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ARXIV:2602.23622 · COMPUTER VISION · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2602.23622COMPUTER VISIONSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
DLEBench offers a benchmark for evaluating and improving the performance of instruction-based image editing models on small objects.
Opportunity summary
Pain DLEBench offers a benchmark for evaluating and improving the performance of instruction-based image editing models on small objects.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
DLEBench offers a benchmark for evaluating and improving the performance of instruction-based image editing models on small objects. However, while these models demonstrate plausible adherence to instructions and strong reasoning ability on current benchmarks,…
Significant progress has been made in the field of Instruction-based Image Editing Models (IIEMs). However, while these models demonstrate plausible adherence to instructions and strong reasoning ability on current benchmarks, their ability to edit…
ScienceToStartup currently rates this 5.0/10 on the public viability pass. However, while these models demonstrate plausible adherence to instructions and strong reasoning ability on current benchmarks, their ability to edit small objects remains underexplored,…
Computer Vision moved forward this cycle; last verified April 2026. Public score 5.0/10.
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DLEBench offers a benchmark for evaluating and improving the performance of instruction-based image editing models on small objects.
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Paper Pack
10.48550/arXiv.2602.23622DLEBench offers a benchmark for evaluating and improving the performance of instruction-based image editing models on small objects.
Abstract
Significant progress has been made in the field of Instruction-based Image Editing Models (IIEMs). However, while these models demonstrate plausible adherence to instructions and strong reasoning ability on current benchmarks, their ability to edit small objects remains underexplored, despite its importance for precise local editing and refining details in both real and generated images. In this paper, we introduce DeepLookEditBench (DLEBench), the first benchmark dedicated to assessing the abilities of IIEMs in editing small-scale objects. Specifically, we construct a challenging testbed comprising 1889 samples across seven instruction types. In these samples, target objects occupy only 1%-10% of the image area, covering complex scenarios such as partial occlusion and multi-object editing. To ensure robust evaluation on this benchmark, we propose an evaluation protocol with refined score rubrics to minimize subjectivity and ambiguity in two criteria: Instruction Following and Visual Consistency. This protocol also introduces a dual-mode evaluation framework (Tool-driven and Oracle-guided Modes) addressing the misalignment between LMM-as-a-Judge and human judgements on DLEBench. Empirical results on 10 IIEMs reveal significant performance gaps in small-scale object editing, highlighting the need for specialized benchmarks to advance this ability.
Source availability
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Extraction status
Derived fallbackRead summaries are estimated from adjacent metadata, not verified extraction rows.
Proof status
unverified0 refs; 0 sources; 17% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
Viability
Time to MVP
Commercial
Export
Preparing verified analysis
Dimensions overall score 5.0
PROBLEM
DLEBench offers a benchmark for evaluating and improving the performance of instruction-based image editing models on small objects. However, while these models demonstrate plausible adherence to instructions and strong reasoning ability on current benchmarks, their ability to e...
METHOD
Significant progress has been made in the field of Instruction-based Image Editing Models (IIEMs). However, while these models demonstrate plausible adherence to instructions and strong reasoning ability on current benchmarks, their ability to edit small objects remains underexp...
RESULT
ScienceToStartup currently rates this 5.0/10 on the public viability pass. However, while these models demonstrate plausible adherence to instructions and strong reasoning ability on current benchmarks, their ability to edit small objects remains underexplored, despite its impor...
WHY NOW
Computer Vision moved forward this cycle; last verified April 2026. Public score 5.0/10.
Abstract-backed public claims while anchored extraction refreshes.
