Methods › Computer Vision › Multi-Modal Methods › VATT

VATT

3 papers tagged archive 2025-07-28

Introduced by Hassan Akbari et al. in VATT: Transformers for Multimodal Self-Supervised Learning from Raw Video, Audio and Text

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

Video-Audio-Text Transformer, or VATT, is a framework for learning multimodal representations from unlabeled data using convolution-free Transformer architectures. Specifically, it takes raw signals as inputs and extracts multidimensional representations that are rich enough to benefit a variety of downstream tasks. VATT borrows the exact architecture from BERT and ViT except the layer of tokenization and linear projection reserved for each modality separately. The design follows the same spirit as ViT that makes the minimal changes to the architecture so that the learned model can transfer its weights to various frameworks and tasks.

VATT linearly projects each modality into a feature vector and feeds it into a Transformer encoder. A semantically hierarchical common space is defined to account for the granularity of different modalities and noise contrastive estimation is employed to train the model.

PaperSource

Papers archive 2025-07-28

3 shown of 3, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

18 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Action Classification1
Action Recognition1
Action Recognition In Videos1
Audio Classification1
Audio Generation1
Audio captioning1
Contrastive Learning1
General Classification1
Image Classification1
Retrieval1
Self-Supervised Learning1
Temporal Action Localization1
Text to Video Retrieval1
Triplet1
Video Retrieval1
Video-to-Sound Generation1
Zero-Shot Video Retrieval1
image-classification1

Usage over time archive 2025-07-28

Papers per year tagged with VATT: 2021 to 2024, peak 1 1 0 2021: 1 paper 2021 2022: 1 paper 2022 2023: 0 papers 2023 2024: 1 paper 2024
Papers per year the archive tags with this method, by the paper's archive date (3 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Multi-Modal MethodsVision Transformers

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