Ashish Patel 🇮🇳’s Post

𝗗𝗮𝘆-𝟰𝟭𝟱 𝗖𝗼𝗺𝗽𝘂𝘁𝗲𝗿 𝗩𝗶𝘀𝗶𝗼𝗻 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 VLP: A Survey on Vision-Language Pre-training by CASIA Follow me for a similar post: Ashish Patel ------------------------------------------------------------------- 𝗜𝗻𝘁𝗲𝗿𝗲𝘀𝘁𝗶𝗻𝗴 𝗙𝗮𝗰𝘁𝘀 : 🔸This paper is published arxiv2022. 👉 VLP survey review its recent advances from five aspects: feature extrac- tion, model architecture, pre-training objectives, pre-training datasets, and downstream tasks and summarize the specific SOTA VLP models in detail. ------------------------------------------------------------------- 𝗜𝗠𝗣𝗢𝗥𝗧𝗔𝗡𝗖𝗘 ✔️ In the past few years, the emergence of pre-training models has brought uni-modal fields such as computer vision (CV) and natural language processing (NLP) to a new era. ✔️ Substantial works have shown they are beneficial for downstream uni-modal tasks and avoid training a new model from scratch. ✔️ So can such pre-trained models be applied to multi-modal tasks? Researchers have explored this problem and made significant progress. ✔️ This paper surveys recent advances and new frontiers in vision-language pre-training (VLP), including image-text and video-text pre-training. ✔️ To give readers a better overall grasp of VLP, we first review its recent advances from five aspects: feature extraction, model architecture, pre-training objectives, pre-training datasets, and downstream tasks. ✔️ Then, we summarize the specific VLP models in detail. Finally, we discuss the new frontiers in VLP. To the best of our knowledge, this is the first survey on VLP. We hope that this survey can shed light on future research in the VLP field. #computervision #artificialintelligence #data