Focus-then-Context: Subject-Centric Progressive Visual Token Reduction for Vision-Language Models
arXiv:2605.20950v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) face a bottleneck of prohibitive computational costs arising from massive visual token sequences during inference. Existing vision token reduction methods alleviate this burden, but they unintentionally preserve the isolated visual subject strictly aligned with the user's query, which fails to substantially explore salient subjects and their contextual relationships. In this paper, we propose SPpruner, a subject-centric progressive reduction paradigm that emulates the \textit{Focus-then-Context} mechanism of the hu
The increasing scale and computational demands of Vision-Language Models necessitate more efficient processing techniques to overcome existing bottlenecks, making token reduction a timely area of research.
Improving the efficiency of VLMs addresses a major computational bottleneck, enabling broader deployment and more complex applications of AI by reducing inference costs and latency.
This advancement changes how VLMs process visual information, moving towards a more focused and contextually aware token reduction, making these models more practical and scalable.
- · AI developers
- · Cloud computing providers (optimizing resource use)
- · Industries deploying VLMs (e.g., robotics, autonomous vehicles)
- · AI hardware manufacturers (increased demand for VLM-optimized chips)
- · Companies with inefficient VLM architectures
- · Data centers with high inference costs
Reduced computational costs and increased inference speed for Vision-Language Models.
Accelerated development and deployment of VLM-powered applications across various sectors due to improved efficiency.
Enhanced AI capabilities leading to the displacement of human tasks in visual analysis and reasoning, impacting white-collar employment.
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Read at arXiv cs.AI