Papers › OPT: Omni-Perception Pre-Trainer for Cross-Modal Understanding and Generation
OPT: Omni-Perception Pre-Trainer for Cross-Modal Understanding and Generation
Jing Liu, Xinxin Zhu, Fei Liu, Longteng Guo, Zijia Zhao, Mingzhen Sun, Weining Wang, Hanqing Lu, Shiyu Zhou, Jiajun Zhang, Jinqiao Wang
In this paper, we propose an Omni-perception Pre-Trainer (OPT) for cross-modal understanding and generation, by jointly modeling visual, text and audio resources. OPT is constructed in an encoder-decoder framework, including three single-modal encoders to generate token-based embeddings for each modality, a cross-modal encoder to encode the correlations among the three modalities, and two cross-modal decoders to generate text and image respectively. For the OPT's pre-training, we design a multi-task pretext learning scheme to model multi-modal resources from three different data granularities, \ie, token-, modality-, and sample-level modeling, through which OPT learns to align and translate among different modalities. The pre-training task is carried out on a large amount of image-text-audio triplets from Open Images. Experimental results show that OPT can learn strong image-text-audio multi-modal representations and achieve promising results on a variety of cross-modal understanding and generation tasks.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Image Retrieval | Localized Narratives | OPT | Text-to-image R@1 | 0.4196 | #1 of 1 | Archive leaderboard | report |
| Image Retrieval | Localized Narratives | OPT | Text-to-image R@10 | 0.8126 | #1 of 1 | Archive leaderboard | report |
| Image Retrieval | Localized Narratives | OPT | Text-to-image R@5 | 0.72 | #1 of 1 | Archive leaderboard | report |
| Text to Audio Retrieval | Localized Narratives | OPT | Text-to-audio R@1 | 0.78 | #1 of 1 | Archive leaderboard | report |
| Text to Audio Retrieval | Localized Narratives | OPT | Text-to-audio R@10 | 0.958 | #1 of 1 | Archive leaderboard | report |
| Text to Audio Retrieval | Localized Narratives | OPT | Text-to-audio R@5 | 0.927 | #1 of 1 | Archive leaderboard | report |
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