Papers › mPLUG: Effective and Efficient Vision-Language Learning by Cross-modal Skip-connections
mPLUG: Effective and Efficient Vision-Language Learning by Cross-modal Skip-connections
Chenliang Li, Haiyang Xu, Junfeng Tian, Wei Wang, Ming Yan, Bin Bi, Jiabo Ye, Hehong Chen, Guohai Xu, Zheng Cao, Ji Zhang, Songfang Huang, Fei Huang, Jingren Zhou, Luo Si
Large-scale pretrained foundation models have been an emerging paradigm for building artificial intelligence (AI) systems, which can be quickly adapted to a wide range of downstream tasks. This paper presents mPLUG, a new vision-language foundation model for both cross-modal understanding and generation. Most existing pre-trained models suffer from the problems of low computational efficiency and information asymmetry brought by the long visual sequence in cross-modal alignment. To address these problems, mPLUG introduces an effective and efficient vision-language architecture with novel cross-modal skip-connections, which creates inter-layer shortcuts that skip a certain number of layers for time-consuming full self-attention on the vision side. mPLUG is pre-trained end-to-end on large-scale image-text pairs with both discriminative and generative objectives. It achieves state-of-the-art results on a wide range of vision-language downstream tasks, such as image captioning, image-text retrieval, visual grounding and visual question answering. mPLUG also demonstrates strong zero-shot transferability when directly transferred to multiple video-language 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 Captioning | COCO Captions | mPLUG | BLEU-4 | 46.5 | #1 of 41 | Archive leaderboard | report |
| Image Captioning | COCO Captions | mPLUG | CIDER | 155.1 | #1 of 41 | Archive leaderboard | report |
| Image Captioning | COCO Captions | mPLUG | METEOR | 32.0 | #1 of 41 | Archive leaderboard | report |
| Image Captioning | COCO Captions | mPLUG | SPICE | 26.0 | #1 of 41 | Archive leaderboard | report |
| Visual Question Answering (VQA) | VQA v2 test-dev | mPLUG (Huge) | Accuracy | 82.43 | #5 of 56 | Archive leaderboard | report |
| Visual Question Answering (VQA) | VQA v2 test-std | mPLUG-Huge | number | 69.82 | #2 of 38 | Archive leaderboard | report |
| Visual Question Answering (VQA) | VQA v2 test-std | mPLUG-Huge | other | 77.02 | #2 of 38 | Archive leaderboard | report |
| Visual Question Answering (VQA) | VQA v2 test-std | mPLUG-Huge | overall | 83.62 | #2 of 38 | Archive leaderboard | report |
| Visual Question Answering (VQA) | VQA v2 test-std | mPLUG-Huge | yes/no | 94.83 | #2 of 38 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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