The Power of Visual Language Models
Advancements in Language Models
The field of language models has made significant progress, driven by transformers and scaling efforts. OpenAI’s GPT series and innovations like Transformer-XL, Mistral, Falcon, Yi, DeepSeek, DBRX, and Gemini have pushed the capabilities of language models further.
Advancements in Visual Language Models
Visual language models (VLMs) have also rapidly advanced, with models like CLIP, BLIP, BLIP-2, LLaVA, InstructBLIP, Kosmos-2, and PaLI-X enhancing capabilities across various visual tasks.
Enhancing Visual Language Models
Recent advancements in VLMs have focused on aligning visual encoders with large language models to improve capabilities across various visual tasks. Researchers are exploring VLM-based data augmentation to enhance datasets and improve model performance.
Auto-regressive Visual Language Models
The research focuses on auto-regressive Visual Language Models (VLMs), employing a three-stage training paradigm: align-pretrain-SFT. The methodology introduces a novel augmentation training regime to enhance data quality and boost VLM performance.
State-of-the-Art Performance
VILA 2 achieves state-of-the-art performance on various benchmarks, demonstrating the effectiveness of enhanced pre-training data quality and the combination of self-augmented and specialist-augmented training strategies.
Revolutionizing Visual-Language Understanding
VILA 2 represents a significant leap forward in visual language models, achieving state-of-the-art performance through innovative self-augmentation and specialist-augmentation techniques.
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