DLEBench offers a benchmark for evaluating and improving the performance of instruction-based image editing models on small objects. However, while these models demonstrate plausible adherence to instructions and strong reasoning ability on current benchmarks, their ability to edit small objects remains underexplored, despite its importance for precise local editing and refining details in both real and generated images.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Significant progress has been made in the field of Instruction-based Image Editing Models (IIEMs). However, while these models demonstrate plausible adherence to instructions and strong reasoning ability on current benchmarks, their ability to edit small objects remains underexplored, despite its importance for precise local editing and refining details in both real and generated images.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 5.0/10 on the public viability pass. However, while these models demonstrate plausible adherence to instructions and strong reasoning ability on current benchmarks, their ability to edit small objects remains underexplored, despite its importance for precise local editing and refining details in both real and generated images.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Computer Vision moved forward this cycle; last verified April 2026. Public score 5.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Concepts
Methods
Materials
Markets
Competitors
DLEBench offers a benchmark for evaluating and improving the performance of instruction-based image editing models on small objects.
Segment
Computer Vision
Adoption evidence
No public code link in the paper record yet
Commercial read
5.0/10 public viability
Direct
Adjacent
Substitute
Unknown
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CITED BY
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Extension
Commercially relevant
Conflicting
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Build Passport
Build passport pending - Proof Lab budget No verified cost estimate / $7.00 cap
status
missing
reason
passport_row_missing
proof status
unverified
cost/budget
No verified cost estimate
confidence low
next verification path
Build brief missing until Build Passport data exists.
Source missing: Build Passport payload.
Experiment plan missing until prototype path is available.
No prototype path attached.
Validation checklist missing until required assets, cost, and regulatory flags are verified.
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Evidence coverage
OpportunityKernel evidence_receipt
0 refs / 0 sources / 17% coverage
stale
Verify missing sources before using this as buyer proof. verified:false
Build readiness
BuildPassport EvidenceState
passport absent
stale
Run Proof Lab or inspect typed missing state. verified:false
Artifact maturity
GitHub and Hugging Face maturity payloads
No public artifact surface observed
stale
Open source artifacts or mark the gap as missing. verified:false
Technical feasibility
partial
Current read
Runnable path is not fully verified.
Evidence
No Build Passport payload attached.
Gaps
Next test
Run minimal reproduction from the Build Passport prototype path.
Market urgency
missing
Current read
Buyer urgency is not verified from source.
Evidence
0 references, 0 sources, 17% evidence coverage.
Gaps
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Collect buyer interview, deployment evidence, or cited demand signal.
Buyer clarity
missing
Current read
No budget owner is verified for this paper.
Evidence
Build tab has no CRM, procurement, or operator source.
Gaps
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Map target operator, economic buyer, and procurement trigger.
Defensibility
missing
Current read
Defensibility signals are missing.
Evidence
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Gaps
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Refresh defensibility bars with source receipts.
Integration burden
missing
Current read
No public implementation surface observed.
Evidence
No GitHub or Hugging Face payload attached.
Gaps
Next test
Write integration checklist from prototype path and target workflow.
Capital intensity
missing
Current read
No observed cost estimate is verified.
Evidence
Cost passport has no observed_usd value.
Gaps
Next test
Run cost passport or mark the cost field not applicable.
Regulatory load
missing
Current read
No regulatory classification is attached.
Evidence
Build Passport ledger does not include regulatory flags.
Gaps
Next test
Classify regulatory flags before commercialization planning.
No named scientific founder assigned.
Paper authors are not treated as operators without consent.
People
No named person assigned.
Gaps
Next verification path
Prototype owner missing.
Build Passport does not name an implementer.
People
No named person assigned.
Gaps
Next verification path
Operator workflow not sourced.
No buyer or workflow interview attached.
People
No named person assigned.
Gaps
Next verification path
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No CRM or outreach source attached.
People
No named person assigned.
Gaps
Next verification path
Regulatory need unclassified.
No clinical or regulatory source attached.
People
No named person assigned.
Gaps
Next verification path
ARTIFACTS
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DEFENSIBILITY
Defensibility and confidence evidence pending.
WATCHTOWER
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FORESIGHT
No prediction yet — minted on next Foresight batch.
OPPORTUNITYKERNEL CHANGES SINCE LAST VIEW
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COMPETITIVE LANDSCAPE UPDATES
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RELATED PAPER UPDATES
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SIGNAL CANVAS HISTORY AND DELTAS
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TIMELINE
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BUZZ
Buzz trend pending.