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

One Representation

OneR

2 papers tagged archive 2025-07-28

Introduced by Jiho Jang et al. in Unifying Vision-Language Representation Space with Single-tower Transformer

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

In the OneR method, model input can be one of image, text or image+text, and CMC objective is combined with the traditional image-text contrastive (ITC) loss. Masked modeling is also carried out for all three input types (i.e., image, text and multi-modal). This framework employs no modality-specific architectural component except for the initial token embedding layer, making our model generic and modality-agnostic with minimal inductive bias.

PaperSource

Papers archive 2025-07-28

2 shown of 2, 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

6 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
Anomaly Detection1
Contrastive Learning1
Object Localization1
Representation Learning1
Retrieval1
Visual Reasoning1

Usage over time archive 2025-07-28

Papers per year tagged with OneR: 2022 to 2024, peak 1 1 0 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 (2 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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