Key Frame Sampling Method

• Key frame sampling is a technique to select representative video frames that optimally preserve semantic content, reduce redundancy, and support various downstream tasks.
• It employs methods such as maximum volume, relevance-diversity optimization, and motion-guided distributions to balance frame diversity and temporal coverage.
• Evaluation focuses on metrics like downstream task accuracy, temporal balance, and redundancy minimization, with applications in summarization, QA, and compression.

A key frame sampling method is a computational technique for selecting a subset of frames—called key frames—from a video or sequence that (1) maximally preserves its semantic content, (2) reduces redundancy for a given downstream task, or (3) satisfies a particular information-theoretic, visual, or task-driven objective. Key frame sampling is foundational in video summarization, data-efficient video understanding, large-scale video QA, multimodal LLM pre-processing, video classification, video retrieval, frame-level video compression, and video generation.

1. Mathematical Formulations and Core Objectives

Key frame sampling algorithms frequently optimize well-defined objectives encoding relevance, diversity, or reconstruction performance. Distinct but related paradigms include:

• Maximum Volume Criterion: Algorithms such as MaxInfo (Li et al., 5 Feb 2025) select $r$ frames whose embedding matrix $Q_s(S,:)$ maximizes the parallelepiped volume,

$$\mathrm{Vol}\bigl(Q_s(S,:)\bigr) = \sqrt{\det\left(Q_s(S,:) Q_s(S,:)^T\right)},$$

thus maximizing embedding diversity.

• Relevance-Diversity (RD) Objective: AdaRD-Key (Zhang et al., 3 Oct 2025) maximizes the sum of prompt-conditioned frame relevance $R(f_i)$ and a log-determinant diversity term,

$$S^* = \arg\max_{S \subset [N], |S|=k} \left[\sum_{i\in S} R(f_i) + \lambda\log\det(E_S^T E_S + \varepsilon I)\right].$$

• Temporal (Coverage) Constraints: Methods like Adaptive Keyframe Sampling (AKS) (Tang et al., 28 Feb 2025) and KFS-Bench/ASCS (Li et al., 16 Dec 2025) add temporal coverage or scene balance rewards to ensure key frames are distributed across the video's timeline or semantically distinct scenes.
• Motion-Guided Distribution: MGSampler (Zhi et al., 2021) selects frames uniformly in the cumulative motion distribution $C(t)$ to ensure motion bursts are well-covered.
• Task-Driven Supervision: Some methods (e.g., those in speech or planning) directly use task loss, e.g. intermediate CTC-based key frame picking (Fan et al., 2023) or ELBO-based selection in KeyIn (Pertsch et al., 2019).

2. Algorithmic Strategies and Representative Methods

Key frame sampling approaches span unsupervised, task-driven, and prompt- or question-conditioned algorithms. Principal classes include:

• Clustering and Peak Picking: CNN + Temporal Segment Density Peaks Clustering (TSDPC) (Tang et al., 2022), attention-based feature clustering (Arslan et al., 2023), and cluster center selection ensure one key frame per semantic group.
• Maximum Volume Methods: MaxInfo (Li et al., 5 Feb 2025) and AdaRD-Key (Zhang et al., 3 Oct 2025) use greedy or MaxVol algorithms to maximize embedding submatrix volume, often after dimensionality reduction.
• Self-Adaptive, Query-Driven Sampling: AKS (Tang et al., 28 Feb 2025), ASCS (Li et al., 16 Dec 2025), AdaRD-Key (Zhang et al., 3 Oct 2025), and moment sampling (Chasmai et al., 18 Jun 2025) integrate video-language relevance, clustering, and balance metrics for context- or question-aware sampling under tight budgets.
• Motion-Based Sampling: MGSampler (Zhi et al., 2021) uses frame difference or optical flow magnitude to compute per-frame motion salience, then samples frames uniformly in motion-CDF to guarantee coverage of high-action intervals.
• Entropy and Information-Theoretic Methods: Key frame bins by global/segment-level entropy and eliminate redundancy using segment entropy distances (Algur et al., 2016).
• Graph-Based Signal Sampling: Fast graph sampling via Gershgorin disc alignment (Sahami et al., 2021) identifies E-optimal sets based on Laplacian eigenvalue lower bounds.
• Self-Supervised Reconstruction: FrameRS (Fu et al., 2023) trains a small MLP to select frame combinations maximizing overall clip reconstructibility under heavy masking, fixing retention to ~30%.
• Neural Network Methods: End-to-end neural mechanisms include autoencoder-attention clustering (Arslan et al., 2023), or hierarchical latent-variable models for differentiable discovery (Pertsch et al., 2019).

