ARXIV:2602.10513 · VISION FOUNDATION MODELS · SUBMITTED 17 MAR · 19:46 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: partial proof status

1%>100%: High-Efficiency Visual Adapter with Complex Linear Projection Optimization

Q: What are the practical use cases?

Develop a tool for rapidly adapting vision models to new tasks in fields like remote sensing or medical imaging, without the computational burden of full fine-tuning.

Q: What industries could this research disrupt?

The solution could replace existing, more resource-intensive model fine-tuning methods, particularly in industries needing rapid deployment and adaptation of vision models.

arXiv

Efficient vision model adapter reducing parameters by 99% with state-of-the-art performance.

Blocked on Code›Score8.0Evidence partial

Opportunity summary

Pain Efficient vision model adapter reducing parameters by 99% with state-of-the-art performance.

Evidence 0 refs | 0 sources | 33% coverage

Blocker Evidence partial

Open Build Read PDF Signal Canvas Track

PROBLEM

Efficient vision model adapter reducing parameters by 99% with state-of-the-art performance. While delta-tuning has proven effective in boosting the performance and efficiency of LLMs during adaptation, its advantages cannot be directly transferred to the…

METHOD

Full abstract

Deploying vision foundation models typically relies on efficient adaptation strategies, whereas conventional full fine-tuning suffers from prohibitive costs and low efficiency. While delta-tuning has proven effective in boosting the performance and efficiency of LLMs during adaptation, its advantages cannot be directly transferred to the fine-tuning pipeline of vision foundation models. To push the boundaries of adaptation efficiency for vision tasks, we propose an adapter with Complex Linear Projection Optimization (CoLin). For architecture, we design a novel low-rank complex adapter that introduces only about 1% parameters to the backbone. For efficiency, we theoretically prove that low-rank composite matrices suffer from severe convergence issues during training, and address this challenge with a tailored loss. Extensive experiments on object detection, segmentation, image classification, and rotated object detection (remote sensing scenario) demonstrate that CoLin outperforms both full fine-tuning and classical delta-tuning approaches with merely 1% parameters for the first time, providing a novel and efficient solution for deployment of vision foundation models. We release the code on https://github.com/DongshuoYin/CoLin.

RESULT

ScienceToStartup currently rates this 8.0/10 on the public viability pass. Extensive experiments on object detection, segmentation, image classification, and rotated object detection (remote sensing scenario) demonstrate that CoLin outperforms both full fine-tuning and classical…

WHY NOW

Vision Foundation Models moved forward this cycle; last verified April 2026. Public score 8.0/10.

Continue into Read for claims, analysis, references, and neighboring papers.

Opportunity summary

Score8.0

PainEfficient vision model adapter reducing parameters by 99% with state-of-the-art performance.

Evidence0 refs | 0 sources | 33% coverage

Blockermissing authors

Analysis summary

Efficient vision model adapter reducing parameters by 99% with state-of-the-art performance.

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: partial proof status

ARXIV:2602.10513 · VISION FOUNDATION MODELS · SUBMITTED 17 MAR · 19:46 UTC · FRESHNESS STALE

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: partial proof status

1%>100%: High-Efficiency Visual Adapter with Complex Linear Projection Optimization

arXiv

Efficient vision model adapter reducing parameters by 99% with state-of-the-art performance.

Blocked on Code›Score8.0Evidence partial

Opportunity summary

Pain Efficient vision model adapter reducing parameters by 99% with state-of-the-art performance.

Evidence 0 refs | 0 sources | 33% coverage

Blocker Evidence partial

Open Build Read PDF Signal Canvas Track

PROBLEM

METHOD

Full abstract

RESULT

WHY NOW

Vision Foundation Models moved forward this cycle; last verified April 2026. Public score 8.0/10.

Continue into Read for claims, analysis, references, and neighboring papers.

Opportunity summary

Score8.0

PainEfficient vision model adapter reducing parameters by 99% with state-of-the-art performance.

Evidence0 refs | 0 sources | 33% coverage

Blockermissing authors

Analysis summary

Efficient vision model adapter reducing parameters by 99% with state-of-the-art performance.

