ARXIV:2603.13056 · EMOTION RECOGNITION · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

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

Team RAS in 10th ABAW Competition: Multimodal Valence and Arousal Estimation Approach

arXiv

A multimodal approach for continuous emotion recognition using face, behavior, and audio data.

Blocked on Code›Score4.0Evidence unverified

Opportunity summary

Pain A multimodal approach for continuous emotion recognition using face, behavior, and audio data.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

A multimodal approach for continuous emotion recognition using face, behavior, and audio data. We present a multimodal method for valence-arousal estimation ITW.

METHOD

Full abstract

Continuous emotion recognition in terms of valence and arousal under in-the-wild (ITW) conditions remains a challenging problem due to large variations in appearance, head pose, illumination, occlusions, and subject-specific patterns of affective expression. We present a multimodal method for valence-arousal estimation ITW. Our method combines three complementary modalities: face, behavior, and audio. The face modality relies on GRADA-based frame-level embeddings and Transformer-based temporal regression. We use Qwen3-VL-4B-Instruct to extract behavior-relevant information from video segments, while Mamba is used to model temporal dynamics across segments. The audio modality relies on WavLM-Large with attention-statistics pooling and includes a cross-modal filtering stage to reduce the influence of unreliable or non-speech segments. To fuse modalities, we explore two fusion strategies: a Directed Cross-Modal Mixture-of-Experts Fusion Strategy that learns interactions between modalities with adaptive weighting, and a Reliability-Aware Audio-Visual Fusion Strategy that combines visual features at the frame-level while using audio as complementary context. The results are reported on the Aff-Wild2 dataset following the 10th Affective Behavior Analysis in-the-Wild (ABAW) challenge protocol. Experiments demonstrate that the proposed multimodal fusion strategy achieves a Concordance Correlation Coefficient (CCC) of 0.658 on the Aff-Wild2 development set.

RESULT

ScienceToStartup currently rates this 4.0/10 on the public viability pass. The results are reported on the Aff-Wild2 dataset following the 10th Affective Behavior Analysis in-the-Wild (ABAW) challenge protocol.

WHY NOW

Emotion Recognition moved forward this cycle; last verified April 2026. Public score 4.0/10.

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

Opportunity summary

Score4.0

PainA multimodal approach for continuous emotion recognition using face, behavior, and audio data.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

A multimodal approach for continuous emotion recognition using face, behavior, and audio data.

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

ARXIV:2603.13056 · EMOTION RECOGNITION · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

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

Team RAS in 10th ABAW Competition: Multimodal Valence and Arousal Estimation Approach

arXiv

A multimodal approach for continuous emotion recognition using face, behavior, and audio data.

Blocked on Code›Score4.0Evidence unverified

Opportunity summary

Pain A multimodal approach for continuous emotion recognition using face, behavior, and audio data.

Evidence 0 refs | 0 sources | 17% coverage

Blocker Evidence unverified

Open Build Read PDF Signal Canvas Track

PROBLEM

A multimodal approach for continuous emotion recognition using face, behavior, and audio data. We present a multimodal method for valence-arousal estimation ITW.

METHOD

Full abstract

RESULT

WHY NOW

Emotion Recognition moved forward this cycle; last verified April 2026. Public score 4.0/10.

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

Opportunity summary

Score4.0

PainA multimodal approach for continuous emotion recognition using face, behavior, and audio data.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

A multimodal approach for continuous emotion recognition using face, behavior, and audio data.

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

Paper Pack

10.48550/arXiv.2603.13056

Team RAS in 10th ABAW Competition: Multimodal Valence and Arousal Estimation Approach

A multimodal approach for continuous emotion recognition using face, behavior, and audio data.

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 fallback44 indexednot indexed

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

Viability

4.0

Time to MVP

MVP estimate missing

Commercial

No commercial flags on file

Export

Preparing verified analysis

lens / founder

PROBLEM

A multimodal approach for continuous emotion recognition using face, behavior, and audio data. We present a multimodal method for valence-arousal estimation ITW.

METHOD

RESULT

WHY NOW

Emotion Recognition moved forward this cycle; last verified April 2026. Public score 4.0/10.

Claim map

Abstract-backed public claims while anchored extraction refreshes.

