Optimization in multi-task learning (MTL) is more challenging than single-task learning (STL), as the gradient from different tasks can be contradictory. When tasks are related, it can be beneficial to share some parameters among them (cooperation). However, some tasks require additional parameters with expertise in a specific type of data or discrimination (specialization). To address the MTL challenge, we propose Mod-Squad, a new model that is Modularized into groups of experts (a 'Squad'). This structure allows us to formalize cooperation and specialization as the process of matching experts and tasks. We optimize this matching process during the training of a single model. Specifically, we incorporate mixture of experts (MoE) layers into a transformer model, with a new loss that incorporates the mutual dependence between tasks and experts. As a result, only a small set of experts are activated for each task. This prevents the sharing of the entire backbone model between all tasks, which strengthens the model, especially when the training set size and the number of tasks scale up. More interestingly, for each task, we can extract the small set of experts as a standalone model that maintains the same performance as the large model. Extensive experiments on the Taskonomy dataset with 13 vision tasks and the PASCAL-Context dataset with 5 vision tasks show the superiority of our approach.

神经辐射场（NERF）已成功用于场景表示。最近的工作还使用基于NERF的环境表示形式开发了机器人导航和操纵系统。由于对象定位是许多机器人应用的基础，因此进一步释放了机器人系统中NERF的潜力，我们研究了NERF场景中的对象定位。我们提出了一个基于变压器的框架NERF-LOC，以在NERF场景中提取3D边界对象框。 Nerf-Loc将预先训练的NERF模型和相机视图作为输入，并产生标记为3D边界对象的框作为输出。具体来说，我们设计了一对平行的变压器编码器分支，即粗流和细流，以编码目标对象的上下文和详细信息。然后将编码的功能与注意层融合在一起，以减轻准确对象定位的歧义。我们已经将我们的方法与基于传统变压器的方法进行了比较，我们的方法可以实现更好的性能。此外，我们还提出了第一个基于NERF样品的对象定位基准Nerflocbench。

通过网络视频的快速增长，视频语言建模引起了很多关注。大多数现有方法都假定视频帧和文本描述是语义上关联的，并专注于视频级别的视频模型。但是，该假设通常是有两个原因的：（1）凭借视频内容丰富的语义，很难用单个视频级别的描述覆盖所有帧； （2）原始视频通常具有嘈杂/毫无意义的信息（例如，镜头，过渡或预告片）。尽管最近的许多作品部署了注意力来减轻此问题，但无关/嘈杂的信息仍然使得很难解决。为了克服此类挑战，我们提出了一个高效有效的模型，称为语言引导网络（LGDN），用于视频语言建模。与使用所有提取的视频帧的大多数现有方法不同，LGDN在语言监督下动态过滤了未对准或冗余的帧，并且每个视频仅获得2---4个显着帧，以进行交叉模式令牌级别的对准。在五个公共数据集上进行的广泛实验表明，我们的LGDN优于最先进的利润率。我们还提供了详细的消融研究，以揭示解决噪声问题的关键重要性，以启发未来的视频语言工作。

多模式学习，尤其是大规模的多模式预训练，在过去的几年中已经迅速发展，并带来了人工智能（AI）的最大进步。尽管具有有效性，但了解多模式预训练模型的潜在机制仍然是一个巨大的挑战。揭示此类模型的解释性可能会使AI领域中新型学习范式的突破。为此，鉴于人脑的多模式性质，我们建议借助非侵入性脑成像技术（例如功能磁共振成像（fMRI））探索多模式学习模型的解释性。具体而言，我们首先提出了1500万个图像文本对预训练的新设计的多模式基础模型，该模型在各种认知下游任务中显示出强烈的多模式理解和概括能力。此外，从神经编码的角度来看（基于我们的基础模型），我们发现，与单峰相比，经过多模式训练的视觉和舌编码器都更像脑状。特别是，我们确定了许多大脑区域，其中多模式训练的编码器表现出更好的神经编码性能。这与现有有关探索大脑多感觉整合的研究的发现是一致的。因此，我们认为，多模式基础模型是神经科学家研究人脑中多模式信号处理机制的更合适的工具。我们的发现还证明了多模式基础模型作为理想的计算模拟器的潜力，以促进脑和大脑的AI研究。

Transformer在学习视觉和语言表示方面取得了巨大的成功，这在各种下游任务中都是一般的。在视觉控制中，可以在不同控制任务之间转移的可转移状态表示对于减少训练样本量很重要。但是，将变压器移植到样品有效的视觉控制仍然是一个具有挑战性且未解决的问题。为此，我们提出了一种新颖的控制变压器（CTRLFORMER），具有先前艺术所没有的许多吸引人的好处。首先，CTRLFORMER共同学习视觉令牌和政策令牌之间的自我注意事项机制，在不同的控制任务之间可以学习和转移多任务表示无灾难性遗忘。其次，我们仔细设计了一种对比的增强学习范式来训练Ctrlformer，从而使其能够达到高样本效率，这在控制问题中很重要。例如，在DMControl基准测试中，与最近的高级方法不同，该方法在使用100K样品转移学习后通过在“ Cartpole”任务中产生零分数而失败，CTRLFORMER可以在维持100K样本的同时获得最先进的分数先前任务的性能。代码和模型已在我们的项目主页中发布。

