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트웰브랩스 Marengo 기반 멀티 벡터 영상 검색 가이드
에릭 킴(Eric Kim), 스태프 솔루션 아키텍트 저
Marengo를 활용한 멀티 벡터 쿼리 라우팅 최적화 기술에 대해 심층적으로 분석합니다. 본 논문에서는 극대화된 임베딩(Fused Embeddings) 방식과 멀티 벡터 검색(Multi-vector Retrieval) 방식을 비교하고, 모달리티 가중치를 결정하는 세 가지 전략을 탐구하여 개발자가 자신의 비디오 AI 검색 파이프라인에 가장 적합한 아키텍처를 선택할 수 있도록 돕습니다.
트웰브랩스 Marengo 기반 멀티 벡터 영상 검색 가이드
에릭 킴(Eric Kim), 스태프 솔루션 아키텍트 저
Marengo를 활용한 멀티 벡터 쿼리 라우팅 최적화 기술에 대해 심층적으로 분석합니다. 본 논문에서는 극대화된 임베딩(Fused Embeddings) 방식과 멀티 벡터 검색(Multi-vector Retrieval) 방식을 비교하고, 모달리티 가중치를 결정하는 세 가지 전략을 탐구하여 개발자가 자신의 비디오 AI 검색 파이프라인에 가장 적합한 아키텍처를 선택할 수 있도록 돕습니다.
트웰브랩스 Marengo 기반 멀티 벡터 영상 검색 가이드
에릭 킴(Eric Kim), 스태프 솔루션 아키텍트 저
Marengo를 활용한 멀티 벡터 쿼리 라우팅 최적화 기술에 대해 심층적으로 분석합니다. 본 논문에서는 극대화된 임베딩(Fused Embeddings) 방식과 멀티 벡터 검색(Multi-vector Retrieval) 방식을 비교하고, 모달리티 가중치를 결정하는 세 가지 전략을 탐구하여 개발자가 자신의 비디오 AI 검색 파이프라인에 가장 적합한 아키텍처를 선택할 수 있도록 돕습니다.
