Recent success of vision transformers has inspired a series of vision backbones with novel feature transformation paradigms, which report steady performance gain. Although the novel feature transformation designs are often claimed as the source of gain, some backbones may benefit from advanced engineering techniques, which makes it hard to identify the real gain from the key feature transformation operators. In this paper, we aim to identify real gain of popular convolution and attention operators and make an in-depth study of them. We observe that the main difference among these feature transformation modules, e.g., attention or convolution, lies in the way of spatial feature aggregation, or the so-called "spatial token mixer" (STM). Hence, we first elaborate a unified architecture to eliminate the unfair impact of different engineering techniques, and then fit STMs into this architecture for comparison. Based on various experiments on upstream/downstream tasks and the analysis of inductive bias, we find that the engineering techniques boost the performance significantly, but the performance gap still exists among different STMs. The detailed analysis also reveals some interesting findings of different STMs, such as effective receptive fields and invariance tests. The code and trained models will be publicly available at https://github.com/OpenGVLab/STM-Evaluation

症状信息主要记录在自由文本临床笔记中，并且无法直接用于下游应用。为了应对这一挑战，需要采用可以处理不同机构和专业的临床语言变化的信息提取方法。在本文中，我们使用预处理和微调数据介绍了症状提取的领域概括，这些数据在机构和/或专业和患者人群方面与目标领域不同。我们使用基于变压器的联合实体和关系提取方法提取症状事件。为了减少对域特异性特征的依赖，我们提出了一种域的概括方法，该方法可以动态掩盖源域中的频繁症状单词。此外，我们将变压器语言模型（LM）预先限定在与任务相关的无标记文本上，以更好地表示。我们的实验表明，当源域与目标域更遥远时，掩盖和自适应预处理方法可以显着提高性能。

在以前的基于深度学习的方法中，语义分割被认为是静态或动态的每个像素分类任务，\ textit {i.e。，}将每个像素表示分类为特定类别。但是，这些方法仅着眼于学习更好的像素表示或分类内核，同时忽略对象的结构信息，这对于人类决策机制至关重要。在本文中，我们提出了一种用于语义分割的新范式，称为结构感知的提取。具体而言，它通过一组可学习的结构令牌与图像特征之间的相互作用生成分割结果，该功能旨在从功能中逐步提取每个类别的结构信息。广泛的实验表明，我们的结构量优先于三个广泛使用的基准，包括ADE20K，CityScapes和Coco-Stuff-10k。

最近，变形金刚在各种视觉任务中表现出具有很大的表现。为了降低全球自我关注引起的二次计算复杂性，各种方法限制了本地区域内的注意范围以提高其效率。因此，单个注意层中的接收领域不够大，导致上下文建模不足。为了解决这个问题，我们提出了一种浅色的自我关注（PS-Legution），这在浅层形状的地区内进行自我关注。与全球自我关注相比，PS-Peponsion可以显着降低计算和内存成本。同时，它可以通过以前的本地自我关注机制捕获类似的计算复杂性下的更丰富的上下文信息。根据PS-Intension，我们开发了一个具有分层架构的一般视觉变压器骨干，名为苍白变压器，其达到83.4％，84.3％和84.9％的前1个精度，分别为22米，48米和85米对于224个Imagenet-1K分类，优于上一个视觉变压器骨干板。对于下游任务，我们的苍白变压器骨干在ADE20K语义分割和Coco对象检测和实例分割中，我们的苍白变压器骨干比最近最近的最新的克斯卡文变压器表现更好。代码将在https://github.com/br -dl/paddlevit上发布。

由于长距离依赖性建模的能力，变压器在各种自然语言处理和计算机视觉任务中表现出令人印象深刻的性能。最近的进展证明，将这种变压器与基于CNN的语义图像分割模型相结合非常有前途。然而，目前还没有很好地研究了纯变压器的方法如何实现图像分割。在这项工作中，我们探索了语义图像分割的新框架，它是基于编码器 - 解码器的完全变压器网络（FTN）。具体地，我们首先提出金字塔组变压器（PGT）作为逐步学习分层特征的编码器，同时降低标准视觉变压器（VIT）的计算复杂性。然后，我们将特征金字塔变换器（FPT）提出了来自PGT编码器的多电平进行语义图像分割的多级别的语义级别和空间级信息。令人惊讶的是，这种简单的基线可以在多个具有挑战性的语义细分和面部解析基准上实现更好的结果，包括帕斯卡背景，ADE20K，Cocostuff和Celebamask-HQ。源代码将在https://github.com/br -dl/paddlevit上发布。

