Methods › Computer Vision › Vision Transformers › LV-ViT

LV-ViT

9 papers tagged archive 2025-07-28

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

LV-ViT is a type of vision transformer that uses token labelling as a training objective. Different from the standard training objective of ViTs that computes the classification loss on an additional trainable class token, token labelling takes advantage of all the image patch tokens to compute the training loss in a dense manner. Specifically, token labeling reformulates the image classification problem into multiple token-level recognition problems and assigns each patch token with an individual location-specific supervision generated by a machine annotator.

Source: All Tokens Matter: Token Labeling for Training Better...

Papers archive 2025-07-28

9 shown of 9, 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

14 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 Classification4
image-classification4
Efficient ViTs2
Knowledge Distillation2
Action Recognition1
All1
Analogical Similarity1
Computational Efficiency1
General Classification1
Informativeness1
Mamba1
Semantic Segmentation1
State Space Models1
Token Reduction1

Usage over time archive 2025-07-28

Papers per year tagged with LV-ViT: 2021 to 2024, peak 4 4 0 2021: 4 papers 2021 2022: 2 papers 2022 2023: 0 papers 2023 2024: 3 papers 2024
Papers per year the archive tags with this method, by the paper's archive date (9 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 Transformers

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