Paper Citation Record · LEDGER

Vision Transformer Adapter for Dense Predictions

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 42 inbound Pith citation observations for arXiv:2205.08534.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2205.08534 v4

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measured 42 of 42 standing notices

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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 42 of 42 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T10:41:01.268204Z

measured 1 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

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External citation measurements

204
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 8b5962d8-85f5-41a6-9c72-5fe108e8be87 · inbound

Uncertainty in Real-Time Semantic Segmentation on Embedded Systems cites this paper.

Uncertainty in Real-Time Semantic Segmentation on Embedded Systems Vision Transformer Adapter for Dense Predictions

Reference 13

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Observation 2b68571f-9d3b-419b-add6-4fc097ce0ed1 · inbound

T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Models cites this paper.

T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Models Vision Transformer Adapter for Dense Predictions

Reference 3

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Observation 0bc9e5cd-1996-4d7a-84a9-fd770152ec25 · inbound

Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey cites this paper.

Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey Vision Transformer Adapter for Dense Predictions

Reference 189

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Observation 963676bc-1edb-48fd-922d-7aef6b7701bd · inbound

Maximizing the Position Embedding for Vision Transformers with Global Average Pooling cites this paper.

Maximizing the Position Embedding for Vision Transformers with Global Average Pooling Vision Transformer Adapter for Dense Predictions

Reference 6

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From Visuals to Vocabulary: Establishing Equivalence Between Image and Text Token Through Autoregressive Pre-training in MLLMs cites this paper.

From Visuals to Vocabulary: Establishing Equivalence Between Image and Text Token Through Autoregressive Pre-training in MLLMs Vision Transformer Adapter for Dense Predictions

Reference 7

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Observation 77cdb184-211e-4059-a2f4-499d2cd81470 · inbound

Radar-Guided Polynomial Fitting for Metric Depth Estimation cites this paper.

Radar-Guided Polynomial Fitting for Metric Depth Estimation Vision Transformer Adapter for Dense Predictions

Reference 9

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Observation 5483b062-d153-4a55-8294-5cf778965ae7 · inbound

Locality-Aware Zero-Shot Human-Object Interaction Detection cites this paper.

Locality-Aware Zero-Shot Human-Object Interaction Detection Vision Transformer Adapter for Dense Predictions

Reference 4

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Data-Efficient Challenges in Visual Inductive Priors: A Retrospective cites this paper.

Data-Efficient Challenges in Visual Inductive Priors: A Retrospective Vision Transformer Adapter for Dense Predictions

Reference 28

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Efficient Medical Vision-Language Alignment Through Adapting Masked Vision Models cites this paper.

Efficient Medical Vision-Language Alignment Through Adapting Masked Vision Models Vision Transformer Adapter for Dense Predictions

Reference 41

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Mamba Guided Boundary Prior Matters: A New Perspective for Generalized Polyp Segmentation cites this paper.

Mamba Guided Boundary Prior Matters: A New Perspective for Generalized Polyp Segmentation Vision Transformer Adapter for Dense Predictions

Reference 5

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Observation 190292f5-0b9c-450d-b19e-a5b00cc01660 · inbound

SAILViT: Towards Robust and Generalizable Visual Backbones for MLLMs via Gradual Feature Refinement cites this paper.

SAILViT: Towards Robust and Generalizable Visual Backbones for MLLMs via Gradual Feature Refinement Vision Transformer Adapter for Dense Predictions

Reference 13

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Colorectal Cancer Tumor Grade Segmentation in Digital Histopathology Images: From Giga to Mini Challenge cites this paper.

Colorectal Cancer Tumor Grade Segmentation in Digital Histopathology Images: From Giga to Mini Challenge Vision Transformer Adapter for Dense Predictions

Reference 31

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Observation 883b2de5-6e7e-4ce7-82b9-f04d83ff57dc · inbound

Beyond Appearance: Geometric Cues for Robust Video Instance Segmentation cites this paper.

Beyond Appearance: Geometric Cues for Robust Video Instance Segmentation Vision Transformer Adapter for Dense Predictions

Reference 7

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Latest Object Memory Management for Temporally Consistent Video Instance Segmentation cites this paper.