Strong 0Mixed 0Weak 4

Evidencepartial
A multimodal approach for continuous emotion recognition using face, behavior, and audio data. We present a multimodal method for valence-arousal estimation ITW.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Continuous emotion recognition in terms of valence and arousal under in-the-wild (ITW) conditions remains a challenging problem due to large variations in appearance, head pose, illumination, occlusions, and subject-specific patterns of affective expression. We present a multimodal method for valence-arousal estimation ITW.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
ScienceToStartup currently rates this 4.0/10 on the public viability pass. The results are reported on the Aff-Wild2 dataset following the 10th Affective Behavior Analysis in-the-Wild (ABAW) challenge protocol.
Implicationpartial
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
Verificationpartial
partial
Evidencepartial
Emotion Recognition moved forward this cycle; last verified April 2026. Public score 4.0/10.
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

Emotion Recognition

Competitors

not indexed

Competitive landscape

A multimodal approach for continuous emotion recognition using face, behavior, and audio data.

Segment

Emotion Recognition

Adoption evidence

No public code link in the paper record yet

Commercial read

4.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

Buzz

No indexed public discussion is attached to 2603.13056 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(44)

Zero-Shot Multimodal Compound Expression Recognition Approach Using Off-the-Shelf Large Visual-Language Models

2025E. Ryumina, Maxim Markitantov et al.

Dynamic Temporal Gating Networks for Cross-Modal Valence-Arousal Estimation

2025Yubeen Lee, Sangeun Lee et al.

From Emotions to Violence: Multimodal Fine-Grained Behavior Analysis at the 9th ABAW

2025D. Kollias, S. Zafeiriou et al.

Leveraging Lightweight Facial Models and Textual Modality in Audio-Visual Emotional Understanding in-the-Wild

2025Andrey V. Savchenko, L. V. Savchenko

Advancements in Affective and Behavior Analysis: The 8th ABAW Workshop and Competition

2025Dimitrios Kollias, Panagiotis Tzirakis et al.

Interactive Multimodal Framework with Temporal Modeling for Emotion Recognition

2025Jun Yu, Yongqi Wang et al.

United We Stand, Divided We Fall: Handling Weak Complementarity for Audio-Visual Emotion Recognition in Valence-Arousal Space

2025Gnana Praveen Rajasekhar, Jahangir Alam et al.

DVD: A Comprehensive Dataset for Advancing Violence Detection in Real-World Scenarios

2025D. Kollias, D. C. Senadeera et al.

Emotion Recognition with CLIP and Sequential Learning

2025Weiwei Zhou, Chenkun Ling et al.

MOSS-6: a multi-label dataset and deep learning model for detecting diverse social support-seeking behaviours in online mental health communities

2025Jihye Kim, Onyu Park et al.

Behaviour4All: in-the-wild Facial Behaviour Analysis Toolkit

2024Dimitrios Kollias, Chunchang Shao et al.

7th ABAW Competition: Multi-Task Learning and Compound Expression Recognition

2024D. Kollias, S. Zafeiriou et al.

Odyssey 2024 - Speech Emotion Recognition Challenge: Dataset, Baseline Framework, and Results

2024Lucas Goncalves, Ali N. Salman et al.

Multi-modal Arousal and Valence Estimation under Noisy Conditions

2024D. Dresvyanskiy, Maxim Markitantov et al.

Language-guided Multi-modal Emotional Mimicry Intensity Estimation

2024Feng Qiu, Wei Zhang et al.

An Effective Ensemble Learning Framework for Affective Behaviour Analysis

2024Wei Zhang, Feng Qiu et al.

The 6th Affective Behavior Analysis in-the-wild (ABAW) Competition

2024D. Kollias, Panagiotis Tzirakis et al.

Distribution Matching for Multi-Task Learning of Classification Tasks: a Large-Scale Study on Faces & Beyond

2024D. Kollias, V. Sharmanska et al.

A Dual-Direction Attention Mixed Feature Network for Facial Expression Recognition

2023Saining Zhang, Yuhang Zhang et al.