Different people speak with diverse personalized speaking styles. Although existing one-shot talking head methods have made significant progress in lip sync, natural facial expressions, and stable head motions, they still cannot generate diverse speaking styles in the final talking head videos. To tackle this problem, we propose a one-shot style-controllable talking face generation framework. In a nutshell, we aim to attain a speaking style from an arbitrary reference speaking video and then drive the one-shot portrait to speak with the reference speaking style and another piece of audio. Specifically, we first develop a style encoder to extract dynamic facial motion patterns of a style reference video and then encode them into a style code. Afterward, we introduce a style-controllable decoder to synthesize stylized facial animations from the speech content and style code. In order to integrate the reference speaking style into generated videos, we design a style-aware adaptive transformer, which enables the encoded style code to adjust the weights of the feed-forward layers accordingly. Thanks to the style-aware adaptation mechanism, the reference speaking style can be better embedded into synthesized videos during decoding. Extensive experiments demonstrate that our method is capable of generating talking head videos with diverse speaking styles from only one portrait image and an audio clip while achieving authentic visual effects. Project Page: https://github.com/FuxiVirtualHuman/styletalk.

Unmanned aerial vehicle (UAV) swarms are considered as a promising technique for next-generation communication networks due to their flexibility, mobility, low cost, and the ability to collaboratively and autonomously provide services. Distributed learning (DL) enables UAV swarms to intelligently provide communication services, multi-directional remote surveillance, and target tracking. In this survey, we first introduce several popular DL algorithms such as federated learning (FL), multi-agent Reinforcement Learning (MARL), distributed inference, and split learning, and present a comprehensive overview of their applications for UAV swarms, such as trajectory design, power control, wireless resource allocation, user assignment, perception, and satellite communications. Then, we present several state-of-the-art applications of UAV swarms in wireless communication systems, such us reconfigurable intelligent surface (RIS), virtual reality (VR), semantic communications, and discuss the problems and challenges that DL-enabled UAV swarms can solve in these applications. Finally, we describe open problems of using DL in UAV swarms and future research directions of DL enabled UAV swarms. In summary, this survey provides a comprehensive survey of various DL applications for UAV swarms in extensive scenarios.

In this work, we focus on instance-level open vocabulary segmentation, intending to expand a segmenter for instance-wise novel categories without mask annotations. We investigate a simple yet effective framework with the help of image captions, focusing on exploiting thousands of object nouns in captions to discover instances of novel classes. Rather than adopting pretrained caption models or using massive caption datasets with complex pipelines, we propose an end-to-end solution from two aspects: caption grounding and caption generation. In particular, we devise a joint Caption Grounding and Generation (CGG) framework based on a Mask Transformer baseline. The framework has a novel grounding loss that performs explicit and implicit multi-modal feature alignments. We further design a lightweight caption generation head to allow for additional caption supervision. We find that grounding and generation complement each other, significantly enhancing the segmentation performance for novel categories. We conduct extensive experiments on the COCO dataset with two settings: Open Vocabulary Instance Segmentation (OVIS) and Open Set Panoptic Segmentation (OSPS). The results demonstrate the superiority of our CGG framework over previous OVIS methods, achieving a large improvement of 6.8% mAP on novel classes without extra caption data. Our method also achieves over 15% PQ improvements for novel classes on the OSPS benchmark under various settings.

Feature selection helps reduce data acquisition costs in ML, but the standard approach is to train models with static feature subsets. Here, we consider the dynamic feature selection (DFS) problem where a model sequentially queries features based on the presently available information. DFS is often addressed with reinforcement learning (RL), but we explore a simpler approach of greedily selecting features based on their conditional mutual information. This method is theoretically appealing but requires oracle access to the data distribution, so we develop a learning approach based on amortized optimization. The proposed method is shown to recover the greedy policy when trained to optimality and outperforms numerous existing feature selection methods in our experiments, thus validating it as a simple but powerful approach for this problem.

Non-line-of-sight (NLOS) imaging aims to reconstruct the three-dimensional hidden scenes from the data measured in the line-of-sight, which uses photon time-of-flight information encoded in light after multiple diffuse reflections. The under-sampled scanning data can facilitate fast imaging. However, the resulting reconstruction problem becomes a serious ill-posed inverse problem, the solution of which is of high possibility to be degraded due to noises and distortions. In this paper, we propose two novel NLOS reconstruction models based on curvature regularization, i.e., the object-domain curvature regularization model and the dual (i.e., signal and object)-domain curvature regularization model. Fast numerical optimization algorithms are developed relying on the alternating direction method of multipliers (ADMM) with the backtracking stepsize rule, which are further accelerated by GPU implementation. We evaluate the proposed algorithms on both synthetic and real datasets, which achieve state-of-the-art performance, especially in the compressed sensing setting. All our codes and data are available at https://github.com/Duanlab123/CurvNLOS.