Latest Object Memory Management for Temporally Consistent Video Instance Segmentation Vision Transformer Adapter for Dense Predictions

Reference 4

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Observation fd56a0fa-cea4-4096-ba1e-76dd1f69161d · inbound

MPT: Motion Prompt Tuning for Micro-Expression Recognition cites this paper.

MPT: Motion Prompt Tuning for Micro-Expression Recognition Vision Transformer Adapter for Dense Predictions

Reference 44

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Observation 32a08aa5-fe12-4efd-9136-73d8ac2d416e · inbound

AI-driven Remote Facial Skin Hydration and TEWL Assessment from Selfie Images: A Systematic Solution cites this paper.

AI-driven Remote Facial Skin Hydration and TEWL Assessment from Selfie Images: A Systematic Solution Vision Transformer Adapter for Dense Predictions

Reference 10

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Observation 0f5a9b79-a8c9-47d7-9340-977239b167d7 · inbound

FoMo4Wheat: Toward reliable crop vision foundation models with globally curated data cites this paper.

FoMo4Wheat: Toward reliable crop vision foundation models with globally curated data Vision Transformer Adapter for Dense Predictions

Reference 72

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Live(r) Die: Predicting Survival in Colorectal Liver Metastasis cites this paper.

Live(r) Die: Predicting Survival in Colorectal Liver Metastasis Vision Transformer Adapter for Dense Predictions

Reference 2019

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Observation 00b8faf1-c4cb-404e-85aa-1d7583512e92 · inbound

Delineate Anything Flow: Fast, Country-Level Field Boundary Detection from Any Source cites this paper.

Delineate Anything Flow: Fast, Country-Level Field Boundary Detection from Any Source Vision Transformer Adapter for Dense Predictions

Reference 1

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Exploring the Rashomon Set for Concept-Based Models cites this paper.

Exploring the Rashomon Set for Concept-Based Models Vision Transformer Adapter for Dense Predictions

Reference 8

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Foundation Model-Driven Semantic Change Detection in Remote Sensing Imagery cites this paper.

Foundation Model-Driven Semantic Change Detection in Remote Sensing Imagery Vision Transformer Adapter for Dense Predictions

Reference 37

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Foundation Model-Driven Semantic Change Detection in Remote Sensing Imagery cites this paper.

Foundation Model-Driven Semantic Change Detection in Remote Sensing Imagery Vision Transformer Adapter for Dense Predictions

Reference 37

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DinoRADE: Full Spectral Radar-Camera Fusion with Vision Foundation Model Features for Multi-class Object Detection in Adverse Weather cites this paper.

DinoRADE: Full Spectral Radar-Camera Fusion with Vision Foundation Model Features for Multi-class Object Detection in Adverse Weather Vision Transformer Adapter for Dense Predictions

Reference 3

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Frequency-Enhanced Diffusion Models: Curriculum-Guided Semantic Alignment for Zero-Shot Skeleton Action Recognition cites this paper.

Frequency-Enhanced Diffusion Models: Curriculum-Guided Semantic Alignment for Zero-Shot Skeleton Action Recognition Vision Transformer Adapter for Dense Predictions

Reference 68

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Memory-Efficient Transfer Learning with Fading Side Networks via Masked Dual Path Distillation cites this paper.

Memory-Efficient Transfer Learning with Fading Side Networks via Masked Dual Path Distillation Vision Transformer Adapter for Dense Predictions

Reference 13

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HAMSA: Scanning-Free Vision State Space Models via SpectralPulseNet cites this paper.

HAMSA: Scanning-Free Vision State Space Models via SpectralPulseNet Vision Transformer Adapter for Dense Predictions

Reference 3

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VFM$^{4}$SDG: Unveiling the Power of VFMs for Single-Domain Generalized Object Detection cites this paper.

VFM$^{4}$SDG: Unveiling the Power of VFMs for Single-Domain Generalized Object Detection Vision Transformer Adapter for Dense Predictions

Reference 47

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VFM$^{4}$SDG: Unveiling the Power of VFMs for Single-Domain Generalized Object Detection cites this paper.

