VisionLLaMA, a vision transformer, merges language and vision modalities. It introduces a tailored architecture, VisionLLaMA, to process 2D images effectively. The design retains LLaMA’s architecture and follows ViT’s pipeline, utilizing innovative features. VisionLLaMA achieves superior performance in various vision tasks, paving the way for further exploration and extending its impact beyond text and vision.
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VisionLLaMA: A Unified Architecture for Vision Tasks
Introducing VisionLLaMA
Large language models, like the LLaMA family, have transformed natural language processing. VisionLLaMA, a vision transformer, brings the same architecture to process 2D images, bridging the gap between language and vision modalities.
Key Aspects of VisionLLaMA
VisionLLaMA processes images through non-overlapping patches and VisionLLaMA blocks, incorporating features such as self-attention via Rotary Positional Encodings (RoPE) and SwiGLU activation. It varies from ViT by relying solely on inherent positional encoding.
VisionLLaMA Variants and Performance
The paper focuses on two versions: plain and pyramid transformers, and assesses its performance in image generation, classification, segmentation, and detection tasks. Results demonstrate its efficiency and adaptability across architectures.
Further Investigations and Implications
The paper proposes VisionLLaMA as an appealing architecture for vision tasks, suggesting possibilities for expanding its capabilities beyond text and vision. Its open-source release promotes cooperative research and creativity in large vision transformers.
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For further details, check out the Paper and Github.
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