Instruction tuning of BLIP-2 with an instruction-aware Query Transformer delivers state-of-the-art zero-shot performance on held-out vision-language datasets and strong finetuned results on downstream tasks.
EV A: Exploring the Limits of Masked Visual Repre- sentation Learning at Scale 2022
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BLIP-2 bootstraps vision-language pre-training from frozen image encoders and LLMs via a lightweight two-stage Querying Transformer, delivering SOTA results with 54x fewer trainable parameters than Flamingo80B on zero-shot VQAv2.
Introduces the XAMI benchmark dataset of 1000 annotated XMM-Newton images for artefact detection together with a hybrid CNN-transformer instance segmentation demonstration.
MiniGPT-4 shows that aligning a frozen vision encoder to Vicuna via one projection layer plus a second-stage detailed-description fine-tune produces GPT-4-like vision-language abilities including detailed captions, creative writing, and instruction following.
EVA-CLIP delivers improved CLIP training recipes that yield 82.0% zero-shot ImageNet-1K accuracy for a 5B-parameter model after only 9 billion samples.
InternVL scales a vision model to 6B parameters and aligns it with LLMs using web data to achieve state-of-the-art results on 32 visual-linguistic benchmarks.
MiniGPT-v2 adds unique task identifiers to a large language model so one system can perform image description, visual question answering, and visual grounding after three-stage training.
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