CoinSeg: Contrast Inter- and Intra- Class Representations for Incremental Segmentation

Class incremental semantic segmentation aims to strike a balance between the model's stability and plasticity by maintaining old knowledge while adapting to new concepts. However, most state-of-the-art methods use the freeze strategy for stability, which compromises the model's plasticity.In contrast, releasing parameter training for plasticity could lead to the best performance for all categories, but this requires discriminative feature representation.Therefore, we prioritize the model's plasticity and propose the Contrast inter- and intra-class representations for Incremental Segmentation (CoinSeg), which pursues discriminative representations for flexible parameter tuning. Inspired by the Gaussian mixture model that samples from a mixture of Gaussian distributions, CoinSeg emphasizes intra-class diversity with multiple contrastive representation centroids. Specifically, we use mask proposals to identify regions with strong objectness that are likely to be diverse instances/centroids of a category. These mask proposals are then used for contrastive representations to reinforce intra-class diversity. Meanwhile, to avoid bias from intra-class diversity, we also apply category-level pseudo-labels to enhance category-level consistency and inter-category diversity. Additionally, CoinSeg ensures the model's stability and alleviates forgetting through a specific flexible tuning strategy. We validate CoinSeg on Pascal VOC 2012 and ADE20K datasets with multiple incremental scenarios and achieve superior results compared to previous state-of-the-art methods, especially in more challenging and realistic long-term scenarios. Code is available at https://github.com/zkzhang98/CoinSeg.Comment: Accepted by ICCV 202

Analysis of Communication Strategies and Approaches of Social Smart Elderly Caring Service Platform

With the development of Internet technology and the intensification of population aging, whether to provide effective smart old-age service security for the elderly has become a social public issue of concern. Through convenient Internet information technology, build an Internet communication platform for smart elderly caring services, and provide comprehensive care and convenience for the elderly with the help of elderly care information dissemination and community mutual assistance in the platform, in order to improve the quality of life of the elderly and the level of social elderly care services, and promote the development of community elderly care services and the elderly silver industry chain. Therefore, aiming at the possible problems in the information communication process of the social smart elderly caring service platform, this paper explores the effective communication strategies and approaches of the social smart elderly caring service platform, which has practical social significance and value

CoinSeg: Contrast Inter- and Intra- Class Representations for Incremental Segmentation

Class incremental semantic segmentation aims to strike a balance between the model’s stability and plasticity by maintaining old knowledge while adapting to new concepts. However, most state-of-the-art methods use the freeze strategy for stability, which compromises the model’s plasticity. In contrast, releasing parameter training for plasticity could lead to the best performance for all categories, but this requires discriminative feature representation. Therefore, we prioritize the model’s plasticity and propose the Contrast inter- and intra-class representations for Incremental Segmentation (CoinSeg), which pursues discriminative representations for flexible parameter tuning. Inspired by the Gaussian mixture model that samples from a mixture of Gaussian distributions, CoinSeg emphasizes intra-class diversity with multiple contrastive representation centroids. Specifically, we use mask proposals to identify regions with strong objectness that are likely to be diverse instances/centroids of a category. These mask proposals are then used for contrastive representations to reinforce intra-class diversity. Meanwhile, to avoid bias from intra-class diversity, we also apply category-level pseudo-labels to enhance category-level consistency and inter-category diversity. Additionally, CoinSeg ensures the model’s stability and alleviates forgetting through a specific flexible tuning strategy. We validate CoinSeg on Pascal VOC 2012 and ADE20K datasets with multiple incremental scenarios and achieve superior results compared to previous state-of-the-art methods, especially in more challenging and realistic long-term scenarios. Code is available at https://github.com/zkzhang98/CoinSeg

HybPSF: Hybrid PSF reconstruction for the observed JWST NIRCam image

The James Webb Space Telescope (JWST) ushers in a new era of astronomical observation and discovery, offering unprecedented precision in a variety of measurements such as photometry, astrometry, morphology, and shear measurement. Accurate point spread function (PSF) models are crucial for many of these measurements. In this paper, we introduce a hybrid PSF construction method called HybPSF for JWST NIRCam imaging data. HybPSF combines the WebbPSF software, which simulates the PSF for JWST, with observed data to produce more accurate and reliable PSF models. We apply this method to the SMACS J0723 imaging data and construct supplementary structures from residuals obtained by subtracting the WebbPSF PSF model from the data. Our results show that HybPSF significantly reduces discrepancies between the PSF model and the data compared to WebbPSF. Specifically, the PSF shape parameter ellipticity and size comparisons indicate that HybPSF improves precision by a factor of approximately 10 for \$R^2\$ and \$50\%\$ for \$e\$. This improvement has important implications for astronomical measurements using JWST NIRCam imaging data

Influence of Vegetation Coverage on Hydraulic Characteristics of Overland Flow

Soil erosion is a major problem in the Loess Plateau (China); however, it can be alleviated through vegetation restoration. In this study, the overland flow on a slope during soil erosion was experimentally simulated using artificial grass as vegetation cover. Nine degrees of vegetation coverage and seven flow rates were tested in combinations along a 12° slope gradient. As the coverage degree increased, the water depth of the overland flow increased, but the flow velocity decreased. The resistance coefficient increased with increasing degree of coverage, especially after a certain point. The resistance coefficient and the Reynolds number had an inverse relationship. When the Reynolds number was relatively small, the resistance coefficient decreased faster; however, when it exceeded 600, the resistance coefficient decreased at a slower rate. A critical degree of vegetation cover was observed in the relationship between the resistance coefficient and submergence degree. When the degree of coverage was greater than 66.42%, the resistance coefficient first decreased and then increased with a higher submergence degree. Finally, the formula for the resistance coefficient under vegetation coverage was derived. This formula has a relatively high accuracy and can serve as a reference for predicting soil erosion