PARCEL: Pool-Anchored Resampling with Conditioned Elastic Queries for Efficient Vision-Language Understanding
arXiv:2605.30126v1 Announce Type: cross Abstract: Large Vision-Language Models (LVLMs) map visual inputs into dense token sequences, imposing a quadratic computational bottleneck for inference. Elastic visual-token compression addresses this by training a single model that can run at multiple visual-token budgets. However, existing approaches struggle under aggressive compression. Spatial-only compression, as in nested pooling, behaves as an imperfect low-pass filter and induces spectral aliasing that obscures fine-grained detail. Query-only compression, as in nested query resampling, replaces
The increasing scale of Large Vision-Language Models (LVLMs) is pushing computational limits, making efficient inference a critical challenge that necessitates new compression techniques.
Efficient visual-token compression methods like PARCEL are crucial for deploying advanced AI models more broadly and affordably, impacting the development and accessibility of future AI capabilities.
This research introduces a novel approach to visual token compression that aims to overcome limitations of existing methods, potentially enabling more aggressive compression without significant loss of fine-grained detail.
- · AI hardware manufacturers
- · Cloud AI service providers
- · Developers of computer vision applications
- · Sectors adopting visual AI
- · Inefficient AI model architectures
- · Companies relying on unoptimized LVLMs
Reduced computational and energy requirements for running large vision models become possible.
Democratization of sophisticated AI visual understanding tools due to lower deployment costs and increased accessibility.
Acceleration of edge AI development and deployment for vision tasks, enabling new applications in autonomous systems and IoT devices.
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Read at arXiv cs.LG