Zhiqiang Shen

Sep 24, 2024 | onlinelibrary.wiley.com | Pengyang Wang |Donghao Wu |Yongjia Wang |Zhiqiang Shen

CONFLICT OF INTEREST STATEMENT The authors declare no competing interests. Supporting Information Filename Description jpe14786-sup-0001-Supinfo.docxWord 2007 document , 4.1 MB Table Se1.1. The model fit with abundance data. Table Se1.2. The model fit with abundance data with inversing the path of RIFA to CNA (poor fit). Table Se1.3. The model fit with mound number data. Table Se1.4. The model fit with absence/presence data.

Jul 24, 2024 | kirill-vish.github.io | Zhiqiang Shen

ConvNet vs Transformer, Supervised vs CLIP: Beyond ImageNet Accuracy Kirill Vishniakov¹ Zhiqiang Shen¹ Zhuang Liu² Modern computer vision offers a great variety of models to practitioners, and selecting a model from multiple options for specific applications can be challenging. Conventionally, competing model architectures and training protocols are compared by their classification accuracy on ImageNet.

Feb 29, 2024 | sciencedirect.com | Xinyi Deng |Yuewei Liu |Zhiqiang Shen |Daiju Tao

Due to the detrimental effects on human health, bisphenol A (BPA) is gradually being banned and replaced in numerous countries(Huang et al., 2018); however, its alternatives are increasingly being used. Bisphenol F (BPF), one of the major alternatives to BPA, is an industrial component commonly used in synthesis of polycarbonate plastics and epoxy resin(Moon et al., 2023). Owing to its extensive production and wide-ranging applications, BPF has been widely detected in various environmental media.

Nov 30, 2023 | arxiv.org | Zhiqiang Shen

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Nov 15, 2023 | arxiv.org | Zhiqiang Shen |Zhuang Liu

[Submitted on 15 Nov 2023 ( v1 ), last revised 5 Jan 2024 (this version, v2)] Title:ConvNet vs Transformer, Supervised vs CLIP: Beyond ImageNet Accuracy Download a PDF of the paper titled ConvNet vs Transformer, Supervised vs CLIP: Beyond ImageNet Accuracy, by Kirill Vishniakov and 2 other authors Download PDF HTML (experimental) Abstract:Modern computer vision offers a great variety of models to practitioners, and selecting a model from multiple options for specific applications can be...