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Enhancing Visual Token Representations for Video Large Language Models via Training-free Spatial-Temporal Pooling and Gridding

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
Visual Token RepresentationVideo UnderstandingMultimodal Large Language Models
Abstract:

Recent advances in Multimodal Large Language Models (MLLMs) have significantly advanced video understanding tasks, yet challenges remain in efficiently compressing visual tokens while preserving spatiotemporal interactions. Existing methods, such as LLaVA family, utilize simplistic pooling or interpolation techniques that overlook the intricate dynamics of visual tokens. To bridge this gap, we propose ST-GridPool, a novel training-free visual token enhancement method designed specifically for Video LLMs. Our approach integrates Pyramid Temporal Gridding (PTG), which captures multi-grained spatiotemporal interactions through hierarchical temporal gridding, and Norm-based Spatial Pooling (NSP), which preserves high-information visual regions by leveraging the correlation between token norms and semantic richness. Extensive experiments on various benchmarks demonstrate that ST-GridPool consistently enhances performance of Video LLMs without requiring costly retraining. Our method offers an efficient and plug-and-play solution for improving visual token representations. Our code is available in https://anonymous.4open.science/r/ST-GridPool-85BE.

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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 ST-GridPool, a training-free visual token enhancement method for Video LLMs, combining Pyramid Temporal Gridding (PTG) and Norm-based Spatial Pooling (NSP). It resides in the 'Unified Spatiotemporal Compression' leaf, which contains only three papers including this one. This leaf sits within the broader 'Spatiotemporal Compression Architectures' branch, indicating a moderately populated research direction focused on jointly modeling spatial and temporal redundancy. The small leaf size suggests this specific approach to unified compression is relatively sparse compared to other branches like pruning-based or merging-based reduction.

The taxonomy reveals neighboring research directions that provide context for this work. The sibling leaf 'Hierarchical Temporal Compression' contains three papers focusing on multi-granular temporal structures, while 'Spatial Compression Modules' addresses frame-level redundancy. The broader 'Token Reduction Mechanisms' branch encompasses pruning and merging approaches that operate differently from architectural compression schemes. ST-GridPool's position in unified spatiotemporal compression distinguishes it from methods that separate spatial and temporal processing, and from token reduction mechanisms that rely on attention-guided pruning or similarity-driven merging rather than architectural pooling strategies.

Among twenty candidates examined, the analysis found limited prior work overlap. The first contribution (ST-GridPool as a training-free method) shows one refutable candidate among ten examined, suggesting some existing work in training-free compression. The second contribution (PTG for multi-grained spatiotemporal features) examined ten candidates with none clearly refuting it, indicating relative novelty in this hierarchical temporal gridding approach. The third contribution (NSP using norm-semantic correlation) was not examined against candidates, leaving its novelty assessment incomplete. These statistics reflect a focused search scope rather than exhaustive coverage of the field.

Based on the limited search of twenty candidates, the work appears to occupy a moderately novel position within unified spatiotemporal compression. The hierarchical temporal gridding and norm-based pooling mechanisms show limited direct overlap with examined prior work, though the training-free aspect has some precedent. The analysis does not cover the full landscape of video token compression methods, particularly those in adjacent branches like adaptive compression or representation learning, which may contain relevant related work not captured in this top-twenty semantic search.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
20
Contribution Candidate Papers Compared
1
Refutable Paper

Research Landscape Overview

Core task: visual token compression for video large language models. The field addresses the computational challenge of processing long video sequences in multimodal LLMs by reducing the number of visual tokens while preserving semantic information. The taxonomy reveals several complementary research directions: Token Reduction Mechanisms explore pruning, merging, and selection strategies (e.g., Prunevid[1], Sparsevlm[5]); Compression Placement Strategies determine where in the architecture to apply compression; Spatiotemporal Compression Architectures design unified or hierarchical schemes that jointly handle spatial and temporal redundancy (e.g., Longvlm[2], PVC[13]); Token Representation Learning focuses on learning compact embeddings; Specialized Compression Contexts adapt methods for specific video types or tasks; Training Paradigms and Optimization address how to train compressed models efficiently; Application-Specific Adaptations tailor compression to downstream needs; and Survey and Taxonomic Studies provide overarching perspectives on the landscape. A particularly active line of work centers on unified spatiotemporal compression, where methods simultaneously reduce tokens across frames and within frames to handle long videos efficiently. Spatial-Temporal Pooling[0] falls squarely within this branch, employing pooling operations to compress both dimensions in a coordinated manner. This approach contrasts with works like Voco-llama[3], which emphasizes vocabulary-based compression and token representation learning, or DyCoke[4], which dynamically adjusts compression based on content. Nearby methods such as Longvlm[2] and PVC[13] similarly pursue unified compression but differ in their architectural choices—some favor attention-based merging while others use hierarchical pooling. The central trade-off across these branches involves balancing compression ratio against information retention, with ongoing questions about how to adaptively allocate tokens based on video complexity and task requirements.

Claimed Contributions

ST-GridPool: Training-free visual token enhancement method for Video LLMs

The authors introduce ST-GridPool, a training-free approach that enhances visual token representations in Video Large Language Models by optimizing the visual token compression process. This method improves video understanding performance while maintaining computational efficiency without requiring costly retraining or architectural modifications.

10 retrieved papers
Can Refute
Pyramid Temporal Gridding (PTG) for multi-grained spatiotemporal feature extraction

The authors propose PTG, a hierarchical gridding strategy applied to the temporal dimension that captures spatiotemporal interactions at multiple granularities. This component grids and updates frame tokens from segments of varying lengths, enabling extraction of both short-term dynamics and long-term context without introducing trainable parameters.

10 retrieved papers
Norm-based Spatial Pooling (NSP) leveraging token norm-semantic correlation

The authors design NSP, a spatial pooling mechanism that exploits the positive correlation between visual token norms and semantic importance. This approach uses norm-based dynamic pooling to preserve high-information regions while adaptively compressing low-energy backgrounds, maximizing retention of semantically meaningful visual details.

0 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

ST-GridPool: Training-free visual token enhancement method for Video LLMs

The authors introduce ST-GridPool, a training-free approach that enhances visual token representations in Video Large Language Models by optimizing the visual token compression process. This method improves video understanding performance while maintaining computational efficiency without requiring costly retraining or architectural modifications.

Contribution

Pyramid Temporal Gridding (PTG) for multi-grained spatiotemporal feature extraction

The authors propose PTG, a hierarchical gridding strategy applied to the temporal dimension that captures spatiotemporal interactions at multiple granularities. This component grids and updates frame tokens from segments of varying lengths, enabling extraction of both short-term dynamics and long-term context without introducing trainable parameters.

Contribution

Norm-based Spatial Pooling (NSP) leveraging token norm-semantic correlation

The authors design NSP, a spatial pooling mechanism that exploits the positive correlation between visual token norms and semantic importance. This approach uses norm-based dynamic pooling to preserve high-information regions while adaptively compressing low-energy backgrounds, maximizing retention of semantically meaningful visual details.

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