FADE: Fusing the Assets of Decoder and Encoder for Task-Agnostic Upsampling

We consider the problem of task-agnostic feature upsampling in dense prediction where an upsampling operator is required to facilitate both region-sensitive tasks like semantic segmentation and detail-sensitive tasks such as image matting. Existing upsampling operators often can work well in either type of the tasks, but not both. In this work, we present FADE, a novel, plug-and-play, and task-agnostic upsampling operator. FADE benefits from three design choices: i) considering encoder and decoder features jointly in upsampling kernel generation; ii) an efficient semi-shift convolutional operator that enables granular control over how each feature point contributes to upsampling kernels; iii) a decoder-dependent gating mechanism for enhanced detail delineation. We first study the upsampling properties of FADE on toy data and then evaluate it on large-scale semantic segmentation and image matting. In particular, FADE reveals its effectiveness and task-agnostic characteristic by consistently outperforming recent dynamic upsampling operators in different tasks. It also generalizes well across convolutional and transformer architectures with little computational overhead. Our work additionally provides thoughtful insights on what makes for task-agnostic upsampling. Code is available at: http://lnkiy.in/fade_inComment: Accepted to ECCV 2022. Code is available at http://lnkiy.in/fade_i

Learning to Upsample by Learning to Sample

We present DySample, an ultra-lightweight and effective dynamic upsampler. While impressive performance gains have been witnessed from recent kernel-based dynamic upsamplers such as CARAFE, FADE, and SAPA, they introduce much workload, mostly due to the time-consuming dynamic convolution and the additional sub-network used to generate dynamic kernels. Further, the need for high-res feature guidance of FADE and SAPA somehow limits their application scenarios. To address these concerns, we bypass dynamic convolution and formulate upsampling from the perspective of point sampling, which is more resource-efficient and can be easily implemented with the standard built-in function in PyTorch. We first showcase a naive design, and then demonstrate how to strengthen its upsampling behavior step by step towards our new upsampler, DySample. Compared with former kernel-based dynamic upsamplers, DySample requires no customized CUDA package and has much fewer parameters, FLOPs, GPU memory, and latency. Besides the light-weight characteristics, DySample outperforms other upsamplers across five dense prediction tasks, including semantic segmentation, object detection, instance segmentation, panoptic segmentation, and monocular depth estimation. Code is available at https://github.com/tiny-smart/dysample.Comment: Accepted by ICCV 202

SAPA: Similarity-Aware Point Affiliation for Feature Upsampling

We introduce point affiliation into feature upsampling, a notion that describes the affiliation of each upsampled point to a semantic cluster formed by local decoder feature points with semantic similarity. By rethinking point affiliation, we present a generic formulation for generating upsampling kernels. The kernels encourage not only semantic smoothness but also boundary sharpness in the upsampled feature maps. Such properties are particularly useful for some dense prediction tasks such as semantic segmentation. The key idea of our formulation is to generate similarity-aware kernels by comparing the similarity between each encoder feature point and the spatially associated local region of decoder features. In this way, the encoder feature point can function as a cue to inform the semantic cluster of upsampled feature points. To embody the formulation, we further instantiate a lightweight upsampling operator, termed Similarity-Aware Point Affiliation (SAPA), and investigate its variants. SAPA invites consistent performance improvements on a number of dense prediction tasks, including semantic segmentation, object detection, depth estimation, and image matting. Code is available at: https://github.com/poppinace/sapaComment: Accepted to NeurIPS 2022. Code is available at https://github.com/poppinace/sap

Million-scale Object Detection with Large Vision Model

Over the past few years, there has been growing interest in developing a broad, universal, and general-purpose computer vision system. Such a system would have the potential to solve a wide range of vision tasks simultaneously, without being restricted to a specific problem or data domain. This is crucial for practical, real-world computer vision applications. In this study, we focus on the million-scale multi-domain universal object detection problem, which presents several challenges, including cross-dataset category label duplication, label conflicts, and the need to handle hierarchical taxonomies. Furthermore, there is an ongoing challenge in the field to find a resource-efficient way to leverage large pre-trained vision models for million-scale cross-dataset object detection. To address these challenges, we introduce our approach to label handling, hierarchy-aware loss design, and resource-efficient model training using a pre-trained large model. Our method was ranked second in the object detection track of the Robust Vision Challenge 2022 (RVC 2022). We hope that our detailed study will serve as a useful reference and alternative approach for similar problems in the computer vision community. The code is available at https://github.com/linfeng93/Large-UniDet.Comment: This paper is revised by ChatGP

Equity in walking access to community home care facility resources for elderly with different mobility: A case study of Lianhu District, Xi'an.

As the aging of China's population continues to deepen, a number of elderly care facilities relying on community platforms to provide home care services have been established in urban communities, effectively alleviating the problem of difficult community elderly care, while a spatial mismatch between the facilities and the elderly population has also emerged. To solve this problem, this paper analyzes the equity in walking access to community home care facilities for elderly people with different mobility abilities in Lianhu District of Xi'an City, taking the resources of community home care facilities as the research object. Firstly, the coverage rate of the facilities was calculated based on the 15-minute walking range of the elderly with different mobility, and the accessibility of the facilities was measured using the Kernel Density-type two-step moving search method. Then, Gini coefficient, Lorenz curve and location entropy were used to analyze the spatial matching pattern of facilities and elderly population. The results show that there is a serious spatial mismatch between the resources of community home care facilities and the elderly population with mobility restriction. In addition, the available facility area per capita is low for more than 80% of the elderly with mobility restriction, and the road network density has a significant impact on the access of the elderly with mobility restriction to the community home care facility resources. These research results indicate that the spatial layout and configuration of community home care facilities are unfair to the elderly with poor mobility, and that these elderly care facility configurations do not favor the disadvantaged groups

