ARXIV:2603.08317 · ACTION RECOGNITION · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

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

Human-AI Divergence in Ego-centric Action Recognition under Spatial and Spatiotemporal Manipulations

arXiv

Identify and leverage minimal visual cues for robust action recognition, bridging the gap between human and AI performance in real-world scenarios.

Blocked on Code›Score7.0Evidence unverified

Opportunity summary

Pain Identify and leverage minimal visual cues for robust action recognition, bridging the gap between human and AI performance in real-world scenarios.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

Identify and leverage minimal visual cues for robust action recognition, bridging the gap between human and AI performance in real-world scenarios. Understanding the sources of this performance gap is essential for developing more robust…

METHOD

Full abstract

Humans consistently outperform state-of-the-art AI models in action recognition, particularly in challenging real-world conditions involving low resolution, occlusion, and visual clutter. Understanding the sources of this performance gap is essential for developing more robust and human-aligned models. In this paper, we present a large-scale human-AI comparative study of egocentric action recognition using Minimal Identifiable Recognition Crops (MIRCs), defined as the smallest spatial or spatiotemporal regions sufficient for reliable human recognition. We used our previously introduced, Epic ReduAct, a systematically spatially reduced and temporally scrambled dataset derived from 36 EPIC KITCHENS videos, spanning multiple spatial reduction levels and temporal conditions. Recognition performance is evaluated using over 3,000 human participants and the Side4Video model. Our analysis combines quantitative metrics, Average Reduction Rate and Recognition Gap, with qualitative analyses of spatial (high-, mid-, and low-level visual features) and spatiotemporal factors, including a categorisation of actions into Low Temporal Actions (LTA) and High Temporal Actions (HTA). Results show that human performance exhibits sharp declines when transitioning from MIRCs to subMIRCs, reflecting a strong reliance on sparse, semantically critical cues such as hand-object interactions. In contrast, the model degrades more gradually and often relies on contextual and mid- to low-level features, sometimes even exhibiting increased confidence under spatial reduction. Temporally, humans remain robust to scrambling when key spatial cues are preserved, whereas the model often shows insensitivity to temporal disruption, revealing class-dependent temporal sensitivities.

RESULT

ScienceToStartup currently rates this 7.0/10 on the public viability pass. Results show that human performance exhibits sharp declines when transitioning from MIRCs to subMIRCs, reflecting a strong reliance on sparse, semantically critical cues such…

WHY NOW

Action Recognition moved forward this cycle; last verified April 2026. Public score 7.0/10.

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

Opportunity summary

Score7.0

PainIdentify and leverage minimal visual cues for robust action recognition, bridging the gap between human and AI performance in real-world scenarios.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

Identify and leverage minimal visual cues for robust action recognition, bridging the gap between human and AI performance in real-world scenarios.

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

ARXIV:2603.08317 · ACTION RECOGNITION · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

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

Human-AI Divergence in Ego-centric Action Recognition under Spatial and Spatiotemporal Manipulations

arXiv

Identify and leverage minimal visual cues for robust action recognition, bridging the gap between human and AI performance in real-world scenarios.

Blocked on Code›Score7.0Evidence unverified

Opportunity summary

Pain Identify and leverage minimal visual cues for robust action recognition, bridging the gap between human and AI performance in real-world scenarios.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

METHOD

Full abstract

RESULT

WHY NOW

Action Recognition moved forward this cycle; last verified April 2026. Public score 7.0/10.

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

Opportunity summary

Score7.0

PainIdentify and leverage minimal visual cues for robust action recognition, bridging the gap between human and AI performance in real-world scenarios.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

Identify and leverage minimal visual cues for robust action recognition, bridging the gap between human and AI performance in real-world scenarios.

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

Paper Pack

10.48550/arXiv.2603.08317

Human-AI Divergence in Ego-centric Action Recognition under Spatial and Spatiotemporal Manipulations

Identify and leverage minimal visual cues for robust action recognition, bridging the gap between human and AI performance in real-world scenarios.

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

unverified

0 refs; 0 sources; 17% coverage.

What was readable

linkedon filenot materializedderived fallback80 indexednot indexed

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

Viability

7.0

Time to MVP

MVP estimate missing

Commercial

No commercial flags on file

Export

Preparing verified analysis

lens / founder

PROBLEM

METHOD

RESULT

WHY NOW

Action Recognition moved forward this cycle; last verified April 2026. Public score 7.0/10.

Claim map

Abstract-backed public claims while anchored extraction refreshes.

