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Research map / Vision-language and multimodal / Human and embodied modeling

Human perception modeling

Models human visual motion, saliency, and eye movement with trainable networks and human signals.

71 accepted papers at ICML, NeurIPS, ICLR, CVPR and AAAI (2016–2026) · an approach in Human and embodied modeling, keyword cluster “computer vision · humans · abstract”

State History Tried Checklist Preprints Who Export

Where it stands

Not enough benchmark data Fewer than four papers report the same benchmark, so there is no record to follow.

29 papers in 2023–24 and 16 in 2025–26 (at ICML, NeurIPS, ICLR, CVPR and AAAI); its share of the topic shrank, from 5.5% to 2.4%.

How this is decided

A benchmark is tracked when at least four papers of this approach report their own method on the same dataset and metric in at least two different years. Its record is the best number reported up to 2024 compared with the best reported in 2025–26: still improving means at least 1% better, flat means at most 0.2% better with at least two papers from 2025–26 trying. Numbers on a different scale from the rest (a fraction among percentages) and far outliers are left out. Results come from different papers with different setups, so they are signals, not head-to-head comparisons. The share of papers is counted at ICML, NeurIPS, ICLR, CVPR and AAAI.

How it got here

2016: 5162017: 5172018: 3182019: 0192020: 3202021: 4212022: 6222023: 10232024: 19242025: 12252026: 426

Most cited paper of each year

Earliest papers here

What has been tried

63 papers here report 567 measured changes (a component added, removed or swapped, compared with a baseline): helped 66%, no clear effect 10%, hurt 19%, mixed 4%. Papers mostly publish changes that helped, so "helped" is over-represented.

Ablations in recent papers

What was changedMeasured onResultEffectPaper
Remove the editing transformerCOCAS+ Real2 CTCC-ReID · mAP32.2 -> 32.6No clear effectInstruct-ReID: A Multi-purpose Person Re-identification Task with Instructions (CVPR 2024 · 49 citations)
add FTSGait3D · rank-1 accuracy73.2 -> 75.4HelpedLearning Visual Prompt for Gait Recognition (CVPR 2024 · 29 citations)
add observer encoderOSIE-ASD · SM0.341 -> 0.377HelpedBeyond Average: Individualized Visual Scanpath Prediction (CVPR 2024 · 17 citations)
Provide human-generated oracle captions rather than model-generated captionsVisual Riddles open-ended VQA · human rating accuracy23 -> 50HelpedVisual Riddles: a Commonsense and World Knowledge Challenge for Large Vision and Language Models (NeurIPS 2024 · 12 citations)
Fine-tune DINOv2 on DepthCuesNYUv2 · accuracy87.78 -> 87.06HurtDepthCues: Evaluating Monocular Depth Perception in Large Vision Models (CVPR 2025 · 8 citations)
Use expanded textual description oti rather than only semantic character osi for conditioningOracle visual-guide test set · TransOV accuracy54.05% -> 72.97%HelpedMaking Visual Sense of Oracle Bones for You and Me (CVPR 2024 · 6 citations)
Add the unit representing mashed avocado dip or spreadImageNet guacamole category · test accuracy84%HelpedAutomated Natural Language Explanation of Deep Visual Neurons with Large Models (Student Abstract) (AAAI 2024 · 3 citations)
Use deeper MoLFormer layers for odorant representationsSnitz and Ravia · representational alignmentHelpedCan Transformers Smell Like Humans? (NeurIPS 2024 · 2 citations)
Tune on NIGHTS instead of BAPPS, THINGS, or ImageNet tripletsCounting and DeepFashion2 instance retrieval · counting RMSE and retrieval recallHelpedWhen does perceptual alignment benefit vision representations? (NeurIPS 2024 · 2 citations)
Add vision salience attraction pointEyEar-20k test set · PDS0.602 -> 0.614HelpedEyEar: Learning Audio Synchronized Human Gaze Trajectory Based on Physics-Informed Dynamics (AAAI 2025 · 1 citation)
Add CoRs to the extracted featuresSemantic Complexity Scoring · PCC0.818 (0.003) -> 0.823 (0.002)HelpedIs Your Image a Good Storyteller? (AAAI 2025 · 1 citation)
Train Stage 3 with images and textual annotations rather than images onlyBlendFace · AUC (%)98.45 -> 99.48HelpedGuard Me If You Know Me: Protecting Specific Face-Identity from Deepfakes (NeurIPS 2025 · 1 citation)

What reviewers will expect

Datasets and baselines named in the 12 papers of this approach from 2025–2026 that have a full-text summary. Leaving out the common ones is a frequent review question.

Datasets

  • COCO · 2 of 12 papers

Baselines

  • CLIP · 2 of 12 papers
  • GPT-4o · 2 of 12 papers

Recent preprints

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    Who publishes on it

    Institutions on the most papers of this approach since 2023 (of 45 papers; from OpenAlex and the papers' affiliations).

    Use it

    Download BibTeX of 37 key papersOpen the topic in the interactive map

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    Same keyword cluster

    Elsewhere in the topic

    Benchmark numbers, ablations and baseline numbers are extracted by AI from each paper's full text and can be wrong; the paper is the authority. Data: results index of 2026-11-02, citation counts from OpenAlex (2026-10-09).

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