Discover and read the best of Twitter Threads about #BLIP2
Most recents (2)
Introducing 🔥InstructBLIP🔥 - our new Multimodal Foundation Models with instruction tuning on BLIP2, achieving new SOTA results on various VL benchmarks and enjoying various advantages over GPT-4.
Paper: arxiv.org/abs/2305.06500
Code: github.com/salesforce/LAV…
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Paper: arxiv.org/abs/2305.06500
Code: github.com/salesforce/LAV…
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InstructBLIP unlocks a range of diverse multimodal capabilities for building next-generation AI agents, including complex visual scene understanding and reasoning, knowledge-grounded image description, multi-turn visual conversation, etc.
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Built on the success of #BLIP2, InstructBLIP proposes a general instruction-tuning framework, where Q-Former extracts instruction-aware visual features from output embeddings of frozen image encoder, and feeds the visual features as soft prompt input to the frozen LLM.
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Experience the power of LLMs that understand images with the BLIP-2 live demo now running at 🤗 Huggingface Space! Check it out and share your favorite examples #BLIP2 #LLM!
Demo: huggingface.co/spaces/Salesfo…
Project Page: github.com/salesforce/LAV…
Demo: huggingface.co/spaces/Salesfo…
Project Page: github.com/salesforce/LAV…
Useful tips when interacting with BLIP2:
1. Try prompting the model: "tell the story behind the photo:", "Question: what is the name of the person. Answer:";
2. Use proper punctuation with your inputs (?.:);
3. Try different generation options, e.g. sampling methods.
1. Try prompting the model: "tell the story behind the photo:", "Question: what is the name of the person. Answer:";
2. Use proper punctuation with your inputs (?.:);
3. Try different generation options, e.g. sampling methods.
