An increasing number of public datasets have shown a marked clinical impact on assessing anatomical structures. However, each of the datasets is small, partially labeled, and rarely investigates severe tumor subjects. Moreover, current models are limited to segmenting specific organs/tumors, which can not be extended to novel domains and classes. To tackle these limitations, we introduce embedding learned from Contrastive Language-Image Pre-training (CLIP) to segmentation models, dubbed the CLIP-Driven Universal Model. The Universal Model can better segment 25 organs and 6 types of tumors by exploiting the semantic relationship between abdominal structures. The model is developed from an assembly of 14 datasets with 3,410 CT scans and evaluated on 6,162 external CT scans from 3 datasets. We rank first on the public leaderboard of the Medical Segmentation Decathlon (MSD) and achieve the state-of-the-art results on Beyond The Cranial Vault (BTCV). Compared with dataset-specific models, the Universal Model is computationally more efficient (6x faster), generalizes better to CT scans from varying sites, and shows stronger transfer learning performance on novel tasks. The design of CLIP embedding enables the Universal Model to be easily extended to new classes without catastrophically forgetting the previously learned classes.

最近，机器学习（ML）电位的发展使得以量子力学（QM）模型的精度进行大规模和长期分子模拟成为可能。但是，对于高水平的QM方法，例如在元gga级和/或具有精确交换的密度函数理论（DFT），量子蒙特卡洛等，生成足够数量的用于训练的数据由于其高成本，计算挑战性。在这项工作中，我们证明了基于ML的DFT模型Deep Kohn-Sham（Deepks）可以在很大程度上缓解这个问题。 DeepKS采用计算高效的基于神经网络的功能模型来构建在廉价DFT模型上添加的校正项。在训练后，DeepKs提供了与高级QM方法相比，具有紧密匹配的能量和力，但是所需的训练数据的数量是比训练可靠的ML潜力所需的数量级要小。因此，DeepKs可以用作昂贵的QM型号和ML电位之间的桥梁：一个人可以生成相当数量的高准确性QM数据来训练DeepKs模型，然后使用DeepKs型号来标记大量的配置以标记训练ML潜力。该周期系统方案在DFT软件包算盘中实施，该计划是开源的，可以在各种应用程序中使用。

When testing conditions differ from those represented in training data, so-called out-of-distribution (OOD) inputs can mar the reliability of black-box learned components in the modern robot autonomy stack. Therefore, coping with OOD data is an important challenge on the path towards trustworthy learning-enabled open-world autonomy. In this paper, we aim to demystify the topic of OOD data and its associated challenges in the context of data-driven robotic systems, drawing connections to emerging paradigms in the ML community that study the effect of OOD data on learned models in isolation. We argue that as roboticists, we should reason about the overall system-level competence of a robot as it performs tasks in OOD conditions. We highlight key research questions around this system-level view of OOD problems to guide future research toward safe and reliable learning-enabled autonomy.

To reproduce the success of text-to-image (T2I) generation, recent works in text-to-video (T2V) generation employ large-scale text-video dataset for fine-tuning. However, such paradigm is computationally expensive. Humans have the amazing ability to learn new visual concepts from just one single exemplar. We hereby study a new T2V generation problem$\unicode{x2014}$One-Shot Video Generation, where only a single text-video pair is presented for training an open-domain T2V generator. Intuitively, we propose to adapt the T2I diffusion model pretrained on massive image data for T2V generation. We make two key observations: 1) T2I models are able to generate images that align well with the verb terms; 2) extending T2I models to generate multiple images concurrently exhibits surprisingly good content consistency. To further learn continuous motion, we propose Tune-A-Video with a tailored Sparse-Causal Attention, which generates videos from text prompts via an efficient one-shot tuning of pretrained T2I diffusion models. Tune-A-Video is capable of producing temporally-coherent videos over various applications such as change of subject or background, attribute editing, style transfer, demonstrating the versatility and effectiveness of our method.

Vector-Quantized (VQ-based) generative models usually consist of two basic components, i.e., VQ tokenizers and generative transformers. Prior research focuses on improving the reconstruction fidelity of VQ tokenizers but rarely examines how the improvement in reconstruction affects the generation ability of generative transformers. In this paper, we surprisingly find that improving the reconstruction fidelity of VQ tokenizers does not necessarily improve the generation. Instead, learning to compress semantic features within VQ tokenizers significantly improves generative transformers' ability to capture textures and structures. We thus highlight two competing objectives of VQ tokenizers for image synthesis: semantic compression and details preservation. Different from previous work that only pursues better details preservation, we propose Semantic-Quantized GAN (SeQ-GAN) with two learning phases to balance the two objectives. In the first phase, we propose a semantic-enhanced perceptual loss for better semantic compression. In the second phase, we fix the encoder and codebook, but enhance and finetune the decoder to achieve better details preservation. The proposed SeQ-GAN greatly improves VQ-based generative models and surpasses the GAN and Diffusion Models on both unconditional and conditional image generation. Our SeQ-GAN (364M) achieves Frechet Inception Distance (FID) of 6.25 and Inception Score (IS) of 140.9 on 256x256 ImageNet generation, a remarkable improvement over VIT-VQGAN (714M), which obtains 11.2 FID and 97.2 IS.