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: partial proof status

Paper Pack

10.48550/arXiv.2602.10513

1%>100%: High-Efficiency Visual Adapter with Complex Linear Projection Optimization

Efficient vision model adapter reducing parameters by 99% with state-of-the-art performance.

Abstract

Source availability

PDF linked

The paper record includes a public PDF URL.

Extraction status

Derived fallback

Read summaries are estimated from adjacent metadata, not verified extraction rows.

Proof status

partial

0 refs; 0 sources; 33% coverage.

What was readable

linkedon filenot materialized7 extracted40 indexednot indexed

Derived fallback: Estimated from adjacent evidence; not verified from source.

Viability

8.0

Time to MVP

MVP estimate missing

Commercial

No commercial flags on file

Export

Preparing verified analysis

lens / founder

PROBLEM

METHOD

RESULT

WHY NOW

Vision Foundation Models moved forward this cycle; last verified April 2026. Public score 8.0/10.

Claim map

Strong 7Mixed 0Weak 0

Evidencepartial
we design a novel low-rank complex adapter that introduces only about 1% parameters to the backbone.
Implicationpartial
This is a key quantitative claim made in the abstract and reinforced in the analysis.
Verificationpartial
partial
Evidencepartial
CoLin outperforms both full fine-tuning and classical delta-tuning approaches with merely 1% parameters for the first time
Implicationpartial
The abstract explicitly states this comparative performance advantage.
Verificationpartial
partial
Evidencepartial
we theoretically prove that low-rank composite matrices suffer from severe convergence issues during training
Implicationpartial
This is a theoretical finding presented as a problem that CoLin addresses.
Verificationpartial
partial
Evidencepartial
Extensive experiments on object detection, segmentation, image classification, and rotated object detection (remote sensing scenario) demonstrate that CoLin outperforms
Implicationpartial
The abstract lists these specific tasks as part of the experimental validation.
Verificationpartial
partial
Evidencepartial
we propose an adapter with Complex Linear Projection Optimization (CoLin).
Implicationpartial
The method's name is explicitly stated in the title and abstract.
Verificationpartial
partial
Evidencepartial
The approach might face challenges with very high-complexity tasks where simplified adaptations could lead to performance drops.
Implicationpartial
This is a stated caveat in the provided analysis, indicating a potential limitation.
Verificationpartial
partial
Evidencepartial
The technology can be productized into a software solution enabling users to efficiently retrain large vision models on new datasets or tasks with minimal computational resources.
Implicationpartial
The 'product_angle' in the analysis suggests this productization potential.
Verificationpartial
partial

Constellation map

Paper-native neighborhood for concepts, methods, materials, markets, and competitors. Missing lanes stay labeled instead of disappearing behind commercialization gates.

Open full Signal Canvas

Concepts

not indexed

Methods

Materials

PDF linked

Markets

Vision Foundation Models

Competitors

not indexed

Competitive landscape

Efficient vision model adapter reducing parameters by 99% with state-of-the-art performance.

Segment

Vision Foundation Models

Adoption evidence

No public code link in the paper record yet

Commercial read

8.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

Buzz

No indexed public discussion is attached to 2602.10513 yet. That is a visibility signal, not a blank module: the monitor is watching the public channels below.

Hacker News

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Bluesky

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PDF

Preview the source document here, or use the hero PDF action for a new tab.

References(40)

DAPE: Dual-Stage Parameter-Efficient Fine-Tuning for Consistent Video Editing with Diffusion Models

2025Junhao Xia, Chaoyang Zhang et al.

Foundation Models Defining a New Era in Vision: A Survey and Outlook

2025Muhammad Awais, Muzammal Naseer et al.

Remote Sensing Tuning: A Survey

2025Dongshuo Yin, Ting-Feng Zhao et al.

5%>100%: Breaking Performance Shackles of Full Fine-Tuning on Visual Recognition Tasks

2024Dongshuo Yin, Leiyi Hu et al.

STAR: A First-Ever Dataset and a Large-Scale Benchmark for Scene Graph Generation in Large-Size Satellite Imagery

2024Yansheng Li, Linlin Wang et al.

Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey

2024Zeyu Han, Chao Gao et al.

