image-text matching
Supplementary Material for Cross-modal Active Complementary Learning with Self-refining CorrespondenceY ang Qin 1, Yuan Sun
For all parameter settings, see Appendix B.2. The code of our CRCL will be released on GitHub.
Technology:
Cross-modalActiveComplementaryLearning withSelf-refiningCorrespondence
These works attempt to leverage the memorization effect of DNNs [25] to gradually distinguish the noisy image-text pairs for robust learning in a co-teaching manner.
Technology:
- Information Technology > Artificial Intelligence > Representation & Reasoning (1.00)
- Information Technology > Artificial Intelligence > Machine Learning (1.00)
- Information Technology > Artificial Intelligence > Natural Language > Text Processing (0.75)
- Information Technology > Artificial Intelligence > Vision (0.70)
23fa71cc32babb7b91130824466d25a5-Supplemental.pdf
Image-text14 retrieval aims to retrieve the most relevant text from candidate images, or vice versa.
Technology:
- Information Technology > Artificial Intelligence > Vision (1.00)
- Information Technology > Artificial Intelligence > Natural Language (1.00)
- Information Technology > Sensing and Signal Processing > Image Processing (0.93)
- Information Technology > Artificial Intelligence > Machine Learning > Neural Networks > Deep Learning (0.46)
Technology:
Technology:
Technology:
- Information Technology > Artificial Intelligence > Vision (1.00)
- Information Technology > Artificial Intelligence > Natural Language (1.00)
- Information Technology > Sensing and Signal Processing > Image Processing (0.93)
- Information Technology > Artificial Intelligence > Machine Learning > Neural Networks > Deep Learning (0.46)
Technology:
- Information Technology > Artificial Intelligence > Representation & Reasoning (1.00)
- Information Technology > Artificial Intelligence > Machine Learning (1.00)
- Information Technology > Artificial Intelligence > Natural Language > Text Processing (0.75)
- Information Technology > Artificial Intelligence > Vision (0.70)