Marengo 2.7: Breakthrough in Multimodal Video Understanding
TwelveLabs and Amazon Web Services (AWS) announced that Amazon Bedrock would soon offer Marengo and Pegasus, TwelveLabs' cutting-edge multimodal foundation models
Twelve Labs is presenting Marengo 2.7, a cutting-edge multimodal embedding model that outperforms Marengo 2.6 by more than 15%
A novel multi-vector strategy is used by Marengo 2.7 to handle this complexity. It generates distinct vectors for each component of the movie rather than condensing everything into a single vector
TwelveLabs created a comprehensive evaluation system that includes more than 60 different datasets since it recognised the shortcomings of the current benchmarks in capturing real-world use cases
Marengo 2.7 exhibits cutting-edge performance on all major benchmarks, with its image-to-visual search capabilities showing especially noteworthy accomplishments
Marengo-2.7 uses a Transformer-based design that interprets video data using a single, cohesive framework that can comprehend
Marengo-2.7’s distinctive multi-vector representation is one of its main features. Marengo-2.7 breaks down the raw inputs into many specialised vectors, in contrast to Marengo-2.6, which condenses all information into a single embedding
Even though Marengo 2.7 shows notable advancements in a number of modalities, there are still a number of obstacles to overcome before full video comprehension is achieved