Exciting breakthrough in multimodal search technology! 
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Exciting breakthrough in multimodal search technology! 
@nvidia
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nvidia
 researchers have developed MM-Embed, a groundbreaking universal multimodal retrieval system that's changing how we think about search.
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 researchers have developed MM-Embed, a groundbreaking universal multimodal retrieval system that's changing how we think about search.
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Key innovations:
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Key innovations:
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• First-ever universal multimodal retriever that excels at both text and image searches across diverse tasks
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• First-ever universal multimodal retriever that excels at both text and image searches across diverse tasks
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• Leverages advanced multimodal LLMs to understand complex queries combining text and images
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• Leverages advanced multimodal LLMs to understand complex queries combining text and images
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• Implements novel modality-aware hard negative mining to overcome modality bias issues
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• Implements novel modality-aware hard negative mining to overcome modality bias issues
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• Achieves state-of-the-art performance on M-BEIR benchmark while maintaining superior text retrieval capabilities
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• Achieves state-of-the-art performance on M-BEIR benchmark while maintaining superior text retrieval capabilities
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Under the hood:
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Under the hood:
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The system uses a sophisticated bi-encoder architecture with LLaVa-Next (based on Mistral 7B) as its backbone. It employs a unique two-stage training approach: first with random negatives, then with carefully mined hard negatives to improve cross-modal understanding.
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The system uses a sophisticated bi-encoder architecture with LLaVa-Next (based on Mistral 7B) as its backbone. It employs a unique two-stage training approach: first with random negatives, then with carefully mined hard negatives to improve cross-modal understanding.
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The real magic happens in the modality-aware negative mining, where the system learns to distinguish between incorrect modality matches and unsatisfactory information matches, ensuring retrieved results match both content and format requirements.
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The real magic happens in the modality-aware negative mining, where the system learns to distinguish between incorrect modality matches and unsatisfactory information matches, ensuring retrieved results match both content and format requirements.
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What sets it apart is its ability to handle diverse search scenarios - from simple text queries to complex combinations of images and text, all while maintaining high accuracy across different domains
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What sets it apart is its ability to handle diverse search scenarios - from simple text queries to complex combinations of images and text, all while maintaining high accuracy across different domains
Exciting breakthrough in multimodal search technology! @nvidia researchers have developed MM-Embed, a groundbreaking universal multimodal retrieval system that's changing how we think about search.

Key innovations:
• First-ever universal multimodal retriever that excels at both text and image searches across diverse tasks
• Leverages advanced multimodal LLMs to understand complex queries combining text and images
• Implements novel modality-aware hard negative mining to overcome modality bias issues
• Achieves state-of-the-art performance on M-BEIR benchmark while maintaining superior text retrieval capabilities

Under the hood:
The system uses a sophisticated bi-encoder architecture with LLaVa-Next (based on Mistral 7B) as its backbone. It employs a unique two-stage training approach: first with random negatives, then with carefully mined hard negatives to improve cross-modal understanding.

The real magic happens in the modality-aware negative mining, where the system learns to distinguish between incorrect modality matches and unsatisfactory information matches, ensuring retrieved results match both content and format requirements.

What sets it apart is its ability to handle diverse search scenarios - from simple text queries to complex combinations of images and text, all while maintaining high accuracy across different domains
👀
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