M5Product: A Multi-modal Pretraining Benchmark for E-commercial Product Downstream Tasks

By Xiao Dong, Xunlin Zhan, Yangxin Wu, Yunchao Wei, Xiaoyong Wei, Minlong Lu, Xiaodan Liang. 2021

9 citations recorded by the source corpus.

Subfields: Multimodal Learning, Evaluation & Benchmarks

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Multimodal Learning

1. Guangyao Chen
2. Guangwu Qian
3. Pengcheng Gao
4. Xiao Wang
5. Xiaoyong Wei
6. Wen Gao
7. Yonghong Tian
8. Xiaodan Liang

In Evaluation & Benchmarks

1. Lewei Yao
2. Chunjing Xu
3. Xin Jiang
4. Xiao Wang
5. Wen Gao
6. Yonghong Tian
7. Xiaodan Liang
8. Hang Xu

Citations in the corpus

• FILIP: Fine-grained Interactive Language-Image Pre-Training (2021, background): “(1) One line of work (Chen et al., 2020; Li et al., 2020b; Dong et al., 2021; Li et al., 2021b; Zhang et al., 2021; Zhan et al., 2021) uses a pre-trained object detector to extract region-of-interest (ROI) features from images, and then fuses it with the paired text through a VLP model.”
• Large-scale Multi-modal Pre-trained Models: A Comprehensive Survey (2023, background): “25 M6-Corpus[100] 2021 60 M Image, image-text Chinese × –”