BioVITA: Biological Dataset, Model, and Benchmark for Visual-Textual-Acoustic Alignment
Understanding animal species from multimodal data poses an emerging challenge at the intersection of computer vision and ecology. While recent biological models, such as BioCLIP, have demonstrated strong alignment between images and textual taxonomic information for species identification, the integration of the audio modality remains an open problem. We propose BioVITA, a novel visual-textual-acoustic alignment framework for biological applications. BioVITA involves (i) a training dataset, (ii) a representation model, and (iii) a retrieval benchmark. First, we construct a large-scale training dataset comprising 1.3 million audio clips and 2.3 million images, covering 14,133 species annot...
| Authors | Risa Shinoda, Kaede Shiohara, Nakamasa Inoue et al. |
|---|---|
| Primary category | cs.CV |
| Published | 25 March 2026 |
| Last updated at source | 25 March 2026 |
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