本文介绍了一种基于纯变压器的方法，称为视频动作识别的多模态视频变压器（MM-VIT）。与仅利用解码的RGB帧的其他方案不同，MM-VIT专门在压缩视频域中进行操作，并利用所有容易获得的模式，即I帧，运动向量，残差和音频波形。为了处理从多种方式提取的大量时空令牌，我们开发了几种可扩展的模型变体，它们将自我关注分解在空间，时间和模态尺寸上。此外，为了进一步探索丰富的模态互动及其效果，我们开发并比较了可以无缝集成到变压器构建块中的三种不同的交叉模态注意力机制。关于三个公共行动识别基准的广泛实验（UCF-101，某事-V2，Kinetics-600）证明了MM-VIT以效率和准确性的最先进的视频变压器，并且表现更好或同样地表现出对于具有计算重型光学流的最先进的CNN对应物。

医疗报告的生成是一项具有挑战性的任务，因为它耗时，需要经验丰富的放射科医生的专业知识。医疗报告生成的目的是准确捕获和描述图像发现。先前的作品在不同域中使用大型数据集预处理其视觉编码神经网络，这些数据集无法在特定的医疗领域中学习一般的视觉表示。在这项工作中，我们提出了一个医学报告生成框架，该框架使用对比度学习方法来预处理视觉编码器，并且不需要其他元信息。此外，我们在对比度学习框架中采用肺部分割作为增强方法。该分割指导网络专注于编码肺部区域内的视觉特征。实验结果表明，所提出的框架可以在定量和定性上提高生成的医疗报告的性能和质量。

被广泛采用的缩减采样是为了在视觉识别的准确性和延迟之间取得良好的权衡。不幸的是，没有学习常用的合并层，因此无法保留重要信息。作为另一个降低方法，自适应采样权重和与任务相关的过程区域，因此能够更好地保留有用的信息。但是，自适应采样的使用仅限于某些层。在本文中，我们表明，在深神经网络的构件中使用自适应采样可以提高其效率。特别是，我们提出了SSBNET，该SSBNET是通过将采样层反复插入Resnet等现有网络构建的。实验结果表明，所提出的SSBNET可以在ImageNet和可可数据集上实现竞争性图像分类和对象检测性能。例如，SSB-Resnet-RS-200在Imagenet数据集上的精度达到82.6％，比基线RESNET-RS-152高0.6％，具有相似的复杂性。可视化显示了SSBNET在允许不同层专注于不同位置的优势，而消融研究进一步验证了自适应采样比均匀方法的优势。

We propose a distributionally robust return-risk model for Markov decision processes (MDPs) under risk and reward ambiguity. The proposed model optimizes the weighted average of mean and percentile performances, and it covers the distributionally robust MDPs and the distributionally robust chance-constrained MDPs (both under reward ambiguity) as special cases. By considering that the unknown reward distribution lies in a Wasserstein ambiguity set, we derive the tractable reformulation for our model. In particular, we show that that the return-risk model can also account for risk from uncertain transition kernel when one only seeks deterministic policies, and that a distributionally robust MDP under the percentile criterion can be reformulated as its nominal counterpart at an adjusted risk level. A scalable first-order algorithm is designed to solve large-scale problems, and we demonstrate the advantages of our proposed model and algorithm through numerical experiments.

Modern deep neural networks have achieved superhuman performance in tasks from image classification to game play. Surprisingly, these various complex systems with massive amounts of parameters exhibit the same remarkable structural properties in their last-layer features and classifiers across canonical datasets. This phenomenon is known as "Neural Collapse," and it was discovered empirically by Papyan et al. \cite{Papyan20}. Recent papers have theoretically shown the global solutions to the training network problem under a simplified "unconstrained feature model" exhibiting this phenomenon. We take a step further and prove the Neural Collapse occurrence for deep linear network for the popular mean squared error (MSE) and cross entropy (CE) loss. Furthermore, we extend our research to imbalanced data for MSE loss and present the first geometric analysis for Neural Collapse under this setting.

Machine Reading Comprehension has become one of the most advanced and popular research topics in the fields of Natural Language Processing in recent years. The classification of answerability questions is a relatively significant sub-task in machine reading comprehension; however, there haven't been many studies. Retro-Reader is one of the studies that has solved this problem effectively. However, the encoders of most traditional machine reading comprehension models in general and Retro-Reader, in particular, have not been able to exploit the contextual semantic information of the context completely. Inspired by SemBERT, we use semantic role labels from the SRL task to add semantics to pre-trained language models such as mBERT, XLM-R, PhoBERT. This experiment was conducted to compare the influence of semantics on the classification of answerability for the Vietnamese machine reading comprehension. Additionally, we hope this experiment will enhance the encoder for the Retro-Reader model's Sketchy Reading Module. The improved Retro-Reader model's encoder with semantics was first applied to the Vietnamese Machine Reading Comprehension task and obtained positive results.

