RGB-Infrared (RGB-IR) cross-modality person re-identification (re-ID) is attracting more and attention due to requirements for 24-h scene surveillance. However, the high cost of labeling identities an RGB-IR dataset largely limits scalability supervised models in real-world scenarios. In this paper, we study unsupervised re-ID problem (or briefly uRGB-IR re-ID) which no identity annotations are available datasets. Considering that intra-modality (i.e., RGB-RGB or IR-IR) much easier than can...

Text-to-image generative models can produce diverse high-quality images of concepts with a text prompt, which have demonstrated excellent ability in image generation, translation, etc. We this work study the problem synthesizing instantiations user's own never-ending manner, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.,</i> create your world, where new from user are quickly learned few examples. To achieve goal, we propose <underline...

Most of the existing person re-identification (re-ID) methods achieve promising accuracy in a supervised manner, but they assume identity labels target domain is available. This greatly limits scalability re-ID real-world scenarios. Therefore, current community focuses on cross-domain that aims to transfer knowledge from labeled source an unlabeled and exploits specific data distribution further improve performance. To reduce gap between domains, we propose Smoothing Adversarial Domain...

This study investigates the relationship between internalized stigmatization brought on by epicenter travel experiences and mental health problems (including anxiety, depression, shame) during period of novel coronavirus disease emergency in China. The cross-sectional data were collected using time-lag design to avoid common method bias as much possible. Regression results structural equation modeling show that may have positive relationships with (i.e., shame), such can be moderated social...

Class-incremental learning (CIL) has achieved remarkable successes in new classes consecutively while overcoming catastrophic forgetting on old categories. However, most existing CIL methods unreasonably assume that all categories have the same pace, and neglect negative influence of heterogeneity among different compensation. To surmount above challenges, we develop a novel Heterogeneous Forgetting Compensation (HFC) model, which can resolve heterogeneous easy-to-forget hard-to-forget from...

Cross-domain transfer learning (CDTL) is an extremely challenging task for the person re-identification (ReID). Given a source domain with annotations and target without annotations, CDTL seeks effective method to knowledge from domain. However, such simple two-domain unavailable ReID in that source/target consists of several sub-domains, e.g., camera-based sub-domains. To address this intractable problem, we propose novel Many-to-Many Generative Adversarial Transfer Learning (M2M-GAN) takes...

With many social challenges posed by an ageing population, the delayed retirement initiative has received wide attention from policymakers. However, China's current multi-level health insurance system seems not perfect and ready for initiative. The public are generally concerned that benefits of late retirees cannot be well guaranteed. Using data China Health Retirement Longitudinal Study (CHARLS) chorological design (CHARLS-2015 -2018 waves), this study finds (1) could beneficial physical...

The delayed retirement initiative and population aging have led to a growing group of late retirees. However, it remains unclear whether the existing employment-based health insurance system can effectively match recently proposed support retirees, especially those with pre-existing function limitations. Thus, this study aims investigate influencing mechanism China's Urban Employee Basic Medical Insurance (UEBMI), physical functioning limitation (PFL) difficulty in instrumental activities...

Maximizing visual effect is a major problem in real-time animation. A hair animation framework was proposed previously by C. K. Koh and Z. Huang (2000, 2001) based on 2D representation texture mapping. One that it lacks of the volumetric due to its nature. This paper presents technique using U-shape strip solve problem.

Objective: The practice of parallel multiple jobs has increasingly become a global trend. However, the effects on physical and mental health have not been well understood. Method: Data come from China Health Retirement Longitudinal Study published by CHARLS in 2015. agricultural population aged 45 years old above are selected through stratified random sampling ( N = 10,118). Robust regression method is used to give robust estimation. Results: U-shape relations found. modest increase number...

Class-incremental learning (CIL) has achieved remarkable successes in new classes consecutively while overcoming catastrophic forgetting on old categories. However, most existing CIL methods unreasonably assume that all categories have the same pace, and neglect negative influence of heterogeneity among different compensation. To surmount above challenges, we develop a novel Heterogeneous Forgetting Compensation (HFC) model, which can resolve heterogeneous easy-to-forget hard-to-forget from...

Relying on large language models (LLMs), embodied robots could perform complex multimodal robot manipulation tasks from visual observations with powerful generalization ability. However, most behavior-cloning agents suffer performance degradation and skill knowledge forgetting when adapting into a series of challenging unseen tasks. We here investigate the above challenge NBCagent in robots, pioneering language-conditioned Never-ending Behavior-Cloning agent, which can continually learn...

Pre-trained large text-to-image (T2I) models with an appropriate text prompt has attracted growing interests in customized images generation field. However, catastrophic forgetting issue make it hard to continually synthesize new user-provided styles while retaining the satisfying results amongst learned styles. In this paper, we propose MuseumMaker, a method that enables synthesis of by following set never-end manner, and gradually accumulate these creative artistic works as Museum. When...

Custom diffusion models (CDMs) have attracted widespread attention due to their astonishing generative ability for personalized concepts. However, most existing CDMs unreasonably assume that concepts are fixed and cannot change over time. Moreover, they heavily suffer from catastrophic forgetting concept neglect on old when continually learning a series of new To address these challenges, we propose novel Concept-Incremental text-to-image Diffusion Model (CIDM), which can resolve learn...

ABSTRACT Objective: The effectiveness of air traffic restriction in containing the spread infectious diseases is full controversy prior literature. In January 2020, Civil Aviation Administration China (CAAC) announced response to coronavirus disease (COVID-19) pandemic. This study’s aim empirically examine policy effectiveness. Method: data from 2 third-party platforms are used this investigation. COVID-19 platform DXY and Airsavvi matched each other. robust panel regression with controlling...

3D object detection have achieved significant performance in many fields, e.g., robotics system, autonomous driving, and augmented reality. However, most existing methods could cause catastrophic forgetting of old classes when performing on the class-incremental scenarios. Meanwhile, current neglect relationships between localization information category semantic information, assume all knowledge model is reliable. To address above challenge, we present a novel Incremental Object Detection...

Text-to-image generative models can produce diverse high-quality images of concepts with a text prompt, which have demonstrated excellent ability in image generation, translation, etc. We this work study the problem synthesizing instantiations use's own never-ending manner, i.e., create your world, where new from user are quickly learned few examples. To achieve goal, we propose Lifelong text-to-image Diffusion Model (L2DM), intends to overcome knowledge "catastrophic forgetting" for past...

With the increase of real-world scenarios such as robotics, urban rescue and autonomous driving, deep learning models are increasingly exposed to open-set where established methods should separate known unknown categories in real world. However, most existing recognition treat all features equally focus on that facilitate discrimination during training, which is detrimental performance open In response this challenge, we propose a novel framework based Cross-Semantic Attention Network (i.e.,...

3D object detection has achieved significant performance in many fields, e.g., robotics system, autonomous driving, and augmented reality. However, most existing methods could cause catastrophic forgetting of old classes when performing on the class-incremental scenarios. Meanwhile, current neglect relationships between localization information category semantic assume all knowledge model is reliable. To address above challenge, we present a novel Incremental Object Detection framework with...