RailVQA-bench supplies 21,168 QA pairs for ATO visual cognition while RailVQA-CoM combines large-model reasoning with small-model efficiency via transparent modules and temporal sampling.
VisionGPT: Vision-language under- standing agent using generalized multimodal frame- work
3 Pith papers cite this work. Polarity classification is still indexing.
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GLANCE introduces a bi-loop multi-agent framework with global-local coordination mechanisms that outperforms baselines by up to 33% on music-grounded nonlinear video editing tasks using a new MVEBench benchmark.
DREAM introduces a two-stage adaptive multi-modal fusion framework that reaches BLEU-4 of 0.241 on DeepEyeNet for retinal image report generation and generalizes to ROCO.
citing papers explorer
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RailVQA: A Benchmark and Framework for Efficient Interpretable Visual Cognition in Automatic Train Operation
RailVQA-bench supplies 21,168 QA pairs for ATO visual cognition while RailVQA-CoM combines large-model reasoning with small-model efficiency via transparent modules and temporal sampling.
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GLANCE: A Global-Local Coordination Multi-Agent Framework for Music-Grounded Non-Linear Video Editing
GLANCE introduces a bi-loop multi-agent framework with global-local coordination mechanisms that outperforms baselines by up to 33% on music-grounded nonlinear video editing tasks using a new MVEBench benchmark.
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DREAM: Dynamic Retinal Enhancement with Adaptive Multi-modal Fusion for Expert Precision Medical Report Generation
DREAM introduces a two-stage adaptive multi-modal fusion framework that reaches BLEU-4 of 0.241 on DeepEyeNet for retinal image report generation and generalizes to ROCO.