取，在许多情况下，专家可以为一小部分图像提供标签是可行的。这项工作提出了一个信息理论的主动学习框架，该框架可以根据评估数据集中最大化预期信息增益（EIG）来指导未标记池的最佳图像选择。实验是在两个不同的医学图像分类数据集上进行的：多类糖尿病性视网膜病变量表分类和多级皮肤病变分类。结果表明，通过调整EIG来说明班级不平衡，我们提出的适应预期信息增益（AEIG）的表现优于几个流行的基线，包括基于多样性的核心和基于不确定性的最大熵抽样。具体而言，AEIG仅占总体表现的95％，只有19％的培训数据，而其他活跃的学习方法则需要约25％。我们表明，通过仔细的设计选择，我们的模型可以集成到现有的深度学习分类器中。

尽管做出了巨大的努力，但GigapixelS的分类全扫描图像（WSI）被严重限制在整个幻灯片的约束计算资源中，或者使用不同尺度的知识利用有限。此外，以前的大多数尝试都缺乏不确定性估计的能力。通常，病理学家经常共同分析不同的宏伟速度的WSI。如果通过使用单个放大倍率来不确定病理学家，那么他们将反复更改放大倍率以发现组织的各种特征。受病理学家的诊断过程的激励，在本文中，我们为WSI提出了一个可信赖的多尺度分类框架。我们的框架利用视觉变压器作为多部门的骨干，可以共同分类建模，估计显微镜的每种放大倍率的不确定性，并整合了来自不同放大倍率的证据。此外，为了利用WSIS的歧视性补丁并减少对计算资源的需求，我们建议使用注意力推广和非最大抑制作用提出一种新颖的补丁选择模式。为了从经验研究我们的方法的有效性，使用两个基准数据库对我们的WSI分类任务进行了经验实验。获得的结果表明，与最先进的方法相比，可信赖的框架可以显着改善WSI分类性能。

我们推出了元学学习算法概括性的新信息 - 理论分析。具体地，我们的分析提出了对传统学习 - 学习框架和现代模型 - 不可知的元学习（MAML）算法的通用理解。此外，我们为MAML的随机变体提供了一种数据依赖的泛化，这对于深入的少量学习是不受空置的。与以前的范围相比，依赖于梯度方形规范的界限，对模拟数据和众所周知的少量射击基准测试的经验验证表明，我们的绑定是大多数情况下更紧密的级。

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.

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.