VFM$^{4}$SDG: Unveiling the Power of VFMs for Single-Domain Generalized Object Detection Vision Transformer Adapter for Dense Predictions

Reference 47

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Observation d0d6cccf-86f6-43ea-8a8f-505af27a292d · inbound

MP-ISMoE: Mixed-Precision Interactive Side Mixture-of-Experts for Efficient Transfer Learning cites this paper.

MP-ISMoE: Mixed-Precision Interactive Side Mixture-of-Experts for Efficient Transfer Learning Vision Transformer Adapter for Dense Predictions

Reference 57

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Beyond ViT Tokens: Masked-Diffusion Pretrained Convolutional Pathology Foundation Model for Cell-Level Dense Prediction cites this paper.

Beyond ViT Tokens: Masked-Diffusion Pretrained Convolutional Pathology Foundation Model for Cell-Level Dense Prediction Vision Transformer Adapter for Dense Predictions

Reference 28

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Unleashing Vision Transformer Potential In Image Quality Assessment via Global-Local Adaptive Interaction cites this paper.

Unleashing Vision Transformer Potential In Image Quality Assessment via Global-Local Adaptive Interaction Vision Transformer Adapter for Dense Predictions

Reference 38

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Selective, Regularized, and Calibrated: Harnessing Vision Foundation Models for Cross-Domain Few-Shot Semantic Segmentation cites this paper.

Selective, Regularized, and Calibrated: Harnessing Vision Foundation Models for Cross-Domain Few-Shot Semantic Segmentation Vision Transformer Adapter for Dense Predictions

Reference 6

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Adapting Prithvi-EO for Fallow Detection for Food-Water Nexus: ViT-Adapter Necks and Parameter-Efficient Backbone tuning of Geospatial Foundation Model cites this paper.

Adapting Prithvi-EO for Fallow Detection for Food-Water Nexus: ViT-Adapter Necks and Parameter-Efficient Backbone tuning of Geospatial Foundation Model Vision Transformer Adapter for Dense Predictions

Reference 9

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Timage: A Generative Text-in-Image Paradigm for Fine-Tuning Vision-Language Models cites this paper.

Timage: A Generative Text-in-Image Paradigm for Fine-Tuning Vision-Language Models Vision Transformer Adapter for Dense Predictions

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UNITY: Attention Flow Networks for Adaptive Conditioning in Diffusion cites this paper.

UNITY: Attention Flow Networks for Adaptive Conditioning in Diffusion Vision Transformer Adapter for Dense Predictions

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UNITY: Attention Flow Networks for Adaptive Conditioning in Diffusion cites this paper.

UNITY: Attention Flow Networks for Adaptive Conditioning in Diffusion Vision Transformer Adapter for Dense Predictions

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State Space Models Meet Remote Sensing: A Survey cites this paper.

State Space Models Meet Remote Sensing: A Survey Vision Transformer Adapter for Dense Predictions

Reference 197

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REAL-OW: Rehearsal-free Open World Object Detection with Low-Rank Adaptation and Dual-Stage Objectness Modeling cites this paper.

REAL-OW: Rehearsal-free Open World Object Detection with Low-Rank Adaptation and Dual-Stage Objectness Modeling Vision Transformer Adapter for Dense Predictions

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FBLayout: Optimizing Memory Layout for Efficient LLM Finetuning on Mobile GPUs cites this paper.

FBLayout: Optimizing Memory Layout for Efficient LLM Finetuning on Mobile GPUs Vision Transformer Adapter for Dense Predictions

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iFAN: Inference-Aware Learning for Plain Mask Transformers cites this paper.

iFAN: Inference-Aware Learning for Plain Mask Transformers Vision Transformer Adapter for Dense Predictions

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From Multi-Resolution Cells to Gigapixel Whole Slide Images Foundation Model for Computational Pathology cites this paper.

From Multi-Resolution Cells to Gigapixel Whole Slide Images Foundation Model for Computational Pathology Vision Transformer Adapter for Dense Predictions

Reference 30

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LiLa-WAM: Lightweight Latent Reasoning World-Action Model for Robotic Manipulation cites this paper.

LiLa-WAM: Lightweight Latent Reasoning World-Action Model for Robotic Manipulation Vision Transformer Adapter for Dense Predictions

Reference 46

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