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Visual Question Answering on BinaryVQA

Visual Question Answering on BinaryVQA

76.01AccuracyFTibVLM

Updated 5mo ago

Evaluation results

Method	Links
FTibVLM2026.05		76.01	73.25
Base2026.05		54.46	53.08

State-of-the-art paper

FTibVLM

FTibSuite: A Comprehensive Resource Suite for Tibetan Vision-Language Modeling

Dataset

BinaryVQA

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Related benchmarks

Multimodal Understanding and Reasoning on MMBench Tibetan Multimodal Capability Profiling on MME Tibetan Visual Entailment on SNLI-VE (Accuracy, Macro P/R/F1)Hallucination Robustness on POPE random Visual Question Answering on COREVQA

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