AdaRD-Key: Adaptive Video Keyframe Sampling

• AdaRD-Key is an adaptive relevance-diversity keyframe sampling module that integrates query-conditioned relevance with a log-determinant diversity term for optimal frame selection.
• It employs a greedy maximization algorithm using Sherman-Morrison updates to efficiently optimize a unified Relevance-Diversity Max-Volume objective for precise semantic coverage.
• The training-free method enhances tasks like video question answering and captioning by providing a real-time, plug-and-play solution for various vision-language models.

AdaRD-Key refers to an adaptive relevance-diversity keyframe sampling module designed to improve query-driven long-form video understanding in multimodal vision-language systems (Zhang et al., 3 Oct 2025). The method prioritizes finding an optimal subset of video frames that both maximizes alignment to a user query and ensures high semantic diversity. Distinct from uniform frame sampling or fixed exclusion-window schemes, AdaRD-Key formulates keyframe selection as the maximization of a unified Relevance-Diversity Max-Volume (RD-MV) objective, integrating query-conditioned relevance signals and a log-determinant diversity term. The system is training-free, computationally efficient (scaling to real-time operation on standard GPUs), and is agnostic to downstream vision-LLM (VLM) architectures.

1. Motivation and Conceptual Foundations

The challenge addressed by AdaRD-Key is the accurate and compact representation of long-form video for tasks such as video question answering and captioning. Uniform frame sampling strategies typically fail to capture critical moments, while exclusion-window-based redundancy reduction can omit fine-grained cues temporally close to important events. Methods focused exclusively on diversity neglect query specificity, whereas query-relevance-driven approaches often yield redundant selections.

AdaRD-Key remedies these problems by simultaneously optimizing relevance and diversity. This is achieved via a Relevance-Diversity Max-Volume selector, which balances the tendency to select highly query-relevant frames with the imperative to maintain global semantic coverage across the video.

2. Mathematical Formulation and Optimization Strategy

The principal objective is defined as:

$$S(F) = \sum_{f \in F} R(f) + \lambda\,\mathcal{D}(F)$$

where:

• $F$ is the set of $k$ selected frames from $N$ candidates.
• $R(f)$ is the relevance score of frame $f$ with respect to the input query, using BLIP-2-based encoding.
• $\mathcal{D}(F) = \log \det(G_F + \varepsilon I)$ is the diversity metric, with $G_F$ the Gram matrix of the selected frames’ normalized feature vectors, $\varepsilon > 0$ for regularization, and $I$ the identity matrix.
• $\lambda$ controls the relative weight between relevance and diversity.

Frame selection proceeds recursively via greedy maximization of the objective function, leveraging the monotonic submodularity of the log-determinant for theoretical approximation guarantees. For each candidate frame $i$ not yet in $F$, the incremental gain is:

$$\Delta(i \mid F) = R(i) + \lambda \log( (1+\varepsilon) - r^\top (G_F + \varepsilon I)^{-1} r )$$

with $r$ comprising inner products between $f_i$ and members of $F$. Updates to $(G_F + \varepsilon I)^{-1}$ utilize the Sherman-Morrison formula for computational efficiency.

3. Query-Relevance Scoring and Diversity Adjustment

Feature extraction is performed via BLIP-2 lightweight frame and query encoders, producing L2-normalized embeddings. Query relevance scores $R(f)$ quantify the frame’s semantic alignment with the query, often resulting in highly non-uniform (peaky) distributions if the query is focused, or flat distributions for generic queries.

The VB-Scale (“Variability–Budget Scaling”) mechanism adaptively sets $\lambda$ according to the coefficient of variation ($\mathrm{CV} = \mathrm{std}(R)/\mathrm{mean}(R)$) and the frame candidate-to-selection budget ratio $\rho = N/k$. This approach ensures the diversity term receives appropriate emphasis when relevance signals are either diffuse or frame selection budgets are loose.

4. Relevance-Aware Gating Mechanism

AdaRD-Key includes a gating protocol to address queries with weak video alignment. The gate operates as follows:

• If $\max(R) \geq \tau$ (e.g., $\tau=0.4$), full relevance-diversity RD-MV selection is applied.
• If $\max(R) < \tau$, relevance weights are suppressed and keyframes are selected solely according to the diversity term.

This “fallback” eliminates adverse query drift and maintains coverage for broad or underspecified queries, as substantiated by flowchart illustrations and performance ablations (Zhang et al., 3 Oct 2025).

5. Empirical Performance and Benchmarks

AdaRD-Key has been rigorously evaluated on LongVideoBench and Video-MME, with integration performed on models including Qwen2-VL and LLaVA-Video. Experimental conditions involve sampling video at 1 fps and applying AdaRD-Key for selection of $k=32$ or $k=64$ frames.

• On LongVideoBench: Qwen2-VL (with AdaRD-Key, $k=32$) attains 60.8% accuracy, LLaVA-Video (with AdaRD-Key, $k=64$) reaches 62.9%. Both outperform the uniform sampling and top-$k$ relevance baselines, and match or exceed previously state-of-the-art approaches (AKS, MAXINFO).
• On Video-MME: AdaRD-Key lifts accuracy scores for both medium and long-form videos, with measured improvements in question answering and information preservation.
• Qualitative results indicate superior retention of critical moments, numeric details, and scene transitions essential for query response fidelity.

6. Training-Free Operation and Model Integration

The system is wholly training-free and compatible with existing VLMs, requiring no additional gradient updates, loss functions, or architectural modifications. The application of greedy selection with the Sherman-Morrison inverse update yields real-time operation on a single GPU (e.g., A100, 80GB). The software design allows plug-and-play keyframe selection for large-scale video repositories and real-world deployment.

7. Practical Applications, Implications, and Future Directions

AdaRD-Key supports a range of tasks including video question answering, video summarization, captioning, surveillance analysis, and multimedia retrieval. Its adaptive mechanism improves event localization, diversity enhancement, and semantic compression for long-duration videos. The paradigm established by AdaRD-Key suggests practical directions in:

• Integrating advanced relevance scoring modules or context-aware attention mechanisms,
• Expanding to multimodal temporal event detection and video segment summarization,
• Applying adaptive gating strategies to other domains with latent weak-query alignment.

A plausible implication is that the method’s efficiency and robustness set a reference point for future training-free, query-adaptive sampling modules in video and broader multimodal understanding tasks.

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AdaRD-Key embodies a mathematically principled, computationally scalable, and empirically validated approach for keyframe selection in long-form video understanding, unifying query relevance and semantic diversity to support enhanced downstream multimodal performance (Zhang et al., 3 Oct 2025).