Table 1: Main Method Families and Exemplars

Method Family	Main Mechanism	Examples/arXiv IDs
Clustering/Peak Picking	Feature clustering, peak detection	(Tang et al., 2022, Arslan et al., 2023)
Volume/Determinant Max	MaxVol, log-det, diversity–relevance submodular optimization	(Li et al., 5 Feb 2025, Zhang et al., 3 Oct 2025)
Question-Adaptive Sampling	Visual-language similarity, adaptive tradeoff with clustering	(Tang et al., 28 Feb 2025, Li et al., 16 Dec 2025)
Motion/Activity-Based	Image-diff, optical flow, motion-CDF	(Zhi et al., 2021)
Entropy-Driven	Global/local entropy binning and redundancy elimination	(Algur et al., 2016)
Graph/Gershgorin Sampling	Laplacian sampling, E-optimality, disc alignment	(Sahami et al., 2021)
Self-Supervised/Autoencoders	MAE, autoencoding, variational latent models	(Fu et al., 2023, Pertsch et al., 2019)

3. Evaluation Metrics and Benchmarks

Key frame sampling methods are typically evaluated with respect to several criteria:

• Downstream Task Performance: Classification and QA accuracy (e.g., top-1 accuracy on UCF101, HMDB51 (Tang et al., 2022); QA accuracy on LongVideoBench, VideoMME (Tang et al., 28 Feb 2025, Li et al., 16 Dec 2025)).
• Sampling Fidelity and Efficiency: Frames retained vs. downstream accuracy; e.g., compressing video by 80–90% while maintaining 95–98% accuracy (Tang et al., 2022), or retaining only 30% of frames with negligible loss (Fu et al., 2023).
• Precision, Coverage, and Balance: The Unified Keyframe Sampling Score (UKSS) in KFS-Bench (Li et al., 16 Dec 2025) combines (i) key-frame precision (KFR), (ii) balanced scene recall (BSR), and (iii) balanced distribution similarity (BDS) to holistically measure sampling quality.
• Human Summary Approximation: $F_1$-score against reference summaries (Sahami et al., 2021), or matching ground-truth “keyframe windows” (Arslan et al., 2023).

4. Key Frame Sampling in Large-Scale Video Understanding

As video-LLMs (VLLMs) and multimodal LLMs scale to long-form videos, input context size imposes hard sampling bottlenecks. The most successful recent methods balance relevance (with respect to question or prompt), temporal and semantic coverage, and redundancy avoidance, to maximize utility per input token:

• Prompt-Relevance Maximization: AKS selects key frames by maximizing the sum of prompt-frame relevance and even temporal spread across the timeline (Tang et al., 28 Feb 2025).
• Relevance-Diversity Submodularity: AdaRD-Key adaptively interpolates between log-det diversity and prompt-conditional frame matching, using a gating switch when the query is broad or unfocused (Zhang et al., 3 Oct 2025).
• Scene and Coverage Awareness: KFS-Bench (Li et al., 16 Dec 2025) provides the first fine-grained benchmark for evaluating scene coverage in QA, demonstrating that balanced scene sampling and relevance–diversity tradeoff (ASCS method) provide consistent state-of-the-art accuracy.
• Moment Retrieval Integration: Moment Sampling (Chasmai et al., 18 Jun 2025) uses a lightweight moment retrieval model to assign soft frame-level relevance, combined with frame quality and diversity objectives; empirically, this method provides consistent +1–3 pp accuracy gains with small sampling budgets.

