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A Training-Free Framework for Long Video Understanding via Video-Query-Options Similarity

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
long video understandingmultimodal large language model
Abstract:

Multimodal Large Language Models (MLLMs) have achieved remarkable success in image and short video understanding tasks, but their performance on hour-long videos remains limited due to constraint of input token capacity. Existing approaches often require costly training procedures, hindering their adaptability to rapidly evolving MLLM architectures. In this paper, we propose a training-free framework for long video understanding, integrating three key innovations: Adaptive Frame Sampling (AFS), Dynamic Resolution Allocation (DRA), and Video-Query-Options Similarity (VQOS). AFS adaptively increases frame sampling density in highly relevant video segments to preserve critical temporal details, while DRA reduces spatial resolution in less relevant segments to suppress redundant information. VQOS enhances similarity calculation by prompting MLLMs to generate candidate answer options, fusing queries with options to refine relevance estimation. Mirroring human cognitive processes (hypothesis generation → focused verification → irrelevance filtering), our framework effectively improve model accuracy without fine-tuning. The method is implemented on LLaVA-Video and Qwen2.5-VL respectively, and experimental results show our method could achieve state-of-the-art performances over 5 mainstream benchmarks. More visualization results and code are available in the Appendix.

Disclaimer
This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

The paper proposes a training-free framework combining Adaptive Frame Sampling, Dynamic Resolution Allocation, and Video-Query-Options Similarity for long video understanding. It resides in the 'Adaptive Sampling and Resolution Allocation' leaf under 'Training-Free Inference Optimization', which contains only two papers total. This is a relatively sparse research direction within the broader taxonomy of fifty papers, suggesting the specific combination of adaptive sampling and dynamic resolution strategies remains underexplored compared to architectural or training-based approaches.

The taxonomy reveals neighboring leaves focused on agent-based iterative reasoning and likelihood-based selection, both addressing inference efficiency through different mechanisms. The parent branch 'Training-Free Inference Optimization' contrasts sharply with 'Architectural Mechanisms for Long Video Processing', which emphasizes learned compression and memory modules requiring training. The paper's training-free stance positions it away from instruction tuning and data curation branches, instead aligning with inference-time resource allocation strategies that avoid model retraining costs.

Among eight candidates examined, the Adaptive Frame Sampling and Dynamic Resolution Allocation contribution shows one refutable candidate from two examined, indicating some prior overlap in this specific mechanism. The Video-Query-Options Similarity mechanism examined three candidates with none refutable, suggesting greater novelty in this query-option fusion approach. The overall training-free framework contribution also examined three candidates without clear refutation. The limited search scope of eight total candidates means these statistics reflect top semantic matches rather than exhaustive field coverage.

Based on the top-eight semantic search results, the framework appears to introduce novel elements particularly in the query-option similarity mechanism, while adaptive sampling and resolution allocation show partial overlap with existing work. The sparse taxonomy leaf and limited sibling papers suggest this specific combination of techniques occupies a relatively unexplored niche, though the small candidate pool examined prevents definitive claims about comprehensive novelty across the broader field.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
8
Contribution Candidate Papers Compared
1
Refutable Paper

Research Landscape Overview

Core task: Long video understanding with multimodal large language models. The field has evolved into several distinct branches addressing complementary challenges. Architectural Mechanisms for Long Video Processing explore novel designs for handling extended temporal sequences, while Training-Free Inference Optimization focuses on efficient sampling and resource allocation strategies that avoid costly retraining. Evaluation Benchmarks and Analysis provide standardized testbeds for measuring progress, and Training Strategies and Data Curation address how to effectively learn from large-scale video corpora. Domain-Specific and Task-Specific Applications tailor models to particular use cases, Multimodal Integration and Cross-Modal Reasoning emphasize combining vision, audio, and language signals, and Specialized Architectures and Novel Frameworks introduce fundamentally new model designs. Together, these branches reflect a maturing ecosystem balancing computational efficiency, representational capacity, and task diversity. Within Training-Free Inference Optimization, adaptive sampling and resolution allocation methods have emerged as a particularly active area, seeking to dynamically adjust which frames or regions receive computational focus without modifying model weights. Video Query Options[0] exemplifies this direction by proposing query-driven mechanisms that allocate processing budgets based on input characteristics, closely aligning with works like 16 Frames Per Second[33] that explore fixed or adaptive frame sampling rates. Nearby efforts such as LongVLM[1] and mplug owl3[3] also investigate inference-time strategies but may emphasize different trade-offs between temporal coverage and per-frame detail. A central tension across these methods is whether to prioritize uniform temporal sampling for global coherence or concentrate resources on salient moments, a question that remains open as benchmarks like Video MME[13] and LongVideoBench[21] reveal varied performance across different video lengths and question types.

Claimed Contributions

Adaptive Frame Sampling and Dynamic Resolution Allocation

The authors introduce two mechanisms: Adaptive Frame Sampling (AFS) increases frame density in video segments with higher relevance to the query, while Dynamic Resolution Allocation (DRA) adjusts spatial resolution based on segment importance, allocating higher resolution to more relevant segments and lower resolution to less relevant ones.

2 retrieved papers
Can Refute
Video-Query-Options Similarity mechanism

The authors propose a novel similarity computation strategy where the MLLM generates plausible answer options for a given query, then computes similarity between video segments and each query-option pair, fusing these scores to produce more robust relevance estimates for frame selection.

3 retrieved papers
Training-free framework for long video understanding

The authors develop a complete training-free framework that combines AFS, DRA, and VQOS to enable effective long video understanding without requiring model fine-tuning. The framework mimics human cognitive processes of hypothesis generation, focused verification, and irrelevance filtering.

3 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Adaptive Frame Sampling and Dynamic Resolution Allocation

The authors introduce two mechanisms: Adaptive Frame Sampling (AFS) increases frame density in video segments with higher relevance to the query, while Dynamic Resolution Allocation (DRA) adjusts spatial resolution based on segment importance, allocating higher resolution to more relevant segments and lower resolution to less relevant ones.

Contribution

Video-Query-Options Similarity mechanism

The authors propose a novel similarity computation strategy where the MLLM generates plausible answer options for a given query, then computes similarity between video segments and each query-option pair, fusing these scores to produce more robust relevance estimates for frame selection.

Contribution

Training-free framework for long video understanding

The authors develop a complete training-free framework that combines AFS, DRA, and VQOS to enable effective long video understanding without requiring model fine-tuning. The framework mimics human cognitive processes of hypothesis generation, focused verification, and irrelevance filtering.

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