Methods › Computer Vision › Vision and Language Pre-Trained Models › ALBEF

ALBEF

17 papers tagged archive 2025-07-28

Introduced by Junnan Li et al. in Align before Fuse: Vision and Language Representation Learning with Momentum Distillation

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

ALBEF introduces a contrastive loss to align the image and text representations before fusing them through cross-modal attention. This enables more grounded vision and language representation learning. ALBEF also doesn't require bounding box annotations. The model consists of an image encode, a text encoder, and a multimodal encoder. The image-text contrastive loss helps to align the unimodal representations of an image-text pair before fusion. The image-text matching loss and a masked language modeling loss are applied to learn multimodal interactions between image and text. In addition, momentum distillation is used to generate pseudo-targets. This improves learning with noisy data.

PaperSource

Papers archive 2025-07-28

17 shown of 17, 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

20 shown of 38 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
Image-text Retrieval7
Retrieval7
Text Retrieval7
Question Answering5
Visual Question Answering5
Visual Question Answering (VQA)4
Image Retrieval3
Language Modelling3
Visual Grounding3
Cross-Modal Retrieval2
Image-text matching2
Language Modeling2
Masked Language Modeling2
Representation Learning2
Transfer Learning2
Visual Reasoning2
Adversarial Robustness1
Caption Generation1
Data Augmentation1
Grounded language learning1

Usage over time archive 2025-07-28

Papers per year tagged with ALBEF: 2021 to 2024, peak 6 6 0 2021: 1 paper 2021 2022: 6 papers 2022 2023: 4 papers 2023 2024: 6 papers 2024
Papers per year the archive tags with this method, by the paper's archive date (17 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

Vision and Language Pre-Trained Models

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