Robust Markov decision processes (RMDPs) are promising models that provide reliable policies under ambiguities in model parameters. As opposed to nominal Markov decision processes (MDPs), however, the state-of-the-art solution methods for RMDPs are limited to value-based methods, such as value iteration and policy iteration. This paper proposes Double-Loop Robust Policy Gradient (DRPG), the first generic policy gradient method for RMDPs with a global convergence guarantee in tabular problems. Unlike value-based methods, DRPG does not rely on dynamic programming techniques. In particular, the inner-loop robust policy evaluation problem is solved via projected gradient descent. Finally, our experimental results demonstrate the performance of our algorithm and verify our theoretical guarantees.

Language models (LMs) have demonstrated remarkable performance on downstream tasks, using in-context exemplars or human instructions. Recent works have shown that chain-of-thought (CoT) prompting can elicit models to solve complex reasoning tasks, step-by-step. However, the efficacy of prompt-based CoT methods is restricted to very large LMs such as GPT-3 (175B), thus limiting deployability. In this paper, we revisit the fine-tuning approach to enable complex reasoning in smaller LMs, optimized to efficiently perform a specific task. We propose Fine-tune-CoT, a method that leverages the capabilities of very large LMs to generate reasoning samples and teach smaller models via fine-tuning. We evaluate our method on publicly available LMs across a wide range of complex tasks and model sizes. We find that Fine-tune-CoT enables substantial reasoning capability in small models, whereas previous prompt-based baselines exhibit near-random performance. Student models can even outperform the teacher in some tasks while reducing model size requirements by several orders of magnitude. We conduct extensive ablations and sample studies to understand the reasoning capabilities of student models. We also identify several important nuances that have been overlooked in concurrent fine-tuning works on CoT and address them in our analysis.

Semi-supervised object detection is important for 3D scene understanding because obtaining large-scale 3D bounding box annotations on point clouds is time-consuming and labor-intensive. Existing semi-supervised methods usually employ teacher-student knowledge distillation together with an augmentation strategy to leverage unlabeled point clouds. However, these methods adopt global augmentation with scene-level transformations and hence are sub-optimal for instance-level object detection. In this work, we propose an object-level point augmentor (OPA) that performs local transformations for semi-supervised 3D object detection. In this way, the resultant augmentor is derived to emphasize object instances rather than irrelevant backgrounds, making the augmented data more useful for object detector training. Extensive experiments on the ScanNet and SUN RGB-D datasets show that the proposed OPA performs favorably against the state-of-the-art methods under various experimental settings. The source code will be available at https://github.com/nomiaro/OPA.

It is well established in neuroscience that color vision plays an essential part in the human visual perception system. Meanwhile, many novel designs for computer vision inspired by human vision have achieved success in a wide range of tasks and applications. Nonetheless, how color differences affect machine vision has not been well explored. Our work tries to bridge this gap between the human color vision aspect of visual recognition and that of the machine. To achieve this, we curate two datasets: CIFAR10-F and CIFAR100-F, which are based on the foreground colors of the popular CIFAR datasets. Together with CIFAR10-B and CIFAR100-B, the existing counterpart datasets with information on the background colors of CIFAR test sets, we assign each image based on its color contrast level per its foreground and background color labels and use this as a proxy to study how color contrast affects machine vision. We first conduct a proof-of-concept study, showing the effect of color difference and validate our datasets. Furthermore, on a broader level, an important characteristic of human vision is its robustness against ambient changes; therefore, drawing inspirations from ophthalmology and the robustness literature, we analogize contrast sensitivity from the human visual aspect to machine vision and complement the current robustness study using corrupted images with our CIFAR-CoCo datasets. In summary, motivated by neuroscience and equipped with the datasets we curate, we devise a new framework in two dimensions to perform extensive analyses on the effect of color contrast and corrupted images: (1) model architecture, (2) model size, to measure the perception ability of machine vision beyond total accuracy. We also explore how task complexity and data augmentation play a role in this setup. Our results call attention to new evaluation approaches for human-like machine perception.