Predict Unmatching Compositions for Compositional Zero-Shot Learning by Soohyeong Kim, Yong Suk Choi

Subjects: “…Compositional zero-shot learning…”
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Zero-Shot Sand-Dust Image Restoration by Fei Shi, Zhenhong Jia, Yanyun Zhou

“…Therefore, it is important to develop a robust sand-dust image restoration method to improve the information processing ability of computer vision. In this paper, we propose a new zero-shot learning method based on an atmospheric scattering physics model to restore sand-dust images. …”
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Advanced Zero-Shot Learning (AZSL) Framework for Secure Model Generalization in Federated Learning by Muhammad Asif, Surayya Naz, Faheem Ali, Amerah Alabrah, Abdu Salam, Farhan Amin, Faizan Ullah

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Zero-Shot Learning for Accurate Project Duration Prediction in Crowdsourcing Software Development by Tahir Rashid, Inam Illahi, Qasim Umer, Muhammad Arfan Jaffar, Waheed Yousuf Ramay, Hanadi Hakami

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ZPVQA: Visual Question Answering of Images Based on Zero-Shot Prompt Learning by Naihao Hu, Xiaodan Zhang, Qiyuan Zhang, Wei Huo, Shaojie You

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Positional embeddings and zero-shot learning using BERT for molecular-property prediction by Medard Edmund Mswahili, JunHa Hwang, Jagath C. Rajapakse, Kyuri Jo, Young-Seob Jeong

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Transductive zero-shot learning via knowledge graph and graph convolutional networks by Qiong Li, Xin Sun, Junyu Dong

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Zero-shot Image Classification Method Based on Discriminator Feedback by FAN Yufei, DING Bo, HE Yongjun

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Adapter With Textual Knowledge Graph for Zero-Shot Sketch-Based Image Retrieval by Jie Zhang, Jiali Tang

“…They aim to retain the knowledge learned by pre-trained models on large-scale datasets, thereby improving the zero-shot generalization ability of models. …”
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A Novel Spectral-Spatial Attention Network for Zero-Shot Pansharpening by Hailiang Lu, Mercedes E. Paoletti, Juan M. Haut, Sergio Moreno-Alvarez, Guangsheng Chen, Weipeng Jing

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A Survey of Zero-Shot Object Detection by Weipeng Cao, Xuyang Yao, Zhiwu Xu, Ye Liu, Yinghui Pan, Zhong Ming

“…Recent advancements in deep learning and increased computational power have led to significant improvements in object detection systems, achieving high recognition accuracy on benchmark datasets. …”
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Zero-Shot Automated Detection of Fake News: An Innovative Approach (ZS-FND) by Rania Baashirah

“…However, these models often struggle with data dependency, overfitting, scalability, flexibility, and high computational costs. A Zero-Shot Learning (ZSL) approach is proposed for Fake News Detection (ZS-FND) to address these challenges. …”
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Zero-Shot Aspect Category Sentiment Analysis Model with Enhanced PPLM Template by FENG Lizhou, LI Mengsha, WANG Youwei, YANG Guijun

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Leveraging the Power of Zero-Shot Learning for Malware Detection Using Application Programming Interface Call Sequences by P. Meena, K. P. Rama Prabha

“…The research results show that deep learning is effective for malware detection and exemplify how online emerging threats can be countered with zero-shot learning.…”
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Hierarchical progressive learning for zero-shot peptide-HLA binding prediction and automated antigenic peptide design by Xinyuan Zhu, Jiadong Lu, Xinting Hu, Tengchuan Jin, Shan Lu, Fuli Feng

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FragMangro: A cross-domain zero-shot model for monitoring fragmented mangrove ecosystems by Ruoxin Zhang, Angela An, Mingcan Cen, Chong Zhang, Aimee Huangfu, Sunny Vinnakota, Shuxiang Song

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Enhancing zero-shot relation extraction with a dual contrastive learning framework and a cross-attention module by Diyou Li, Lijuan Zhang, Jie Huang, Neal Xiong, Lei Zhang, Jian Wan

“…However, the existing ZSRE models ignore the importance of semantic information fusion and possess limitations when used for zero-shot relation extraction tasks. Thus, this paper proposes a dual contrastive learning framework and a cross-attention network module for ZSRE. …”
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Zero‐shot insect detection via weak language supervision by Benjamin Feuer, Ameya Joshi, Minsu Cho, Shivani Chiranjeevi, Zi Kang Deng, Aditya Balu, Asheesh K. Singh, Soumik Sarkar, Nirav Merchant, Arti Singh, Baskar Ganapathysubramanian, Chinmay Hegde

“…In this paper, we demonstrate the power of zero‐shot computer vision methods—a new family of approaches that require (almost) no manual supervision—for plant phenomics applications. …”
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ISAnWin: inductive generalized zero-shot learning using deep CNN for malware detection across windows and android platforms by Umm-e-Hani Tayyab, Faiza Babar Khan, Asifullah Khan, Muhammad Hanif Durad, Farrukh Aslam Khan, Aftab Ali

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Low-Light Image and Video Enhancement for More Robust Computer Vision Tasks: A Review by Mpilo M. Tatana, Mohohlo S. Tsoeu, Rito C. Maswanganyi

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