EmotiEffNets for Facial Processing in Video-based Valence-Arousal Prediction, Expression Classification and Action Unit Detection

2023A. Savchenko

Showing 20 of 44 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 WorkTeam LEYA in 10th ABAW Competition: Multimodal Ambivalence/Hesitancy Recognition Approach

4.0

Prior WorkFoundation Model Embeddings Meet Blended Emotions: A Multimodal Fusion Approach for the BLEMORE Challenge

4.0

Extension

none indexed

Commercially relevant

Higher ViabilitySolution to the 10th ABAW Expression Recognition Challenge: A Robust Multimodal Framework with Safe Cross-Attention and Modality Dropout

7.0

Higher ViabilityMultimodal Emotion Recognition via Bi-directional Cross-Attention and Temporal Modeling

6.0

Higher ViabilityA Two-Stage Dual-Modality Model for Facial Emotional Expression Recognition

7.0

Higher ViabilityHSEmotion Team at ABAW-10 Competition: Facial Expression Recognition, Valence-Arousal Estimation, Action Unit Detection and Fine-Grained Violence Classification

6.0

Higher ViabilityAnchoring Emotions in Text: Robust Multimodal Fusion for Mimicry Intensity Estimation

7.0

Higher ViabilityStage-Adaptive Reliability Modeling for Continuous Valence-Arousal Estimation

7.0

Higher ViabilityConflict-Aware Multimodal Fusion for Ambivalence and Hesitancy Recognition

7.0

Higher ViabilityHierarchical Granularity Alignment and State Space Modeling for Robust Multimodal AU Detection in the Wild

7.0

Conflicting

none indexed

Related Resources

Owned Distribution

Subscribe to the weekly brief

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: 2603.13056
Route: /paper/team-ras-in-10th-abaw-competition-multimodal-valence-and-arousal-estimation-approach
Active tab: read
Artifact: team-ras-in-10th-abaw-competition-multimodal-valence-and-arousal-estimation-approach

Available agents

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

API/MCP endpoints

REST paper pack API/api/v1/paper/team-ras-in-10th-abaw-competition-multimodal-valence-and-arousal-estimation-approach/paper-pack
REST build passport API/api/v1/paper/team-ras-in-10th-abaw-competition-multimodal-valence-and-arousal-estimation-approach/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

{
  "contract_version": "paper-r2",
  "paper_id": "d49dd3f9-0855-47d6-aad9-b0978ed971f3",
  "arxiv_id": "2603.13056",
  "canonical_route": "/paper/team-ras-in-10th-abaw-competition-multimodal-valence-and-arousal-estimation-approach",
  "active_tab": "synced from current hash by the drawer client",
  "selected_artifact": "team-ras-in-10th-abaw-competition-multimodal-valence-and-arousal-estimation-approach",
  "endpoints": {
    "paper_pack": "/api/v1/paper/team-ras-in-10th-abaw-competition-multimodal-valence-and-arousal-estimation-approach/paper-pack",
    "build_passport": "/api/v1/paper/team-ras-in-10th-abaw-competition-multimodal-valence-and-arousal-estimation-approach/build-passport",
    "mcp_resource": "sciencetostartup://surfaces/paper-workspace"
  }
}

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/team-ras-in-10th-abaw-competition-multimodal-valence-and-arousal-estimation-approach

stale

Proof freshness: stale
Proof status: unverified
Display score: 4/10
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

Team RAS in 10th ABAW Competition: Multimodal Valence and Arousal Estimation Approach

Canonical ID team-ras-in-10th-abaw-competition-multimodal-valence-and-arousal-estimation-approach | Route /paper/team-ras-in-10th-abaw-competition-multimodal-valence-and-arousal-estimation-approach

REST example

curl https://sciencetostartup.com/api/v1/agent-handoff/paper/team-ras-in-10th-abaw-competition-multimodal-valence-and-arousal-estimation-approach

MCP example

{
  "tool": "get_paper",
  "arguments": {
    "arxiv_id": "2603.13056"
  }
}

source_context

{
  "surface": "paper",
  "mode": "paper",
  "query": "Team RAS in 10th ABAW Competition: Multimodal Valence and Arousal Estimation Approach",
  "normalized_query": "2603.13056",
  "route": "/paper/team-ras-in-10th-abaw-competition-multimodal-valence-and-arousal-estimation-approach",
  "paper_ref": "team-ras-in-10th-abaw-competition-multimodal-valence-and-arousal-estimation-approach",
  "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

Not build-ready: Team RAS in 10th ABAW Competition: Multimodal Valence and Arousal Estimation Approach

/buildability/team-ras-in-10th-abaw-competition-multimodal-valence-and-arousal-estimation-approach

Ignoreblocked

Subject: Team RAS in 10th ABAW Competition: Multimodal Valence and Arousal Estimation Approach

Verdict

Ignore

Verdict is Ignore because current viability and proof state do not clear the buildability gate.