FE analysis of residual stress and welding deformation of a low-alloy UHS quenched steel fillet joint

In this study, both the distribution of residual stress and welding deformation in a double-pass T-joint made of low-alloy ultra-high strength (UHS) quenched steel have been investigated experimentally and numerically. Using Abaqus software, the primary aim of this study is to develop an advanced computational approach incorporating a highly precise material model that accounts for solid-state phase transformation (SSPT) in the heat-affected zone (HAZ) and softening effect (SE) in the subcritical heat-affected zone (SCHAZ) to simulate residual stress and welding deformation in the double-pass T-joint. The predictions of welding thermal cycles, residual stress distribution, and welding deformation made by the finite element model have been validated against corresponding experimental results. When both SSPT and SE were considered in the finite element model, the predictions closely aligned with the experimental measurements. The experimental findings reveal that the maximum degree of softening in a T-joint subjected to double thermal cycles is more pronounced than in a T-joint subjected to a single thermal cycle. The simulation results indicate that SSPT significantly impacts both the magnitude and distribution of residual stress in the HAZ of the T-joint, while SE also can reduce the magnitude of longitudinal residual stress to a certain extent especially in SCHAZ. Furthermore, the simulation results suggest that SSPT moderately affects the distribution of longitudinal plastic strain in the double-pass T-joint, with minimal influence on angular distortion and transverse shrinkage. Additionally, the simulation results indicate that the SE has a limited impact on the welding deformation of the T-joint

Online Political Protest in China: Its Causes and Implications

This thesis examines the growing social phenomenon of online political protests in China. While studies have found that internet technologies have empowered the Chinese government to stifle political mobilization and dissent on the Internet, they failed to explain the proliferation of political protests in China's cyberspace. Other studies focused on how the Internet's technical features, contradictions within China's political system, or individuals' grievances contributed to the occurrence of online political protests. However, the principal limitation of these studies was that they could not explain why the effects of online political protests on China's political changes were so limited. This thesis presents an examination of the emergence and political impacts of China's online political protests. It employs the Political Opportunity Structure theory to explain why online political protests frequently occur in China and why they have not caused meaningful changes to China's political system.The thesis argues that the emergence and demise of China's online political protests are dependent upon four key factors--elite divisions, internet control, online social networks, and influential allies, which form political opportunity structures for online political protests. While the existence of elite divisions, capricious internet control, extensive online social networks, and the active role of influential allies cause the occurrence and development of online political protests, the disappearance of such factors will damage political opportunity structures for online political protests, thereby leading to their end. The thesis conducts three case studies--the Lei Yang event (雷洋事件), the Watch Brother event (表哥事件), and the Chai Jing event (柴静事件)--to test the validity of its main argument.The thesis represents a further step towards apply social movement theories to study acts of protest in China's cyberspace. Moreover, by examining the dynamic process of online political protests, it has provided a deeper insight into the role of the Internet in catalyzing political changes in China

Exploring drivers for public engagement in social media communication with medical social influencers in China.

Social networking sites offer an important means for increasing the accessibility and enabling new forms of health communication between the public and medical social influencers (MSIs). MSIs have a social presence and are perceived as a credible source of health-related information. A research gap, however, exists in understanding the communication strategies employed by MSIs and the factors driving the public to engage in health communication with MSIs. This study, therefore, developed a new conceptual framework incorporating health communication, dialogic and interpersonal communication by employing quantitative content analysis to examine public engagement with MSI communication on the largest microblogging site in China, Sina Weibo. The analysis yielded insights into how the usefulness of health-related information provided alongside the interactive dialogue and affective practices played an active role in engaging the public. The public sought health-related information primarily to address issues of concern for well-being and a high level of engagement in terms of online shares, likes, and comments was found. The use of multimedia made the site more appealing, resulting in likes while the expression of emotions by MSIs generated likes and comments. The need to connect with other online users and have a sense of community was reflected in engagement through sharing useful MSI posts by the public. By identifying influential MSIs on social networking sites, health information providers such as organizations and the government can raise awareness of health issues to foster a healthy lifestyle and contribute to better living in the community

Comparison of Machine-Learning Methods for Urban Land-Use Mapping in Hangzhou City, China

Urban land-use information is important for urban land-resource planning and management. However, current methods using traditional surveys cannot meet the demand for the rapid development of urban land management. There is an urgent need to develop new methods to overcome the shortcomings of conventional methods. To address the issue, this study used the random forest (RF), support vector machine (SVM), and artificial neural network (ANN) models to build machine-leaning methods for urban land-use classification. Taking Hangzhou as an example, these machine-leaning methods could all successfully classify the essential urban land use into 6 Level I classes and 13 Level II classes based on the semantic features extracted from Sentinel-2A images, multi-source features of types of points of interest (POIs), land surface temperature, night lights, and building height. The validation accuracy of the RF model for the Level I and Level II land use was 79.88% and 71.89%, respectively, performing better compared to SVM (78.40% and 68.64%) and ANN models (71.30% and 63.02%). However, the variations of the user accuracy among the methods depended on the urban land-use level. For the Level I land-use classification, the user accuracy was high, except for the transportation land by all methods. In general, the RF and SVM models performed better than the ANN model. For the Level II land-use classification, the user accuracy of different models was quite distinct. With the RF model, the user accuracy of educational and medical land was above 80%. Moreover, with the SVM model, the user accuracy of the business office and educational land classification was above 75%. However, the user accuracy of the ANN model on the Level II land-use classification was poor. Our results showed that the RF model performs best, followed by SVM model, and ANN model was relatively poor in the essential urban land-use classification. The results proved that the use of machine-learning methods can quickly extract land-use types with high accuracy, and provided a better method choice for urban land-use information acquisition