MMEB
A unified benchmark for evaluating general-purpose multimodal embedding models. It covers tasks such as classification, retrieval, visual question answering and grounding across many datasets, and emphasizes horizontal comparison of different strategies under identical data and training conditions.
Timeline (last 90 days)
A recent study systematically evaluated contrastive learning strategies for multimodal embeddings on the unified benchmark MMEB, finding that most strategies yield only marginal improvements over the standard alignment baseline, with some strategies even dragging down overall performance.
Taking Qwen2-VL-2B as an example, the combined strategy integrating hard negative mining, false-negative mitigation and hard batch construction improves performance on MMEB by about 6.0% on average.
MMEB (Massive Multimodal Embedding Benchmark) is a unified benchmark for evaluating general-purpose multimodal embedding models, covering classification, retrieval, visual question answering, grounding and many other tasks and numerous datasets, emphasizing horizontal comparison of different strategies under the same data and training conditions.
Evaluating multimodal embedding training strategies must return to unified benchmarks and reproducible conditions; improvements reported by different papers under their own settings are often difficult to compare horizontally, and only controlled experiments within a unified framework can expose the true contribution of a strategy.
All Facts (4)
A recent study systematically evaluated contrastive learning strategies for multimodal embeddings on the unified benchmark MMEB, finding that most strategies yield only marginal improvements over the standard alignment baseline, with some strategies even dragging down overall performance.
50%UnverifiedTaking Qwen2-VL-2B as an example, the combined strategy integrating hard negative mining, false-negative mitigation and hard batch construction improves performance on MMEB by about 6.0% on average.
50%UnverifiedMMEB (Massive Multimodal Embedding Benchmark) is a unified benchmark for evaluating general-purpose multimodal embedding models, covering classification, retrieval, visual question answering, grounding and many other tasks and numerous datasets, emphasizing horizontal comparison of different strategies under the same data and training conditions.
50%UnverifiedEvaluating multimodal embedding training strategies must return to unified benchmarks and reproducible conditions; improvements reported by different papers under their own settings are often difficult to compare horizontally, and only controlled experiments within a unified framework can expose the true contribution of a strategy.
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