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New method optimizes LVLMs for 3D-VQA with token pruning and coding

By PulseAugur Editorial · [1 sources] ·

Researchers have developed TP-JSCC, a novel method for optimizing large vision-language models (LVLMs) in cloud-edge collaboration for 3D visual question answering (3D-VQA). The approach includes a Local and Global Semantics-based Hybrid Token Pruning (LGS-HTP) module that significantly reduces visual token count by decoupling foreground-background tokens and selecting critical semantics. Additionally, a Token Joint Source-Channel Coding (T-JSCC) module compresses these pruned tokens for robust transmission over error-prone channels, adapting to fluctuating conditions with minimal overhead. Experiments on ScanQA and SQA3D datasets demonstrate substantial data volume reduction and computational complexity savings. AI

IMPACT This method could enable more efficient deployment of advanced AI models on resource-constrained devices for complex visual tasks.

RANK_REASON The item is a research paper detailing a novel method for optimizing LVLMs. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New method optimizes LVLMs for 3D-VQA with token pruning and coding

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The item is a research paper detailing a novel method for optimizing LVLMs. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Zhengxin Li, Chongzhen Tian, Hui Yuan, Hao Liu, Sam Kwong ·

    TP-JSCC: Efficient Token Pruning and Joint Source-Channel Coding for LVLMs-based 3D-VQA in Cloud-Edge Collaboration

    arXiv:2610.35489v1 Announce Type: new Abstract: Deploying large vision-language models (LVLMs) on resource-constrained embodied agents for 3D visual question answering necessitates cloud-edge collaboration. However, offloading massive visual data to the cloud over bandwidth-limit…

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