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/team-ras-in-10th-abaw-competition-multimodal-valence-and-arousal-estimation-approach

Paper ref

team-ras-in-10th-abaw-competition-multimodal-valence-and-arousal-estimation-approach

arXiv id

2603.13056

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

1532ab4a39dfc78f484b01781740d74206bd43bc0a8e0df1c647cd951a760eac

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.

{
  "@context": "https://schema.org",
  "@graph": [
    {
      "@type": "WebPage",
      "@id": "https://sciencetostartup.com/paper/team-ras-in-10th-abaw-competition-multimodal-valence-and-arousal-estimation-approach#webpage",
      "url": "https://sciencetostartup.com/paper/team-ras-in-10th-abaw-competition-multimodal-valence-and-arousal-estimation-approach",
      "name": "Team RAS in 10th ABAW Competition: Multimodal Valence and Arousal Estimation Approach",
      "description": "A multimodal approach for continuous emotion recognition using face, behavior, and audio data.",
      "isPartOf": {
        "@id": "https://sciencetostartup.com/#website"
      }
    },
    {
      "@type": "ScholarlyArticle",
      "@id": "https://sciencetostartup.com/paper/team-ras-in-10th-abaw-competition-multimodal-valence-and-arousal-estimation-approach#scholarlyArticle",
      "headline": "Team RAS in 10th ABAW Competition: Multimodal Valence and Arousal Estimation Approach",
      "description": "A multimodal approach for continuous emotion recognition using face, behavior, and audio data.",
      "url": "https://sciencetostartup.com/paper/team-ras-in-10th-abaw-competition-multimodal-valence-and-arousal-estimation-approach",
      "sameAs": "https://arxiv.org/abs/2603.13056",
      "identifier": {
        "@type": "PropertyValue",
        "propertyID": "arXiv",
        "value": "2603.13056"
      },
      "isAccessibleForFree": true,
      "isPartOf": {
        "@id": "https://sciencetostartup.com/#website"
      },
      "datePublished": "2026-03-13T15:06:14.000Z",
      "citation": [
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "e6a54b0db221a8c9b1593106658c2abac0a9e704"
          },
          "url": "https://www.semanticscholar.org/paper/e6a54b0db221a8c9b1593106658c2abac0a9e704"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "2cc7cce2afb731d71844e6b154932789707bdf36"
          },
          "url": "https://www.semanticscholar.org/paper/2cc7cce2afb731d71844e6b154932789707bdf36"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "cb7e32527530b01dc4402127b42eedd2ae5b8e8f"
          },
          "url": "https://www.semanticscholar.org/paper/cb7e32527530b01dc4402127b42eedd2ae5b8e8f"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "ec0abdedbb7d607bcb3610bc8222b81eaaa5a976"
          },
          "url": "https://www.semanticscholar.org/paper/ec0abdedbb7d607bcb3610bc8222b81eaaa5a976"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "3bc4d2c1291ba2dc9c1b8b2f3a16f5cc6517975b"
          },
          "url": "https://www.semanticscholar.org/paper/3bc4d2c1291ba2dc9c1b8b2f3a16f5cc6517975b"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "37db02c454cd7a01da1ca1aa6bb8801e538fe785"
          },
          "url": "https://www.semanticscholar.org/paper/37db02c454cd7a01da1ca1aa6bb8801e538fe785"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "e8026df28d6d7f3c8b4e43d3ac3b606b8cfb0a43"
          },
          "url": "https://www.semanticscholar.org/paper/e8026df28d6d7f3c8b4e43d3ac3b606b8cfb0a43"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "a9ef03e616222795d3c5f20164e2d67b8371325e"
          },
          "url": "https://www.semanticscholar.org/paper/a9ef03e616222795d3c5f20164e2d67b8371325e"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "15ce743750eeabccc3eaf86fbc6d93a790a2ea33"
          },
          "url": "https://www.semanticscholar.org/paper/15ce743750eeabccc3eaf86fbc6d93a790a2ea33"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "6d2d4db556f4f197d06dc4b17b4b7d5ee9814364"
          },
