@inproceedings{
wang2024nft,
title={{NFT}1000: A Cross-Modal Dataset For Non-Fungible Token Retrieval},
author={Shuxun Wang and Yunfei Lei and Ziqi Zhang and Wei Liu and Haowei Liu and Li Yang and Bing Li and Wenjuan Li and Jin Gao and Weiming Hu},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=xUtNrKH8iB}
}
With the rise of "Metaverse" and "Web 3.0", Non-Fungible Tokens (NFTs) have emerged as a kind of pivotal digital asset, garnering significant attention. By the end of March 2024, more than 1.7 billion NFTs have been minted across various blockchain platforms. To effectively locate a desired NFT token, conducting searches within the huge amount NFTs is essential. The challenge in NFT retrieval is heightened due to the high degree of similarity among different NFTs regarding regional and semantic aspects. In this paper, we introduce a dataset named “NFT Top1000 Visual-Text Dataset”(NFT1000), containing 7.56 million image-text pairs, and being collected from 1000 most famous PFP NFT collections by sales volume on the Ethereum blockchain. Based on this dataset, building upon the foundation of the CLIP series of pre-trained models, we propose a dynamic masking fine-grained contrastive learning fine-tuning approach, which enables us to fine-tune a more performant model using only 13% of the total training data (0.79 million v.s. 6.1 million), resulting in a 7.2% improvement in the top-1 accuracy rate. We also propose a robust metric Comprehensive Variance Index (CVI) to assess the similarity and retrieval difficulty of visual-text pairs data. Please try our retrieval demo at https://876p9s4054.vicp.fun/
NFT1000: A Cross-Modal Dataset For Non-Fungible Token Retrieval
Shuxun Wang Yunfei Lei Ziqi Zhang Wei Liu Haowei Liu Li Yang Bing Li Wenjuan Li Jin Gao Weiming Hu