CaptionQA is a new benchmark with 33,027 questions across natural, document, e-commerce, and embodied AI domains that measures how much utility model-generated captions retain compared to original images when used by LLMs for downstream tasks.
What is a good caption? a comprehensive visual caption benchmark for evaluating both correctness and thoroughness
5 Pith papers cite this work. Polarity classification is still indexing.
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ChartFI-Bench supplies 896 chart-description pairs and four metrics (Faithfulness, Coverage, Informativeness, Acuity) to evaluate MLLM-generated chart descriptions on faithfulness and insightfulness.
CineNeuron improves fMRI-to-video reconstruction by combining bottom-up semantic enrichment with top-down Mixture-of-Memories integration and outperforms prior methods on benchmarks.
CHAI framework pairs AI pre-captions with expert human critiques to produce precise video descriptions, enabling open models to outperform closed ones like Gemini-3.1-Pro and improve fine-grained control in video generation models.
HONES ranks feed-forward neurons by their causal contributions from task-relevant attention heads and uses lightweight scaling to steer performance on multiple vision-language tasks.
citing papers explorer
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CaptionQA: Is Your Caption as Useful as the Image Itself?
CaptionQA is a new benchmark with 33,027 questions across natural, document, e-commerce, and embodied AI domains that measures how much utility model-generated captions retain compared to original images when used by LLMs for downstream tasks.
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ChartFI: Benchmarking Faithfulness and Insightfulness of Chart Descriptions from Multimodal Large Language Models
ChartFI-Bench supplies 896 chart-description pairs and four metrics (Faithfulness, Coverage, Informativeness, Acuity) to evaluate MLLM-generated chart descriptions on faithfulness and insightfulness.
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Bridging Brain and Semantics: A Hierarchical Framework for Semantically Enhanced fMRI-to-Video Reconstruction
CineNeuron improves fMRI-to-video reconstruction by combining bottom-up semantic enrichment with top-down Mixture-of-Memories integration and outperforms prior methods on benchmarks.
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Building a Precise Video Language with Human-AI Oversight
CHAI framework pairs AI pre-captions with expert human critiques to produce precise video descriptions, enabling open models to outperform closed ones like Gemini-3.1-Pro and improve fine-grained control in video generation models.
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From Heads to Neurons: Causal Attribution and Steering in Multi-Task Vision-Language Models
HONES ranks feed-forward neurons by their causal contributions from task-relevant attention heads and uses lightweight scaling to steer performance on multiple vision-language tasks.