          "url": "https://www.semanticscholar.org/paper/6d2d4db556f4f197d06dc4b17b4b7d5ee9814364"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "7fd89f02f9b5caa9cfbb6b1301b8d88fff373d3f"
          },
          "url": "https://www.semanticscholar.org/paper/7fd89f02f9b5caa9cfbb6b1301b8d88fff373d3f"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "7f00de83fe8527dc157eadd6427b0e33c797d7d9"
          },
          "url": "https://www.semanticscholar.org/paper/7f00de83fe8527dc157eadd6427b0e33c797d7d9"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "44a30157f437065fd0672b1327edaa32a9239ce5"
          },
          "url": "https://www.semanticscholar.org/paper/44a30157f437065fd0672b1327edaa32a9239ce5"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "8dcbdfd2f53eb519e6e0becb9f3762f724c596fb"
          },
          "url": "https://www.semanticscholar.org/paper/8dcbdfd2f53eb519e6e0becb9f3762f724c596fb"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "a3abb210bb4c33aaf1eea9df0ff7df6eac729298"
          },
          "url": "https://www.semanticscholar.org/paper/a3abb210bb4c33aaf1eea9df0ff7df6eac729298"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "6017c7905cb150c16b1eeac17e404b452c7b210c"
          },
          "url": "https://www.semanticscholar.org/paper/6017c7905cb150c16b1eeac17e404b452c7b210c"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "bf98a07a70121ec89816515c3d8fd8394acc531e"
          },
          "url": "https://www.semanticscholar.org/paper/bf98a07a70121ec89816515c3d8fd8394acc531e"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "16d26f3adaf77d477b60b96081c488ae26abb730"
          },
          "url": "https://www.semanticscholar.org/paper/16d26f3adaf77d477b60b96081c488ae26abb730"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "dbee3c698a5684a4b33950a2a09ebc2595cde7c3"
          },
          "url": "https://www.semanticscholar.org/paper/dbee3c698a5684a4b33950a2a09ebc2595cde7c3"
        },
        {
          "@type": "ScholarlyArticle",
          "identifier": {
            "@type": "PropertyValue",
            "propertyID": "SemanticScholar",
            "value": "76653e3b6008d5a38524533f96d8a2a09aa36c68"
          },
          "url": "https://www.semanticscholar.org/paper/76653e3b6008d5a38524533f96d8a2a09aa36c68"
        }
      ],
      "additionalProperty": [
        {
          "@type": "PropertyValue",
          "propertyID": "viabilityScore",
          "value": 4
        },
        {
          "@type": "PropertyValue",
          "propertyID": "researchDomain",
          "value": "Emotion Recognition"
        }
      ]
    },
    {
      "@type": "BreadcrumbList",
      "itemListElement": [
        {
          "@type": "ListItem",
          "position": 1,
          "name": "Home",
          "item": "https://sciencetostartup.com"
        },
        {
          "@type": "ListItem",
          "position": 2,
          "name": "Emotion Recognition",
          "item": "https://sciencetostartup.com/topics"
        },
        {
          "@type": "ListItem",
          "position": 3,
          "name": "Team RAS in 10th ABAW Competition: Multimodal Valence and Ar",
          "item": "https://sciencetostartup.com/paper/team-ras-in-10th-abaw-competition-multimodal-valence-and-arousal-estimation-approach"
        }
      ]
    }
  ]
}

Team RAS in 10th ABAW Competition: Multimodal Valence and Arousal Estimation Approach

Team RAS in 10th ABAW Competition: Multimodal Valence and Arousal Estimation Approach

Claim map

Constellation map

Competitive landscape

Buzz

PDF

References(44)

Related Papers

Related Resources

Subscribe to the weekly brief

Build artifacts

Brief

Experiment plan

Validation checklist

Scientific founder

Translational engineer

Domain operator

GTM lead

Regulatory/clinical advisor

Timeline

Claim map

Constellation map

Competitive landscape

Buzz

PDF

References(44)

Related Papers

Related Resources

Subscribe to the weekly brief

Build artifacts

Brief

Experiment plan

Validation checklist

Scientific founder

Translational engineer

Domain operator

GTM lead

Regulatory/clinical advisor

Timeline