Benefiting from the intrinsic supervision information exploitation capability, contrastive learning has achieved promising performance in the field of deep graph clustering recently. However, we observe that two drawbacks of the positive and negative sample construction mechanisms limit the performance of existing algorithms from further improvement. 1) The quality of positive samples heavily depends on the carefully designed data augmentations, while inappropriate data augmentations would easily lead to the semantic drift and indiscriminative positive samples. 2) The constructed negative samples are not reliable for ignoring important clustering information. To solve these problems, we propose a Cluster-guided Contrastive deep Graph Clustering network (CCGC) by mining the intrinsic supervision information in the high-confidence clustering results. Specifically, instead of conducting complex node or edge perturbation, we construct two views of the graph by designing special Siamese encoders whose weights are not shared between the sibling sub-networks. Then, guided by the high-confidence clustering information, we carefully select and construct the positive samples from the same high-confidence cluster in two views. Moreover, to construct semantic meaningful negative sample pairs, we regard the centers of different high-confidence clusters as negative samples, thus improving the discriminative capability and reliability of the constructed sample pairs. Lastly, we design an objective function to pull close the samples from the same cluster while pushing away those from other clusters by maximizing and minimizing the cross-view cosine similarity between positive and negative samples. Extensive experimental results on six datasets demonstrate the effectiveness of CCGC compared with the existing state-of-the-art algorithms.

As one of the prevalent methods to achieve automation systems, Imitation Learning (IL) presents a promising performance in a wide range of domains. However, despite the considerable improvement in policy performance, the corresponding research on the explainability of IL models is still limited. Inspired by the recent approaches in explainable artificial intelligence methods, we proposed a model-agnostic explaining framework for IL models called R2RISE. R2RISE aims to explain the overall policy performance with respect to the frames in demonstrations. It iteratively retrains the black-box IL model from the randomized masked demonstrations and uses the conventional evaluation outcome environment returns as the coefficient to build an importance map. We also conducted experiments to investigate three major questions concerning frames' importance equality, the effectiveness of the importance map, and connections between importance maps from different IL models. The result shows that R2RISE successfully distinguishes important frames from the demonstrations.

Text clustering and topic extraction are two important tasks in text mining. Usually, these two tasks are performed separately. For topic extraction to facilitate clustering, we can first project texts into a topic space and then perform a clustering algorithm to obtain clusters. To promote topic extraction by clustering, we can first obtain clusters with a clustering algorithm and then extract cluster-specific topics. However, this naive strategy ignores the fact that text clustering and topic extraction are strongly correlated and follow a chicken-and-egg relationship. Performing them separately fails to make them mutually benefit each other to achieve the best overall performance. In this paper, we propose an unsupervised text clustering and topic extraction framework (ClusTop) which integrates text clustering and topic extraction into a unified framework and can achieve high-quality clustering result and extract topics from each cluster simultaneously. Our framework includes four components: enhanced language model training, dimensionality reduction, clustering and topic extraction, where the enhanced language model can be viewed as a bridge between clustering and topic extraction. On one hand, it provides text embeddings with a strong cluster structure which facilitates effective text clustering; on the other hand, it pays high attention on the topic related words for topic extraction because of its self-attention architecture. Moreover, the training of enhanced language model is unsupervised. Experiments on two datasets demonstrate the effectiveness of our framework and provide benchmarks for different model combinations in this framework.

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.

Recent advances in self-supervised learning (SSL) in computer vision are primarily comparative, whose goal is to preserve invariant and discriminative semantics in latent representations by comparing siamese image views. However, the preserved high-level semantics do not contain enough local information, which is vital in medical image analysis (e.g., image-based diagnosis and tumor segmentation). To mitigate the locality problem of comparative SSL, we propose to incorporate the task of pixel restoration for explicitly encoding more pixel-level information into high-level semantics. We also address the preservation of scale information, a powerful tool in aiding image understanding but has not drawn much attention in SSL. The resulting framework can be formulated as a multi-task optimization problem on the feature pyramid. Specifically, we conduct multi-scale pixel restoration and siamese feature comparison in the pyramid. In addition, we propose non-skip U-Net to build the feature pyramid and develop sub-crop to replace multi-crop in 3D medical imaging. The proposed unified SSL framework (PCRLv2) surpasses its self-supervised counterparts on various tasks, including brain tumor segmentation (BraTS 2018), chest pathology identification (ChestX-ray, CheXpert), pulmonary nodule detection (LUNA), and abdominal organ segmentation (LiTS), sometimes outperforming them by large margins with limited annotations.