PolyViT: Co-training Vision Transformers on Images, Videos and Audio

• URL: http://arxiv.org/abs/2111.12993v1
• Date: Thu, 25 Nov 2021 10:01:05 GMT
• Title: PolyViT: Co-training Vision Transformers on Images, Videos and Audio
• Authors: Valerii Likhosherstov, Anurag Arnab, Krzysztof Choromanski, Mario Lucic, Yi Tay, Adrian Weller, Mostafa Dehghani
• Abstract summary: We present PolyViT, a model trained on image, audio and video. By co-training different tasks on a single modality, we are able to improve the accuracy of each individual task. We show that co-training is simple and practical to implement.
• Score: 80.0913507142036
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Can we train a single transformer model capable of processing multiple modalities and datasets, whilst sharing almost all of its learnable parameters? We present PolyViT, a model trained on image, audio and video which answers this question. By co-training different tasks on a single modality, we are able to improve the accuracy of each individual task and achieve state-of-the-art results on 5 standard video- and audio-classification datasets. Co-training PolyViT on multiple modalities and tasks leads to a model that is even more parameter-efficient, and learns representations that generalize across multiple domains. Moreover, we show that co-training is simple and practical to implement, as we do not need to tune hyperparameters for each combination of datasets, but can simply adapt those from standard, single-task training.

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