Multimodal embeddings expand search across visual content
In a post published on August 25, 2026, Tencent describes a model that represents text, images, video, and visual documents in a shared space and claims top results on two benchmarks.
Source: WeMM-Embedding-9B, da Tencent, mapeia várias modalidades em um espaço compartilhado (huggingface.co). Text prepared with AI from this source.
What happened and what to do
In a post published on August 25, 2026, Tencent presents WeMM-Embedding-9B, which maps text, images, video, and visual documents into a shared embedding space. The post claims top results on the MMEB-v2 and MMEB-v3 benchmarks and points to its use in multimodal retrieval.
A company could use this approach to find images, video, and visual documents with text queries, or combine textual and visual results in one search. Implementation could include ingesting and indexing existing collections, a query API, and metrics to evaluate relevance, coverage, and performance on representative operational data.
How the consultancy can help
Wendelmaques can assess your collections, search use cases, and security requirements; scope an implementation for multimodal ingestion, indexing, and queries; and support its operation and monitoring.
Next step
Send a brief description of your collection and what you need to find to receive a scoped proposal.
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I reply with scope and a proposal.