A unified autoregressive vision-language framework integrates segmentation, detection, and appearance reasoning for CT images via task-routing tokens and progressive refinement, with gains on public benchmarks.
Mm- llms: Recent advances in multimodal large language models
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.CV 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Zero-shot MLLMs on ShanghaiTech and CHAD exhibit strong conservative bias with high precision but collapsed recall; class-specific prompts raise peak F1 from 0.09 to 0.64 yet recall remains the bottleneck.
citing papers explorer
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Segmentation, Detection and Explanation: A Unified Framework for CT Appearance Reasoning
A unified autoregressive vision-language framework integrates segmentation, detection, and appearance reasoning for CT images via task-routing tokens and progressive refinement, with gains on public benchmarks.
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Are Multimodal LLMs Ready for Surveillance? A Reality Check on Zero-Shot Anomaly Detection in the Wild
Zero-shot MLLMs on ShanghaiTech and CHAD exhibit strong conservative bias with high precision but collapsed recall; class-specific prompts raise peak F1 from 0.09 to 0.64 yet recall remains the bottleneck.