BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding&Generation
308#blip #review #ai
Cross-modal pre-training has been all the rage lately in deep learning, especially training vision and language models together. However, there are a number of issues, such as low quality datasets that limit the performance of any model trained on it, and also the fact that pure contrastive pre-training cannot be easily fine-tuned for most downstream tasks. BLIP unifies different tasks and objectives in a single pre-training run and achieves a much more versatile model, which the paper immediately uses to create, filter, clean and thus bootstrap its own dataset to improve performance even more!
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OUTLINE:
0:00 - Intro
0:50 - Sponsor: Zeta Alpha
3:40 - Paper Overview
6:40 - Vision-Language Pre-Training
11:15 - Contributions of the paper
14:30 - Model architecture: many parts for many tasks
19:50 - How data flows in the model
26:50 - Parameter sharing between the modules
29:45 - Captioning & Filtering bootstrapping
41:10 - Fine-tuning the model for downstream tasks
Paper: https://arxiv.org/abs/2201.12086
Code: https://github.com/salesforce/BLIP
Demo: https://huggingface.co/spaces/Salesforce/BLIP
Abstract:
Vision-Language Pre-training (VLP) has advanced the performance for many vision-language tasks. However, most existing pre-trained models only excel in either understanding-based tasks or generation-based tasks. Furthermore, performance improvement has been largely achieved by scaling up the dataset with noisy image-text pairs collected from the web, which is a suboptimal source of supervision. In this paper, we propose BLIP, a new VLP framework which transfers flexibly to both vision-language understanding and generation tasks. BLIP effectively utilizes the noisy web data by bootstrapping the captions, where a captioner generates synthetic captions and a filter removes the noisy ones. We achieve state-of-the-art results on a wide range of vision-language tasks, such as image-text retrieval (+2.7% in average recall@1), image captioning (+2.8% in CIDEr), and VQA (+1.6% in VQA score). BLIP also demonstrates strong generalization ability when directly transferred to video-language tasks in a zero-shot manner. Code, models, and datasets are released at this https URL.
Authors: Junnan Li, Dongxu Li, Caiming Xiong, Steven Hoi
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