Papers with AudioCaps
Multilingual-To-Multimodal (M2M): Unlocking New Languages with Monolingual Text (2026.findings-eacl)
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| Challenge: | Existing multimodal models rely on machine translation, but performance drops for other languages due to limited multilingual multimodal resources. |
| Approach: | They propose a lightweight alignment method that learns only a few linear layers using English text alone to map multilingual text embeddings into multimodal space. |
| Outcome: | M2M achieves strong zero-shot transfer on XTD Text-to-Image retrieval in English and spanish . it learns only a few linear layers to map multilingual text embeddings into multimodal space . |
Learning to See through Sound: From VggCaps to Multi2Cap for Richer Automated Audio Captioning (2025.emnlp-main)
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| Challenge: | Existing AAC datasets suffer from short and simplistic captions, limiting expressiveness and semantic depth. |
| Approach: | They propose a multi-modal dataset that pairs audio with corresponding video and leverages large language models to generate rich, descriptive captions. |
| Outcome: | The proposed framework outperforms existing benchmarks in caption length, lexical diversity, and human-rated quality. |
e5-omni: Explicit Cross-modal Alignment for Omni-modal Embeddings (2026.findings-acl)
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| Challenge: | Recent omni-modal embeddings rely heavily on implicit alignment from pretrained visionlanguage models. |
| Approach: | They propose a lightweight explicit alignment recipe that adapts off-the-shelf VLMs into robust omni-modal embedding models. |
| Outcome: | The proposed model improves on MMEB-V2 and AudioCaps with a lightweight explicit alignment recipe. |
Omni-Embed-Audio: Leveraging Multimodal LLMs for Robust Audio-Text Retrieval (2026.acl-long)
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| Challenge: | Experiments with AudioCaps, Clotho, and MECAT show that OEA achieves comparable text-to-text retrieval performance to state-of-the-art M2D-CLAP. |
| Approach: | They propose a retrieval-oriented encoder leveraging multimodal LLMs with native audio understanding that allows users to express their queries in five different ways. |
| Outcome: | Experiments on AudioCaps, Clotho, and MECAT show that OEA achieves comparable text-to-audio retrieval performance to state-of-the-art M2D-CLAP while demonstrating clear advantages in two critical areas. |