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arxiv: 2604.16214 · v1 · submitted 2026-04-17 · 💻 cs.CV

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GAViD: A Large-Scale Multimodal Dataset for Context-Aware Group Affect Recognition from Videos

Deepak Kumar , Abhishek Pratap Singh , Puneet Kumar , Xiaobai Li , Balasubramanian Raman
Authors on Pith no claims yet

Pith reviewed 2026-05-10 08:55 UTC · model grok-4.3

classification 💻 cs.CV
keywords group affectmultimodal datasetemotion recognitioncontext-awarevideo analysissocial interactionsaffective computingin-the-wild videos
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The pith

A new dataset supplies 5091 multimodal video clips with context annotations to support group affect recognition.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces the GAViD dataset to fill the gap in large-scale annotated resources for modeling how groups express emotions during real-world social interactions. It collects video and audio from in-the-wild clips, adds ternary valence labels plus discrete emotion categories, and layers on contextual metadata generated by VideoGPT along with human-marked action cues. A network named CAGNet is built on top of this data to fuse the multimodal streams with context, reaching 63.20 percent test accuracy. This matters because group affect arises from intertwined behaviors and surroundings, so better data should let models capture those dynamics more reliably than before. The dataset and code are released publicly so others can train and test similar systems.

Core claim

The central claim is that the GAViD dataset of 5091 video clips, equipped with video, audio, and contextual information and labeled for ternary valence and discrete emotions plus action cues, supplies the missing foundation for training context-aware models. The Context-Aware Group Affect Recognition Network (CAGNet) trained on this data achieves 63.20 percent accuracy on held-out test clips, matching current state-of-the-art levels for the task.

What carries the argument

The GAViD dataset acts as the central object, supplying aligned multimodal streams and context annotations that let CAGNet incorporate surrounding information into its predictions of group valence and emotion.

If this is right

  • Models can now be trained to weigh contextual cues when deciding group emotional states rather than relying on visual and audio features alone.
  • The combination of generated metadata and human action cues provides richer supervision signals for learning how behaviors and surroundings shape collective affect.
  • Public release of the clips, labels, and code removes the data barrier that previously slowed progress on in-the-wild group affect tasks.
  • CAGNet performance shows that multimodal fusion with explicit context yields results on par with existing specialized methods.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Researchers could test whether the same clips support fine-grained analysis of emotion transitions over time rather than static labels.
  • The dataset structure may transfer to related problems such as predicting group consensus or detecting conflict in social videos.
  • Adding longitudinal clips from the same groups could reveal how affect evolves across repeated interactions.
  • Systems trained on GAViD might improve downstream applications like automated moderation of online group calls or analysis of crowd responses at events.

Load-bearing premise

The human and VideoGPT annotations correctly capture the actual group affect and relevant context in the selected clips without major errors or selection biases.

What would settle it

An independent set of fresh human annotators labeling a random subset of the clips and showing low agreement with the published valence and emotion labels would falsify the assumption that the dataset annotations are reliable.

Figures

Figures reproduced from arXiv: 2604.16214 by Abhishek Pratap Singh, Balasubramanian Raman, Deepak Kumar, Puneet Kumar, Xiaobai Li.

Figure 1
Figure 1. Figure 1: Overview of data statistics. Left. Ethnicity distribution across the dataset. Middle. Gender distribution across the dataset. Right. Face count distribution across the dataset. Abbreviations: Middle-E = Middle Eastern, Latino-H = Latino Hispanic, W = Women, M = Men.Right. Per image detected face distribution. MLLM generates a scene’s contextual metadata that annotators verify in further phases. 2) Data Seg… view at source ↗
Figure 2
Figure 2. Figure 2: Overview of the GAViD annotation pipeline and interface. The diagram illustrates stages of data collection, sample video frames with valence, [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Schematic architecture of CAGNet for Group Affect Recognition. Input video clips are processed into frames, audio and textual context, followed [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Sample results for CAGNet on GAViD dataset under two settings: V+A and V+A+C. Pos: Positive, Neg: Negative and Neu: Neutral. V+A denotes [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
read the original abstract

Understanding affective dynamics in real-world social systems is fundamental to modeling and analyzing human-human interactions in complex environments. Group affect emerges from intertwined human-human interactions, contextual influences, and behavioral cues, making its quantitative modeling a challenging computational social systems problem. However, computational modeling of group affect in in-the-wild scenarios remains challenging due to limited large-scale annotated datasets and the inherent complexity of multimodal social interactions shaped by contextual and behavioral variability. The lack of comprehensive datasets annotated with multimodal and contextual information further limits advances in the field. To address this, we introduce the Group Affect from ViDeos (GAViD) dataset, comprising 5091 video clips with multimodal data (video, audio and context), annotated with ternary valence and discrete emotion labels and enriched with VideoGPT-generated contextual metadata and human-annotated action cues. We also present Context-Aware Group Affect Recognition Network (CAGNet) for multimodal context-aware group affect recognition. CAGNet achieves 63.20\% test accuracy on GAViD, comparable to state-of-the-art performance. The dataset and code are available at github.com/deepakkumar-iitr/GAViD.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The paper introduces the GAViD dataset comprising 5091 video clips with multimodal (video, audio, context) data, annotated with ternary valence and discrete emotion labels, enriched with VideoGPT-generated contextual metadata and human-annotated action cues. It also presents the Context-Aware Group Affect Recognition Network (CAGNet) achieving 63.20% test accuracy on GAViD, claimed to be comparable to state-of-the-art performance.