Strong 0Mixed 0Weak 4

Evidencepartial
Identify and leverage minimal visual cues for robust action recognition, bridging the gap between human and AI performance in real-world scenarios. Understanding the sources of this performance gap is essential for developing more robust and human-aligned models.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Humans consistently outperform state-of-the-art AI models in action recognition, particularly in challenging real-world conditions involving low resolution, occlusion, and visual clutter. Understanding the sources of this performance gap is essential for developing more robust and human-aligned models.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Results show that human performance exhibits sharp declines when transitioning from MIRCs to subMIRCs, reflecting a strong reliance on sparse, semantically critical cues such as hand-object interactions.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Action Recognition moved forward this cycle; last verified April 2026. Public score 7.0/10.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
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

Action Recognition

Competitors

not indexed

Competitive landscape

Identify and leverage minimal visual cues for robust action recognition, bridging the gap between human and AI performance in real-world scenarios.

Segment

Action Recognition

Adoption evidence

No public code link in the paper record yet

Commercial read

7.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

Buzz

No indexed public discussion is attached to 2603.08317 yet. That is a visibility signal, not a blank module: the monitor is watching the public channels below.

Hacker News

Not indexed yet

Bluesky

Not indexed yet

PDF

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

References(80)

Seeing Just Enough: The Contribution of Hands, Objects and Visual Features to Egocentric Action Recognition

2026Filip Rybansky, Sadegh Rahmaniboldaji et al.

Human vs. Machine Minds: Ego-Centric Action Recognition Compared

2025Sadegh Rahmani-Boldaji, Filip Rybansky et al.

VideoLLaMA 3: Frontier Multimodal Foundation Models for Image and Video Understanding

2025Boqiang Zhang, Kehan Li et al.

DEAR: Depth-Enhanced Action Recognition

2024Sadegh Rahmaniboldaji, Filip Rybansky et al.

SAM 2: Segment Anything in Images and Videos

2024Nikhila Ravi, Valentin Gabeur et al.

FILS: Self-Supervised Video Feature Prediction In Semantic Language Space

2024Mona Ahmadian, F. Guerin et al.

Side4Video: Spatial-Temporal Side Network for Memory-Efficient Image-to-Video Transfer Learning

2023Huanjin Yao, Wenhao Wu et al.

MOFO: MOtion FOcused Self-Supervision for Video Understanding

2023Mona Ahmadian, Frank Guerin et al.

Do humans and Convolutional Neural Networks attend to similar areas during scene classification: Effects of task and image type

2023Romy Müller, Marcel Duerschmidt et al.

Deep problems with neural network models of human vision

2022J. Bowers, Gaurav Malhotra et al.

Harmonizing the object recognition strategies of deep neural networks with humans

2022Thomas Fel, Ivan Felipe et al.

ViewFool: Evaluating the Robustness of Visual Recognition to Adversarial Viewpoints

2022Yinpeng Dong, Shouwei Ruan et al.

If a Human Can See It, So Should Your System: Reliability Requirements for Machine Vision Components

2022Boyue Caroline Hu, Lina Marsso et al.

Noise-trained deep neural networks effectively predict human vision and its neural responses to challenging images

2021Hojin Jang, Devin McCormack et al.

Feature blindness: A challenge for understanding and modelling visual object recognition

2021Gaurav Malhotra, M. Dujmović et al.

Oculo-retinal dynamics can explain the perception of minimal recognizable configurations

2021L. Gruber, S. Ullman et al.

Partial success in closing the gap between human and machine vision

2021Robert Geirhos, Kantharaju Narayanappa et al.

Qualitative similarities and differences in visual object representations between brains and deep networks

2021Georgin Jacob, R. Pramod et al.

From Observed Action Identity to Social Affordances.

2021G. Orban, M. Lanzilotto et al.

Learning Transferable Visual Models From Natural Language Supervision

2021Alec Radford, Jong Wook Kim et al.