A Survey for Foundation Models in Autonomous Driving

2024Haoxiang Gao, Yaqian Li et al.

AiRs: Adapter in Remote Sensing for Parameter-Efficient Transfer Learning

2024Leiyi Hu, Hongfeng Yu et al.

TEA: A Training-Efficient Adapting Framework for Tuning Foundation Models in Remote Sensing

2024Leiyi Hu, Wanxuan Lu et al.

Parameter-efficient is not Sufficient: Exploring Parameter, Memory, and Time Efficient Adapter Tuning for Dense Predictions

2023Dongshuo Yin, Xueting Han et al.

1% VS 100%: Parameter-Efficient Low Rank Adapter for Dense Predictions

2023Dongshuo Yin, Yiran Yang et al.

Parameter-Efficient Fine-Tuning for Medical Image Analysis: The Missed Opportunity

2023Raman Dutt, Linus Ericsson et al.

Visual Tuning

2023Bruce X. B. Yu, Jianlong Chang et al.

Parameter-efficient fine-tuning of large-scale pre-trained language models

2023Ning Ding, Yujia Qin et al.

The Expressive Power of Tuning Only the Norm Layers

2023Angeliki Giannou, Shashank Rajput et al.

Polyhistor: Parameter-Efficient Multi-Task Adaptation for Dense Vision Tasks

2022Yen-Cheng Liu, Chih-Yao Ma et al.

ST-Adapter: Parameter-Efficient Image-to-Video Transfer Learning for Action Recognition

2022Junting Pan, Ziyi Lin et al.

AdaptFormer: Adapting Vision Transformers for Scalable Visual Recognition

2022Shoufa Chen, Chongjian Ge et al.

MMRotate: A Rotated Object Detection Benchmark using PyTorch

2022Yue Zhou, Xue Yang et al.

Visual Prompt Tuning

2022Menglin Jia, Luming Tang et al.

Showing 20 of 40 references

CITED BY

No citing papers are indexed in the public S2S graph yet. This is an explicit zero-signal state, not a hidden lookup.

Foundation

none indexed

Extension

Builds On ThisAn Adapter-free Fine-tuning Approach for Tuning 3D Foundation Models

7.0

Builds On ThisLoCO: Low-rank Compositional Rotation Fine-tuning

7.0

Builds On ThisCompress Then Adapt? No, Do It Together via Task-aware Union of Subspaces

7.0

Builds On ThisTrainDeeploy: Hardware-Accelerated Parameter-Efficient Fine-Tuning of Small Transformer Models at the Extreme Edge

6.0

Builds On ThisBeyond LoRA vs. Full Fine-Tuning: Gradient-Guided Optimizer Routing for LLM Adaptation

7.0

Builds On ThisTAPE: A two-stage parameter-efficient adaptation framework for foundation models in OCT-OCTA analysis

7.0

Builds On ThisResAdapt: Adaptive Resolution for Efficient Multimodal Reasoning

7.0

Builds On ThisRDP LoRA: Geometry-Driven Identification for Parameter-Efficient Adaptation in Large Language Models

7.0

Builds On ThisConcept-Guided Fine-Tuning: Steering ViTs away from Spurious Correlations to Improve Robustness

7.0

Commercially relevant

Higher ViabilityAdapterTune: Zero-Initialized Low-Rank Adapters for Frozen Vision Transformers

9.0

Conflicting

none indexed

Related Resources

Owned Distribution

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Agent drawer

5 surfaces preserved for agents. Humans can ignore.

Developer contracts, payload previews, evidence maps, and run controls stay here instead of the Read, Build, and Track workspace.

