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REVIEW 3 major objections 4 minor 68 references

Engagement Prediction of Short Videos with Large Multimodal Models

T0 review · 3 major / 4 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read Large multimodal models with audio input predict short-video engagement better than visual-only models and prior feature-based pipelines.

desk verdict Solid challenge report with a believable headline result, but the audio-attribution claim is not backed by an ablation. read the letter →

arxiv 2508.02516 v2 pith:PVEUD3G5 submitted 2025-08-04 cs.CV

Wei Sun , Linhan Cao , Yuqin Cao , Weixia Zhang , Wen Wen , Kaiwei Zhang , Zijian Chen , Fangfang Lu
show 2 more authors
Xiongkuo Min Guangtao Zhai
This is my paper · ORCID
classification cs.CV
keywords videoengagementpredictionlargemultimodalmodelsaudio-visualunderstandingcontinuationrateSnapUGCcold-startregressionshort-form
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper sets out to show that general-purpose large multimodal models can predict the engagement continuation rate of short videos before any user-interaction data exists. On the SnapUGC benchmark, an audio-visual-language model (VideoLLaMA2) and a vision-language model (Qwen2.5-VL), both fed keyframes plus title and description, outperform a purpose-built two-stage feature pipeline, with VideoLLaMA2 additionally taking background audio. The authors attribute that lead to background audio, with VideoLLaMA2 reaching SROCC 0.691 versus Qwen2.5-VL's 0.665. If the result holds, cold-start engagement prediction can move away from handcrafted multimodal features and toward end-to-end multimodal models.

What carries the argument

The object that carries the argument is the multimodal prompt: keyframes are encoded into visual tokens, the audio track into spectrogram tokens, and title and description into text, all placed inside a natural-language instruction that asks for the engagement continuation rate. VideoLLaMA2 averages the final-layer hidden states of its decoder and maps that average to a scalar through a two-layer MLP with an MSE loss, while Qwen2.5-VL learns to generate the numeric score as tokens with a cross-entropy loss. The intended comparison between the audio-inclusive and audio-exclusive branches is what supports the paper's conclusion about sound.

What would settle it

Run VideoLLaMA2 on SnapUGC with the audio tokens masked or removed, and run Qwen2.5-VL with an added audio branch; if their scores stay unequal or reverse, audio is not the unique cause of the gap.

Watch

Extended reading notes

Core claim

On the SnapUGC test set, VideoLLaMA2, prompted with eight keyframes, title and description, and a spectrogram of the audio track, predicts ECR with SROCC 0.691 and PLCC 0.701, beating Qwen2.5-VL (0.665 and 0.662) and the prior two-stage baseline (0.657 and 0.665). The paper also reports that regressing the averaged hidden state through a two-layer MLP is better than generating the score as output tokens (Qwen2.5-VL feature-based 0.674/0.679 versus token-based 0.665/0.662), that eight frames outperform five, and that full training data outperform 60 percent. Ensembling four model variants produces a final score of 0.710 and first place in the EVQA-SnapUGC challenge.

Load-bearing premise

The paper treats VideoLLaMA2 and Qwen2.5-VL as identical except for audio, so the measured gap is credited to sound; no test in the paper removes audio from one model or adds it to the other.

Editorial extensions

If this is right

  • End-to-end large multimodal models can replace the two-stage extract-features-then-regress recipe for cold-start engagement prediction.
  • Including background audio is worth a measurable gain: the audio-visual branch leads the vision-language branch by roughly 0.03 in SROCC and 0.04 in PLCC.
  • For fine-grained score prediction, a lightweight regression head on hidden states beats having the model generate the number as text.
  • Using more keyframes and more training data both push engagement prediction upward.
  • Ensembling several LMM variants gives a further boost, lifting the final score from 0.695 to 0.710.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The paper's core comparison does not isolate audio: the two models differ in architecture, training procedure, and regression head, so the audio attribution is a hypothesis rather than a demonstrated mechanism.
  • If audio is the real driver, engagement prediction for music-heavy platforms should include a dedicated audio branch, while platforms with mostly speech-over-visuals may get less benefit.
  • The feature-based-versus-token regression result suggests a transferable recipe: keep the LMM's decoder lightly trained and fit a small MLP head, which is cheaper than full token-level fine-tuning.
  • A direct audio-masking ablation on the same backbone is the natural next experiment, and its outcome would decide whether the gap is modality or model.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 4 minor

Summary. The paper investigates the use of large multimodal models (LMMs) for cold-start short-video engagement prediction, specifically predicting Engagement Continuation Rate (ECR) on the SnapUGC dataset. Two LMMs are compared: VideoLLaMA2 (audio-visual-language) with a feature-based MLP regression head, and Qwen2.5-VL (visual-language) with token-based regression. The authors report that VideoLLaMA2 achieves SROCC 0.691 / PLCC 0.701, outperforming Qwen2.5-VL (0.665/0.662) and the prior two-stage baseline Li24 (0.657/0.665). Ablations on number of input frames, training data proportion, and regression strategy are presented, and an ensemble of four models is reported to achieve first place in the ICCV VQualA 2025 EVQA-SnapUGC Challenge. The paper concludes that audio features play a critical role in engagement prediction.

