On 190 tasks and a 12-direction cross-modal diagnostic, seven embedding models frequently fail to honor explicit target-modality instructions: retrieval is biased toward the query modality and instruction-induced shifts are misaligned.
Berg, and Mohit Bansal
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VLM2Vec-V2 is a multimodal embedding model trained on an extended MMEB-V2 benchmark that adds video and visual document tasks and reports gains on both new and prior image benchmarks.
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MMEB-V3: Measuring the Performance Gaps of Omni-Modality Embedding Models
On 190 tasks and a 12-direction cross-modal diagnostic, seven embedding models frequently fail to honor explicit target-modality instructions: retrieval is biased toward the query modality and instruction-induced shifts are misaligned.
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VLM2Vec-V2: Advancing Multimodal Embedding for Videos, Images, and Visual Documents
VLM2Vec-V2 is a multimodal embedding model trained on an extended MMEB-V2 benchmark that adds video and visual document tasks and reports gains on both new and prior image benchmarks.