Papers › Omnivore: A Single Model for Many Visual Modalities

Omnivore: A Single Model for Many Visual Modalities

20 Jan 2022CVPR 2022 1arXiv:2201.08377archive 2025-07-28

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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OmnivoreModel facebookresearch/omnivore/omnivore/models/omnivore_model.py official repository ran · metamorphic tier: deterministic licence not identified · pointer only · 9b5687fccc4bf734 · report
OmnivoreModel towhee-io/towhee/towhee/models/omnivore/omnivore.py community (archive-listed) ran Apache-2.0 (permissive) · d92bf522f8f7f64f · report

Tasks

Action ClassificationAction RecognitionImage ClassificationScene RecognitionSemantic Segmentationmodel

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
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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