5. Domain-Specific and Advanced Applications

Key frame sampling finds application beyond classical video summarization:

• ASR (Speech Recognition): Intermediate-CTC guided selection of non-blank acoustic frames for self-attention and downsampling in Conformer-based end-to-end ASR, yielding $>60\%$ reduction in sequence length with minimal CER/WER sacrifice (Fan et al., 2023).
• Predictive Planning: Hierarchical latent-variable models (KeyIn) with differentiable keyframe inference are used to sparsify temporal representation in control, robotics, or forecasting (Pertsch et al., 2019).
• Video Synthesis and Interpolation: ViBiDSampler (Yang et al., 8 Oct 2024) treats provided key frames as hard boundary conditions for bidirectional diffusion models, using sequential on-manifold sampling for artifact-free interpolation between keyframes.
• Compression: FrameRS (Fu et al., 2023) uses a learned small MLP selector atop masked autoencoders to select the minimal frame subset for faithful video reconstruction, achieving high compression with competitive reconstruction fidelity.

6. Practical Considerations, Limitations, and Future Directions

• Efficiency: Sampling methods may aggregate over 10–20$\times$ less computation by reducing frame budgets (e.g., LSTM over M key frames vs. C3D/ResNet over full video (Tang et al., 2022)).
• Integration: Nearly all leading methods are training-free or require minimal fine-tuning, and are thus plug-and-play front-ends for any video backbone or VLLM.
• Trade-Offs: High diversity leads to broad coverage at the risk of relevance dilution; high relevance narrows focus but may miss multi-scene cues. Adaptive interpolation parameters, gating, and mixed-criterion designs aim to rectify this.
• Challenges: Temporal continuity, fine-grained event capture under hard budget constraints, and generalization to domain-specific tasks (e.g., medical, egocentric datasets) remain open research areas.
• Ablation and Robustness: Most empirical studies demonstrate monotonic gains from integrating more advanced sampling (e.g., MaxVol+diversity, prompt-adaptive gates). Robustness to frame-level annotation noise and deployment variants is increasingly studied (Li et al., 16 Dec 2025).

7. Summary Table: Core Features of Leading Key Frame Sampling Approaches

Approach	Driven by	Diversity Control	Context Adaptivity	Sampling Budget	Empirical Gains
MaxInfo	Embedding volume	MaxVol/rectangular volume	No	Fixed	+3–6 pp accuracy
AdaRD-Key	Relevance+logdet	Log-determinant, adaptive λ	Yes, gate for relevance	Fixed	+0.3–1.2 pp over baselines
AKS (Adaptive)	Prompt similarity	Hier. bin coverage	Yes, threshold recursion	Fixed	+2–5 pp over uniform
ASCS (KFS-Bench)	QA similarity	Clustering, QVRS-balancing	Yes, QVRS trade-off	Fixed	SOTA QA, UKSS
MGSampler	Motion salience	CDF-uniform in motion score	No	Fixed	+1–2 pp action recognition
FrameRS	Reconstruct. loss	MLP over feature pairs	No	~30% of frames	$\leq$0.2 dB from oracle
Entropy methods	Entropy bins	Global/local, redundancy	No	Data-driven	Lower manual deviation

Key frame sampling is an active area of research crucial for scalable, efficient, and task-adaptive video processing pipelines. Algorithmic innovation is driven by submodular optimization, volume-based selection, self-supervision, and language-aware relevance—each facilitating dramatically improved accuracy, efficiency, and downstream utility in modern video-based machine learning systems (Tang et al., 2022, Li et al., 5 Feb 2025, Tang et al., 28 Feb 2025, Zhang et al., 3 Oct 2025, Li et al., 16 Dec 2025, Zhi et al., 2021, Chasmai et al., 18 Jun 2025).