Significance. If the empirical findings are reliable, the paper provides a useful demonstration that end-to-end LMMs can be competitive with or superior to two-stage feature-based pipelines for short-video engagement prediction, and the ensembling result is an externally validated engineering achievement with code released. The ablations on frame count, training data, and regression head are valuable for practitioners. However, the central interpretive claim that audio is the cause of VideoLLaMA2's advantage is not supported by the experiments as designed, because the two compared LMMs differ in several other dimensions simultaneously. The paper's main positive contribution is the empirical comparison and the challenge result; the audio-attribution conclusion needs either supporting evidence or careful reframing.

major comments (3)
  1. [Section 4.5 and Conclusion] The claim that VideoLLaMA2's superiority over Qwen2.5-VL 'highlights the critical role of audio features' is not supported by the presented experiments. The two models differ not only in audio input but also in base LLM architecture, regression strategy (MLP head vs. token generation), training protocol (frozen vision encoder with 5e-5 LR vs. full fine-tuning with 2e-6/1e-5 LR), and input resolution. Table 4 shows that switching Qwen2.5-VL from token-based to feature-based regression changes its final score from 0.664 to 0.676, reducing the gap to VideoLLaMA2 (0.695) from 0.031 to 0.019. There is no ablation that removes audio from VideoLLaMA2 while holding everything else fixed, nor one that adds audio to Qwen2.5-VL. Please add such an ablation (e.g., VideoLLaMA2 without the audio track) or, failing that, weaken the conclusion to state that the audio-visual-language model outperformed the visual-language model without attributing the difference specifically to audio.
  2. [Tables 1-4] All results are reported as single-run point estimates with no error bars, multiple seeds, or significance tests. Differences such as SROCC 0.691 vs. 0.674 (VideoLLaMA2 vs. feature-based Qwen2.5-VL) or 0.691 vs. 0.686 (Table 2) may be within run-to-run variation, especially given the different training budgets and hyperparameters. Please report the mean and standard deviation over at least three training runs, or apply a significance test (e.g., bootstrap on the test set or paired test across seeds), so readers can judge whether the reported ordering is statistically reliable.
  3. [Section 4.7 and Table 5] There is an inconsistency between the text and the table: Section 4.7 states 'our method ... achieves the highest final score of 0.714', but Table 5 lists the final score as 0.710, with 0.714 being the PLCC value. Please correct this. In addition, the description 'ensembles four models, including three VideoLLaMA2 variants (from Table 2 and Table 3) and one Qwen2.5-VL model' is ambiguous, because the variants in Tables 2 and 3 differ in frame count and training proportion rather than being independently trained models; clarify whether the ensemble members are distinct trained checkpoints and how their predictions were combined.
minor comments (4)
  1. [Section 3.1] The phrase 'the i-the keyframe' in the definition of x_i appears to be a typo; it should read 'the i-th keyframe'.
  2. [Section 4.5] The statement that VideoLLaMA2 'significantly outperforms' Qwen2.5-VL uses 'significantly' without statistical evidence; consider using 'consistently outperforms' or providing significance tests.
  3. [Section 2.2] The related work section cites Li et al. [21] for the SnapUGC dataset, but the description of the cross-modal attention architecture is brief; adding one or two sentences on how the proposed LMM-based approach differs from or improves upon this two-stage method would help position the contribution.
  4. [Table 1] The baseline 'MinimalisticVQA' is described as being retrained on SnapUGC, but the table caption does not state whether the LMM results are also from models trained on SnapUGC; specify the training condition for each row for clarity.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the engagement predictions are direct held-out evaluations on the external SnapUGC dataset, and the audio-attribution claim is confounded but not definitionally circular.

full rationale

The derivation chain is entirely empirical and self-contained: both LMMs are trained on the external SnapUGC training split and scored on the held-out test split, with external baselines (MinimalisticVQA and Li24) retrained under the same protocol. The paper's central performance claim (VideoLLaMA2 SROCC 0.691/PLCC 0.701 versus Qwen2.5-VL 0.665/0.662) is a direct measurement, not a quantity reconstructed from fitted inputs. No equation defines the output in terms of the target, no parameter is fitted to the test labels, and no result is justified solely by a self-citation chain: VideoLLaMA2 and Qwen2.5-VL are external model families, and the SnapUGC labels and test split are external. The conclusion that audio is critical is confounded because the two models differ in base architecture, regression head, and training protocol, and no audio ablation is run; that is an attribution and correctness weakness, not a circular derivation. The ensemble's reported 0.714 in Section 4.7 versus 0.710 in Table 5 is also a consistency slip, not a circular step. Accordingly, the circularity score is 0.