Run context

Paper: 2602.10513
Route: /paper/1-100-high-efficiency-visual-adapter-with-complex-linear-projection-optimization
Active tab: read
Artifact: 1-100-high-efficiency-visual-adapter-with-complex-linear-projection-optimization

Available agents

Read extractor
Build planner
Track monitor
Competitive mapper
Related-paper scout

API/MCP endpoints

REST paper pack API/api/v1/paper/1-100-high-efficiency-visual-adapter-with-complex-linear-projection-optimization/paper-pack
REST build passport API/api/v1/paper/1-100-high-efficiency-visual-adapter-with-complex-linear-projection-optimization/build-passport
REST OpenAPI/api/openapi.json
MCP descriptor/api/mcp
MCP resourcesciencetostartup://surfaces/paper-workspace

Tool contracts

paper_packbuild_passportopportunity_kernelforesightsource_proofevidence_state

Payload preview

Inspect payload

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Schema validation

paper-r2 contract: present
JSON-LD twin: SSR emitted
OpenAPI path parity: /api/openapi.json
MCP resource parity: paper-workspace

Job trace

queued: drawer opened by user action
running: inspect or copy payload
succeeded: payload available in SSR
failed: route errors appear in evidence cards

Evidence map

sources used: page freshness, source proof anchors, JSON-LD
missing sources: exposed by PaperPack and EvidenceState chips
derived fallbacks: marked unverified before handoff

Page Freshness

Canonical route, proof status, last verified, refs, sources, and coverage.

Page Freshness

Paper proof surface

Canonical route: /paper/1-100-high-efficiency-visual-adapter-with-complex-linear-projection-optimization

stale

Proof freshness: stale
Proof status: partial
Display score: 8/10
Last proof check: 2026-03-17
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 0
Source count: 0
Coverage: 33%

This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.

OpenAlex: pending — this preprint is not yet indexed by OpenAlex.

Agent Handoff

Endpoint list, payload shape, route context, and copyable handoff data.

Agent Handoff

1%>100%: High-Efficiency Visual Adapter with Complex Linear Projection Optimization

Canonical ID 1-100-high-efficiency-visual-adapter-with-complex-linear-projection-optimization | Route /paper/1-100-high-efficiency-visual-adapter-with-complex-linear-projection-optimization

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/paper/1-100-high-efficiency-visual-adapter-with-complex-linear-projection-optimization

MCP example

{
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}

source_context

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Buildability Receipt

Verdict, compute envelope, blockers, signature state, and receipt links.

Paper proof page receipt window

Watch and verify: 1%>100%: High-Efficiency Visual Adapter with Complex Linear Projection Optimization

/buildability/1-100-high-efficiency-visual-adapter-with-complex-linear-projection-optimization

Watchwatch

Subject: 1%>100%: High-Efficiency Visual Adapter with Complex Linear Projection Optimization

Verdict

Watch

Verdict is Watch because viability or proof quality is intermediate and should be re-evaluated before execution.

Time to first demo

Insufficient data

No first-demo timestamp, owner estimate, or elapsed demo receipt is attached to this surface.

Compute envelope

Structured compute envelope

Insufficient data

No data, compute, hardware, memory, latency, dependency, or serving requirement receipt is attached.

Evidence ids

Receipt path

/buildability/1-100-high-efficiency-visual-adapter-with-complex-linear-projection-optimization

Paper ref

1-100-high-efficiency-visual-adapter-with-complex-linear-projection-optimization

arXiv id

2602.10513

Freshness

Generated at

2026-03-17T19:46:04.153Z

Evidence freshness

stale

Last verification

2026-03-17T19:46:04.153Z

Sources

References

Coverage

33%

Hash state

Lineage hash

2db2d717cba298415fe80d2b32945e78bafd4d20042dd826c5244ff38483b04b

Canonical opportunity-kernel lineage hash.

Signature state

External signature

unsigned_external

No founder, registry, pilot, or production-adoption signature is attached to this receipt.

Verification

not_verified

Verification is blocked until an external signature is provided.

Blockers

Missing: repo_url
Missing: references
Missing: distribution_readiness_scores
Missing: paper_extraction_scorecards
Unknown: distribution readiness has not been computed yet

Verification pending / evidence receipt incomplete

repo_url

references

Missing proof, requirement, signature, approval, adoption, or telemetry fields are blockers and must not be inferred.

Open receipt API receipt Build Loop Signal Canvas Proof divergence Divergence API Brier outcomes API

Source Proof anchors

Visual citations from the paper document graph.

JSON-LD twin

The application/ld+json payload rendered for agents.

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1%>100%: High-Efficiency Visual Adapter with Complex Linear Projection Optimization

1%>100%: High-Efficiency Visual Adapter with Complex Linear Projection Optimization

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