Showing 20 of 80 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

Prior WorkLearning Object-Centric Spatial Reasoning for Sequential Manipulation in Cluttered Environments

7.0

Prior WorkBeyond Pedestrians: Caption-Guided CLIP Framework for High-Difficulty Video-based Person Re-Identification

7.0

Prior WorkDo Machines Fail Like Humans? A Human-Centred Out-of-Distribution Spectrum for Mapping Error Alignment

7.0

Extension

Builds On ThisHumanEgo: Zero-Shot Robot Learning from Minutes of Human Egocentric Videos

0.0

Builds On ThisEXPLORE-Bench: Egocentric Scene Prediction with Long-Horizon Reasoning

4.0

Builds On ThisAdaptive Human-AI Coordination via Hierarchical Action Disentanglement

0.0

Builds On ThisInteraction Locality in Hierarchical Recursive Reasoning

5.0

Builds On ThisHuman-AI Ensembles Improve Deepfake Detection in Low-to-Medium Quality Videos

4.0

Builds On ThisNon-verbal Real-time Human-AI Interaction in Constrained Robotic Environments

6.0

Commercially relevant

Higher ViabilityLearning Whole-Body Human-Humanoid Interaction from Human-Human Demonstrations

8.0

Conflicting

none indexed

Owned Distribution

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Get the weekly shortlist of commercializable papers, benchmark movers, and proof receipts that matter for product execution.

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: 2603.08317
Route: /paper/human-ai-divergence-in-ego-centric-action-recognition-under-spatial-and-spatiotemporal-manipulations
Active tab: read
Artifact: human-ai-divergence-in-ego-centric-action-recognition-under-spatial-and-spatiotemporal-manipulations

Available agents

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

API/MCP endpoints

REST paper pack API/api/v1/paper/human-ai-divergence-in-ego-centric-action-recognition-under-spatial-and-spatiotemporal-manipulations/paper-pack
REST build passport API/api/v1/paper/human-ai-divergence-in-ego-centric-action-recognition-under-spatial-and-spatiotemporal-manipulations/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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  "arxiv_id": "2603.08317",
  "canonical_route": "/paper/human-ai-divergence-in-ego-centric-action-recognition-under-spatial-and-spatiotemporal-manipulations",
  "active_tab": "synced from current hash by the drawer client",
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  "endpoints": {
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    "build_passport": "/api/v1/paper/human-ai-divergence-in-ego-centric-action-recognition-under-spatial-and-spatiotemporal-manipulations/build-passport",
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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/human-ai-divergence-in-ego-centric-action-recognition-under-spatial-and-spatiotemporal-manipulations

stale

Proof freshness: stale
Proof status: unverified
Display score: 7/10
Last proof check: 2026-04-02
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 0
Source count: 0
Coverage: 17%

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

Human-AI Divergence in Ego-centric Action Recognition under Spatial and Spatiotemporal Manipulations

Canonical ID human-ai-divergence-in-ego-centric-action-recognition-under-spatial-and-spatiotemporal-manipulations | Route /paper/human-ai-divergence-in-ego-centric-action-recognition-under-spatial-and-spatiotemporal-manipulations

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/paper/human-ai-divergence-in-ego-centric-action-recognition-under-spatial-and-spatiotemporal-manipulations

MCP example

{
  "tool": "get_paper",
  "arguments": {
    "arxiv_id": "2603.08317"
  }
}

source_context

{
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  "mode": "paper",
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  "paper_ref": "human-ai-divergence-in-ego-centric-action-recognition-under-spatial-and-spatiotemporal-manipulations",
  "topic_slug": null,
  "benchmark_ref": null,
  "dataset_ref": null
}

Buildability Receipt

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

Paper proof page receipt window

Watch and verify: Human-AI Divergence in Ego-centric Action Recognition under Spatial and Spatiotemporal Manipulations

/buildability/human-ai-divergence-in-ego-centric-action-recognition-under-spatial-and-spatiotemporal-manipulations

Watchwatch

Subject: Human-AI Divergence in Ego-centric Action Recognition under Spatial and Spatiotemporal Manipulations

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/human-ai-divergence-in-ego-centric-action-recognition-under-spatial-and-spatiotemporal-manipulations

Paper ref

human-ai-divergence-in-ego-centric-action-recognition-under-spatial-and-spatiotemporal-manipulations

arXiv id

2603.08317

Freshness

Generated at

2026-04-02T02:30:40.136Z

Evidence freshness

stale

Last verification

2026-04-02T02:30:40.136Z

Sources

References

Coverage

17%

Hash state

Lineage hash

2f3ddd0135385bf6c12ca6c11496cebd2bdbbf5c40127b1e1a02609c5382459b

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: proof_status
Missing: distribution_readiness_scores
Missing: paper_extraction_scorecards
Unknown: distribution readiness has not been computed yet
Unknown: proof verification has not been recorded 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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Human-AI Divergence in Ego-centric Action Recognition under Spatial and Spatiotemporal Manipulations

Human-AI Divergence in Ego-centric Action Recognition under Spatial and Spatiotemporal Manipulations

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