📄

Paper

Transformer in Transformer

by Kai Han, An Xiao, E. Wu, Jianyuan Guo, Chunjing Xu, Yunhe Wang 2103.00112

Free2AITools Nexus Index

63.4

S: Semantic 50

Query-time baseline · scored live at search

A: Authority 94

P: Popularity 75

R: Recency 100

Q: Quality 65

Tech Context

Vital Performance —

Transformer is a new kind of neural architecture which encodes the input data as powerful features via the attention mechanism. Basically, the visual transformers first divide the input images into several local patches and then calculate both representations and their relationship. Since natural images are of high complexity with abundant detail and color information, the granularity of the patch dividing is not fine enough for excavating features of objects in different scales and locations...

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Semantic Scholar 2.0K Citations

Paper Information Summary
Entity Passport
Registry ID	2103.00112
License	ArXiv
Provider	semantic_scholar

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Cite this paper

Academic & Research Attribution

BibTeX

@misc{arxiv_2103_00112,
  author = {Kai Han, An Xiao, E. Wu, Jianyuan Guo, Chunjing Xu, Yunhe Wang},
  title = {Transformer in Transformer Paper},
  year = {2026},
  howpublished = {\url{https://arxiv.org/abs/2103.00112}},
  note = {Accessed via Free2AITools.}
}

APA Style

Kai Han, An Xiao, E. Wu, Jianyuan Guo, Chunjing Xu, Yunhe Wang. (2026). Transformer in Transformer [Paper]. Free2AITools. https://arxiv.org/abs/2103.00112

🔬Technical Deep Dive

Full Specifications [+]

⚖️ Free2AITools Nexus Index V2.0

Methodology How FNI works

Semantic (S) 50

Query-time baseline · scored live at search

Authority (A) 94

Popularity (P) 75

Recency (R) 100

Quality (Q) 65

💬 Index Insight

FNI V2.0 for Transformer in Transformer: Authority (A:94), Popularity (P:75), Recency (R:100), Quality (Q:65). Semantic (S) is a query-time baseline scored live at search.

Free2AITools Nexus Index

Data Sources / Provenance

HuggingFace API GitHub Metadata Arxiv Citation DB Methodology

Open data Updated: Live data

📝 Executive Summary

"Transformer is a new kind of neural architecture which encodes the input data as powerful features via the attention mechanism. Basically, the visual transformers first divide the input images into several local patches and then calculate both representations and their relationship. Since natural images are of high complexity with abundant detail and color information, the granularity of the patch dividing is not fine enough for excavating features of objects in different scales and locations..."

❝ Cite Node

@article{Han2026Transformer,
  title={Transformer in Transformer},
  author={Kai Han and An Xiao and E. Wu and Jianyuan Guo and Chunjing Xu and Yunhe Wang},
  journal={arXiv preprint arXiv:2103.00112},
  year={2026}
}

👥 Collaborating Minds

Kai Han An Xiao E. Wu Jianyuan Guo Chunjing Xu Yunhe Wang

🔗 Full Paper

Free2AITools indexes the abstract and factual metadata for this paper. Read the complete, authoritative paper on the official source.

Read the full paper on arXiv

📊 Research Signals

📈2,049CitationsSemantic Scholar

🏛️94AuthorityFNI pillar

⏱️100RecencyFNI pillar

✅65QualityFNI pillar

🗂️text generationField

🏷️ Research Topics

transformer architectureattention mechanismimage generation

📦Data Source: semantic_scholar

🔄 Updated daily

Source summary: Based on semantic_scholar metadata. Not a recommendation.

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🛡️ Paper Transparency Report

Technical metadata sourced from upstream repositories.

Open Metadata

🆔 Identity & Source

id: 2103.00112
slug: 2103.00112
source: semantic_scholar
author: Kai Han, An Xiao, E. Wu, Jianyuan Guo, Chunjing Xu, Yunhe Wang
license: ArXiv
tags: paper, research, academic

⚙️ Technical Specs

architecture: null
params billions: null
context length: null
pipeline tag

📊 Engagement & Metrics

downloads: 0
stars: 0
forks: 0
citations: 2,049

Data indexed from public sources. Updated daily.