Papers › Omnivore: A Single Model for Many Visual Modalities
Omnivore: A Single Model for Many Visual Modalities
Rohit Girdhar, Mannat Singh, Nikhila Ravi, Laurens van der Maaten, Armand Joulin, Ishan Misra
Prior work has studied different visual modalities in isolation and developed separate architectures for recognition of images, videos, and 3D data. Instead, in this paper, we propose a single model which excels at classifying images, videos, and single-view 3D data using exactly the same model parameters. Our 'Omnivore' model leverages the flexibility of transformer-based architectures and is trained jointly on classification tasks from different modalities. Omnivore is simple to train, uses off-the-shelf standard datasets, and performs at-par or better than modality-specific models of the same size. A single Omnivore model obtains 86.0% on ImageNet, 84.1% on Kinetics, and 67.1% on SUN RGB-D. After finetuning, our models outperform prior work on a variety of vision tasks and generalize across modalities. Omnivore's shared visual representation naturally enables cross-modal recognition without access to correspondences between modalities. We hope our results motivate researchers to model visual modalities together.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Action Classification | Kinetics-400 | OMNIVORE (Swin-L) | Acc@1 | 84.1 | #62 of 207 | Archive leaderboard | report |
| Action Classification | Kinetics-400 | OMNIVORE (Swin-L) | Acc@5 | 96.1 | #62 of 207 | Archive leaderboard | report |
| Action Classification | Kinetics-400 | OMNIVORE (Swin-B) | Acc@1 | 84.0 | #63 of 207 | Archive leaderboard | report |
| Action Classification | Kinetics-400 | OMNIVORE (Swin-B) | Acc@5 | 96.2 | #63 of 207 | Archive leaderboard | report |
| Action Recognition | EPIC-KITCHENS-100 | OMNIVORE (Swin-B, finetuned) | Action@1 | 49.9 | #9 of 32 | Archive leaderboard | report |
| Action Recognition | EPIC-KITCHENS-100 | OMNIVORE (Swin-B, finetuned) | Noun@1 | 61.7 | #9 of 32 | Archive leaderboard | report |
| Action Recognition | EPIC-KITCHENS-100 | OMNIVORE (Swin-B, finetuned) | Verb@1 | 69.5 | #9 of 32 | Archive leaderboard | report |
| Action Recognition | Something-Something V2 | OMNIVORE (Swin-B, IN-21K+ Kinetics400 pretrain) | Top-1 Accuracy | 71.4 | #29 of 123 | Archive leaderboard | report |
| Action Recognition | Something-Something V2 | OMNIVORE (Swin-B, IN-21K+ Kinetics400 pretrain) | Top-5 Accuracy | 93.5 | #29 of 123 | Archive leaderboard | report |
| Image Classification | ImageNet | Omnivore (Swin-L) | Top 1 Accuracy | 86.0% | #177 of 1060 | Archive leaderboard | report |
| Image Classification | ImageNet | Omnivore (Swin-B) | Top 1 Accuracy | 85.3% | #239 of 1060 | Archive leaderboard | report |
| Image Classification | iNaturalist 2018 | OMNIVORE (Swin-L) | Top-1 Accuracy | 84.1% | #11 of 60 | Archive leaderboard | report |
| Scene Recognition | SUN-RGBD | OMNIVORE (Swin-B) | Accuracy (%) | 67.2 | #1 of 2 | Archive leaderboard | report |
| Semantic Segmentation | NYU Depth v2 | OMNIVORE (Swin-L, finetuned) | Mean IoU | 56.8% | #19 of 121 | Archive leaderboard | report |
| Semantic Segmentation | NYU Depth v2 | OMNIVORE (Swin-B, finetuned) | Mean IoU | 55.1% | #28 of 121 | 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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