In this study, we explore the representation mapping from the domain of visual arts to the domain of music, with which we can use visual arts as an effective handle to control music generation. Unlike most studies in multimodal representation learning that are purely data-driven, we adopt an analysis-by-synthesis approach that combines deep music representation learning with user studies. Such an approach enables us to discover \textit{interpretable} representation mapping without a huge amount of paired data. In particular, we discover that visual-to-music mapping has a nice property similar to equivariant. In other words, we can use various image transformations, say, changing brightness, changing contrast, style transfer, to control the corresponding transformations in the music domain. In addition, we released the Vis2Mus system as a controllable interface for symbolic music generation.

在混乱的环境中自动二次运动的敏捷飞行需要受到限制的运动计划和控制，但要受翻译和旋转动力学的影响。传统的基于模型的方法通常需要复杂的设计和重型计算。在本文中，我们开发了一种基于深厚的增强学习方法，该方法解决了通过动态狭窄大门飞行的挑战性任务。我们设计了一个模型预测控制器，其自适应跟踪参考参考由深神经网络（DNN）进行了参数。这些参考文献包括遍历时间和四型SE（3）遍历姿势，这些姿势鼓励机器人从各种初始条件中使用最大的安全边缘飞行大门。为了应对在高度动态环境中的训练困难，我们开发了一个增强的学习框架，以有效地训练DNN，从而很好地介绍了各种环境。此外，我们提出了一种二进制搜索算法，该算法允许在线适应（3）对动态门的引用。最后，通过广泛的高保真模拟，我们表明我们的方法对门的速度不确定性具有鲁棒性，并适应了不同的门轨迹和方向。

音乐包含超出节拍和措施的层次结构。尽管层次结构注释有助于音乐信息检索和计算机音乐学，但在当前的数字音乐数据库中，这种注释很少。在本文中，我们探讨了一种数据驱动的方法，以自动从分数中提取分层的度量结构。我们提出了一个具有时间卷积网络条件随机字段（TCN-CRF）体系结构的新模型。给定符号音乐得分，我们的模型以良好的形式采用任意数量的声音，并预测了从偏低级别到截面级别的4级层次级别结构。我们还使用RWC-POP MIDI文件来注释数据集，以促进培​​训和评估。我们通过实验表明，在不同的编排设置下，提出的方法的性能优于基于规则的方法。我们还对模型预测进行了一些简单的音乐分析。所有演示，数据集和预培训模型均在GitHub上公开可用。

抒情的解释可以帮助人们快速理解歌曲及其歌词，还可以使管理，检索和发现音乐档案不断增长，从而更加容易地检索和发现歌曲。在本文中，我们提出了Bart-Fusion，这是一种新型模型，用于从歌词和音乐音频中生成歌词解释，该模型将大规模的预训练的语言模型与音频编码器结合在一起。我们采用跨模式注意模块将音频表示形式纳入歌词表示形式，以帮助预先训练的语言模型从音频的角度了解歌曲，同时保留语言模型的原始生成性能。我们还发布了歌曲解释数据集，这是一个新的大型数据集，用于培训和评估我们的模型。实验结果表明，其他音频信息有助于我们的模型更好地理解单词和音乐，并产生精确和流利的解释。跨模式音乐检索的另一个实验表明，巴特融合产生的解释也可以帮助人们比原始的巴特更准确地检索音乐。

本文解决了对预先训练的深神经网络进行排名并筛选最下游任务的重要问题。这是具有挑战性的，因为每个任务的基本模型排名只能通过微调目标数据集中的预训练模型来生成，该模型是蛮力且计算昂贵的。最近的高级方法提出了几个轻巧的可转移性指标来预测微调结果。但是，这些方法仅捕获静态表示，但忽略了微调动态。为此，本文提出了一个新的可传递性度量，称为\ textbf {s} elf-challenging \ textbf {f} isher \ textbf {d} is Criminant \ textbf {a} nalisy（\ textbf {\ textbf {sfda}）现有作品没有的有吸引力的好处。首先，SFDA可以将静态特征嵌入渔民空间中，并完善它们，以在类之间更好地分离性。其次，SFDA使用一种自我挑战的机制来鼓励不同的预训练模型来区分硬性示例。第三，SFDA可以轻松地为模型集合选择多个预训练的模型。 $ 33 $预培训的$ 11 $下游任务的$ 33 $预培训模型的广泛实验表明，在测量预训练模型的可传递性时，SFDA具有高效，有效和健壮。例如，与最先进的方法NLEEP相比，SFDA平均显示了59.1美元的增益，同时带来了$ 22.5 $ x的墙壁速度速度。该代码将在\ url {https://github.com/tencentarc/sfda}上提供。