Assumptions & free parameters 5 free parameters · 4 assumptions · 0 invented entities

The paper is purely empirical; it introduces no new entities or derived constants. The central claims rest on dataset quality, sampling choices, and the comparability of the two LMMs, which are listed above.

free parameters (5)
  • Number of input keyframes N = 8
    Chosen by hand in Section 3.1; ablation in Table 2 shows 8 frames gives 0.695 vs 0.690 for 5 frames, a small difference that the paper interprets as supporting more frames.
  • Training epochs = 1 for both models
    Implementation details in Section 4.1; no epoch search reported, and results may depend on this choice.
  • Learning rates = 5e-5 (VideoLLaMA2); 2e-6 vision / 1e-5 other (Qwen2.5-VL)
    Chosen in Section 4.1; not tuned systematically in the paper.
  • Batch sizes = 12 (VideoLLaMA2), 16 (Qwen2.5-VL)
    Section 4.1; chosen by hardware constraints.
  • Ensemble composition = Four models: three VideoLLaMA2 variants and one Qwen2.5-VL; weighting unspecified
    Section 4.7 reports the ensemble result but does not state how predictions were combined; the challenge score depends on this choice.
assumptions (4)
  • domain assumption SnapUGC ECR labels are reliable ground-truth engagement scores
    The paper trains and evaluates against ECR from SnapUGC (Section 4.2) without auditing label noise or bias.
  • domain assumption Eight uniformly sampled keyframes from the video start capture enough visual content for ECR prediction
    Section 3.1 fixes N=8; the 5 vs 8 ablation (Table 2) only tests frame count, not sampling strategy.
  • ad hoc to paper The two LMMs differ only in audio modality for the purpose of attributing the performance gap
    Section 4.5 attributes the gap to audio, but base models, regression heads, and training protocols differ; this comparability assumption is unstated and load-bearing.
  • ad hoc to paper Freezing the vision encoder in VideoLLaMA2 while fully fine-tuning Qwen2.5-VL does not disadvantage either model unfairly
    Section 4.1 uses different training strategies; the paper does not justify that this choice is neutral for the comparison.

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Pith. "Pith review of Engagement Prediction of Short Videos with Large Multimodal Models." pith.science (2026). https://pith.science/paper/PVEUD3G5

@misc{pith2026250802516,
  author       = {Pith},
  title        = {Pith review of: Engagement Prediction of Short Videos with Large Multimodal Models},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/PVEUD3G5}},
  note         = {Machine review of arXiv:2508.02516}
}
read the original abstract

The rapid proliferation of user-generated content (UGC) on short-form video platforms has made video engagement prediction increasingly important for optimizing recommendation systems and guiding content creation. However, this task remains challenging due to the complex interplay of factors such as semantic content, visual quality, audio characteristics, and user background. Prior studies have leveraged various types of features from different modalities, such as visual quality, semantic content, background sound, etc., but often struggle to effectively model their cross-feature and cross-modality interactions. In this work, we empirically investigate the potential of large multimodal models (LMMs) for video engagement prediction. We adopt two representative LMMs: VideoLLaMA2, which integrates audio, visual, and language modalities, and Qwen2.5-VL, which models only visual and language modalities. Specifically, VideoLLaMA2 jointly processes key video frames, text-based metadata, and background sound, while Qwen2.5-VL utilizes only key video frames and text-based metadata. Trained on the SnapUGC dataset, both models demonstrate competitive performance against state-of-the-art baselines, showcasing the effectiveness of LMMs in engagement prediction. Notably, VideoLLaMA2 consistently outperforms Qwen2.5-VL, highlighting the importance of audio features in engagement prediction. By ensembling two types of models, our method achieves first place in the ICCV VQualA 2025 EVQA-SnapUGC Challenge on short-form video engagement prediction. The code is available at https://github.com/sunwei925/LMM-EVQA.git.

Figures

Figures reproduced from arXiv: 2508.02516 by the authors.

Figure 1
Figure 1. Overview of the proposed ensemble framework for engagement continuation rate prediction. The model integrates two large [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗

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Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.