Significance. If the annotations are shown to be reliable, GAViD would constitute a useful large-scale multimodal resource for in-the-wild group affect recognition, addressing a noted scarcity of such datasets and supporting reproducibility via public code and data release. The central performance claim, however, cannot be fully assessed without the missing validation and evaluation details.

major comments (3)
  1. [Abstract] Abstract: The single reported accuracy of 63.20% is presented without any evaluation protocol details (train/test split sizes, class distribution for discrete emotions, or metric definition), baseline comparisons, or error analysis, rendering the 'comparable to state-of-the-art' claim unverifiable from the given text.
  2. [Dataset annotation description (likely §3)] Dataset annotation description (likely §3): No inter-annotator agreement statistics, expert validation subset results, or bias audit on clip sourcing are provided for the ternary valence, discrete emotion, or action cue labels, which directly undermines the dataset's utility as a reliable proxy for true group affect.
  3. [CAGNet experiments (likely §4 or §5)] CAGNet experiments (likely §4 or §5): The manuscript supplies no ablation studies on multimodal/context components, comparisons to prior group affect methods on GAViD, or analysis of VideoGPT metadata quality, so the 63.20% figure cannot be interpreted as evidence of genuine progress.
minor comments (2)
  1. [Abstract] Abstract: The dataset acronym expansion 'Group Affect from ViDeos' uses inconsistent capitalization that should be standardized.
  2. [Introduction] Introduction: A brief comparison table to existing group affect datasets would clarify the claimed novelty in scale and multimodal context.

Simulated Author's Rebuttal

3 responses · 0 unresolved

Thank you for the referee's constructive comments. We address each major point below and commit to revisions that will incorporate the suggested improvements to enhance the clarity and rigor of the manuscript.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The single reported accuracy of 63.20% is presented without any evaluation protocol details (train/test split sizes, class distribution for discrete emotions, or metric definition), baseline comparisons, or error analysis, rendering the 'comparable to state-of-the-art' claim unverifiable from the given text.

    Authors: We agree that the abstract, being concise, does not provide the necessary evaluation protocol details, making the performance claim difficult to verify from the abstract alone. We will revise the abstract to include key details such as the train/test split sizes and the metric definition. Additionally, we will enhance the experiments section to include class distribution information, baseline comparisons to prior methods, and error analysis to substantiate the claim of comparability to state-of-the-art performance. revision: yes

  2. Referee: [Dataset annotation description (likely §3)] Dataset annotation description (likely §3): No inter-annotator agreement statistics, expert validation subset results, or bias audit on clip sourcing are provided for the ternary valence, discrete emotion, or action cue labels, which directly undermines the dataset's utility as a reliable proxy for true group affect.

    Authors: We acknowledge that the current description of the dataset annotation process does not include inter-annotator agreement statistics or other validation measures. We will add these in the revised manuscript, including inter-annotator agreement statistics for the valence, discrete emotion, and action cue labels, results from an expert validation subset, and a bias audit on the clip sourcing process. revision: yes

  3. Referee: [CAGNet experiments (likely §4 or §5)] CAGNet experiments (likely §4 or §5): The manuscript supplies no ablation studies on multimodal/context components, comparisons to prior group affect methods on GAViD, or analysis of VideoGPT metadata quality, so the 63.20% figure cannot be interpreted as evidence of genuine progress.

    Authors: We agree that additional analyses are required to properly interpret the reported accuracy. We will add ablation studies on the multimodal and context components of CAGNet, direct comparisons against prior group affect methods evaluated on GAViD, and an analysis of the VideoGPT-generated metadata quality in the revised manuscript. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical dataset introduction and held-out accuracy measurement

full rationale

The paper introduces the GAViD dataset (5091 clips with annotations and metadata) and reports CAGNet's 63.20% test accuracy as an empirical result on held-out data. No derivation chain, equations, or self-citations reduce any claimed result to its inputs by construction. The accuracy is a direct measurement rather than a fitted or renamed quantity, and dataset properties are presented as collected facts, not derived tautologically. This is a standard non-circular dataset-plus-model paper.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The central claims rest on standard supervised learning assumptions (i.i.d. train/test splits, annotation reliability) and the representativeness of the collected clips; no new physical entities or ad-hoc free parameters beyond ordinary model hyperparameters are introduced.

axioms (2)
  • domain assumption Human and AI-generated annotations faithfully capture ground-truth group affect and context.
    Invoked when reporting labels and accuracy without further validation details.
  • domain assumption The 5091 clips constitute a representative sample of real-world group interactions.
    Required for the dataset to serve as a general benchmark.

pith-pipeline@v0.9.0 · 5523 in / 1421 out tokens · 82755 ms · 2026-05-10T08:55:19.570895+00:00 · methodology

discussion (0)

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