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Metric Hub

Accuracy In CS.CV Papers

Updated from current HFEPX corpus (Feb 27, 2026). 18 papers are grouped in this metric page. Common evaluation modes: Automatic Metrics, Simulation Env. Most common rater population: Domain Experts. Frequent quality control: Calibration. Frequently cited benchmark: Retrieval. Common metric signal: accuracy. Use this page to compare protocol setup, judge behavior, and labeling design decisions before running new eval experiments. Newest paper in this set is from Feb 25, 2026.

Papers: 18 Last published: Feb 25, 2026 Global RSS

Research Narrative

Grounded narrative Model: deterministic-grounded Source: persisted

Updated from current HFEPX corpus (Feb 27, 2026). This page tracks 18 papers for Accuracy In CS.CV Papers. Dominant protocol signals include automatic metrics, simulation environments, with frequent benchmark focus on Retrieval, DocVQA and metric focus on accuracy, auroc. Use the grounded sections below to prioritize reproducible protocol choices, benchmark-matched comparisons, and judge-vs-human evaluation checks.

Why This Matters For Eval Research

Protocol Takeaways

Benchmark Interpretation

  • Retrieval appears in 11.1% of hub papers (2/18); use this cohort for benchmark-matched comparisons.
  • DocVQA appears in 5.6% of hub papers (1/18); use this cohort for benchmark-matched comparisons.

Metric Interpretation

  • accuracy is reported in 100% of hub papers (18/18); compare with a secondary metric before ranking methods.
  • auroc is reported in 5.6% of hub papers (1/18); compare with a secondary metric before ranking methods.

Researcher Checklist

  • Close gap on Papers with explicit human feedback. Coverage is a replication risk (16.7% vs 45% target).
  • Close gap on Papers reporting quality controls. Coverage is a replication risk (5.6% vs 30% target).
  • Close gap on Papers naming benchmarks/datasets. Coverage is a replication risk (16.7% vs 35% target).
  • Maintain strength on Papers naming evaluation metrics. Coverage is strong (100% vs 35% target).
  • Tighten coverage on Papers with known rater population. Coverage is usable but incomplete (22.2% vs 35% target).
  • Close gap on Papers with known annotation unit. Coverage is a replication risk (0% vs 35% target).

Papers with explicit human feedback

Coverage is a replication risk (16.7% vs 45% target).

Papers reporting quality controls

Coverage is a replication risk (5.6% vs 30% target).

Papers naming benchmarks/datasets

Coverage is a replication risk (16.7% vs 35% target).

Papers naming evaluation metrics

Coverage is strong (100% vs 35% target).

Papers with known rater population

Coverage is usable but incomplete (22.2% vs 35% target).

Papers with known annotation unit

Coverage is a replication risk (0% vs 35% target).

Suggested Reading Order

  1. 1. NoLan: Mitigating Object Hallucinations in Large Vision-Language Models via Dynamic Suppression of Language Priors

    Start here for detailed protocol reporting, including rater and quality-control evidence.

  2. 2. DynamicGTR: Leveraging Graph Topology Representation Preferences to Boost VLM Capabilities on Graph QAs

    Start here for detailed protocol reporting, including rater and quality-control evidence.

  3. 3. SurGo-R1: Benchmarking and Modeling Contextual Reasoning for Operative Zone in Surgical Video

    Start here for detailed protocol reporting, including rater and quality-control evidence.

  4. 4. Virtual Biopsy for Intracranial Tumors Diagnosis on MRI

    Adds automatic metrics for broader coverage within this hub.

  5. 5. XMorph: Explainable Brain Tumor Analysis Via LLM-Assisted Hybrid Deep Intelligence

    Adds automatic metrics for broader coverage within this hub.

  6. 6. OrthoDiffusion: A Generalizable Multi-Task Diffusion Foundation Model for Musculoskeletal MRI Interpretation

    Adds automatic metrics for broader coverage within this hub.

  7. 7. MedCLIPSeg: Probabilistic Vision-Language Adaptation for Data-Efficient and Generalizable Medical Image Segmentation

    Adds automatic metrics for broader coverage within this hub.

  8. 8. When Pretty Isn't Useful: Investigating Why Modern Text-to-Image Models Fail as Reliable Training Data Generators

    Adds automatic metrics for broader coverage within this hub.

Known Limitations

  • Only 5.6% of papers report quality controls; prioritize calibration/adjudication evidence.
  • Rater population is under-specified (22.2% coverage).
  • Narrative synthesis is grounded in metadata and abstracts only; full-paper implementation details are not parsed.

Research Utility Links

automatic_metrics vs simulation_env

both=2, left_only=16, right_only=0

2 papers use both Automatic Metrics and Simulation Env.

Benchmark Brief

Retrieval

Coverage: 2 papers (11.1%)

2 papers (11.1%) mention Retrieval.

Examples: VIGiA: Instructional Video Guidance via Dialogue Reasoning and Retrieval , Index Light, Reason Deep: Deferred Visual Ingestion for Visual-Dense Document Question Answering

Benchmark Brief

DocVQA

Coverage: 1 papers (5.6%)

1 papers (5.6%) mention DocVQA.

Examples: Chain-of-Thought Compression Should Not Be Blind: V-Skip for Efficient Multimodal Reasoning via Dual-Path Anchoring

Metric Brief

accuracy

Coverage: 18 papers (100%)

18 papers (100%) mention accuracy.

Examples: NoLan: Mitigating Object Hallucinations in Large Vision-Language Models via Dynamic Suppression of Language Priors , DynamicGTR: Leveraging Graph Topology Representation Preferences to Boost VLM Capabilities on Graph QAs , SurGo-R1: Benchmarking and Modeling Contextual Reasoning for Operative Zone in Surgical Video

Metric Brief

auroc

Coverage: 1 papers (5.6%)

1 papers (5.6%) mention auroc.

Examples: MedicalPatchNet: A Patch-Based Self-Explainable AI Architecture for Chest X-ray Classification

Metric Brief

calibration

Coverage: 1 papers (5.6%)

1 papers (5.6%) mention calibration.

Examples: Adaptive Data Augmentation with Multi-armed Bandit: Sample-Efficient Embedding Calibration for Implicit Pattern Recognition

Most Cited In This Hub

Fast path to methods with the strongest citation traction in this scope.

Papers: NoLan: Mitigating Object Hallucinations in Large Vision-Language Models via Dynamic Suppression of Language Priors , DynamicGTR: Leveraging Graph Topology Representation Preferences to Boost VLM Capabilities on Graph QAs , SurGo-R1: Benchmarking and Modeling Contextual Reasoning for Operative Zone in Surgical Video

Most Recent

Fast path to latest protocol changes and newly published evaluation setups.

Papers: NoLan: Mitigating Object Hallucinations in Large Vision-Language Models via Dynamic Suppression of Language Priors , DynamicGTR: Leveraging Graph Topology Representation Preferences to Boost VLM Capabilities on Graph QAs , SurGo-R1: Benchmarking and Modeling Contextual Reasoning for Operative Zone in Surgical Video

Best Protocol Detail

Papers with explicit rater/unit metadata and quality-control signals for reproducibility.

Papers: NoLan: Mitigating Object Hallucinations in Large Vision-Language Models via Dynamic Suppression of Language Priors , DynamicGTR: Leveraging Graph Topology Representation Preferences to Boost VLM Capabilities on Graph QAs , SurGo-R1: Benchmarking and Modeling Contextual Reasoning for Operative Zone in Surgical Video

Top Papers Reporting This Metric

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