Papers with contextual accuracy

3 papers
MAVL: A Multilingual Audio-Video Lyrics Dataset for Animated Song Translation (2025.emnlp-main)

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Challenge: Experimental results show that multimodal, multimodal approaches to lyrics translation are more effective than text-only approaches.
Approach: They propose a multilingual, multimodal benchmark for singable lyrics translation . they propose syllable-constrained audio-video LLM with Chain-of-Thought .
Outcome: The proposed system outperforms text-based models in singability and contextual accuracy.
MAGIC-VQA: Multimodal And Grounded Inference with Commonsense Knowledge for Visual Question Answering (2025.findings-acl)

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Challenge: Existing Large Vision-Language Models (LVLMs) lack integrated commonsense knowledge . lack of integrated common knowledge limits their robustness and accuracy in VQA .
Approach: They propose a framework to enhance multimodal inference by integrating commonsense reasoning.
Outcome: MAGIC-VQA improves comprehensive benchmark datasets, surpassing existing models in tasks requiring advanced commonsense reasoning.
Towards Fine-grained Audio Captioning with Multimodal Contextual Fusion (2026.acl-long)

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Challenge: Existing methods for audio captioning lack fine-grained detail and contextual accuracy due to limited unimodal or superficial information.
Approach: They propose a two-stage automated pipeline that uses pretrained models to extract contextual cues from video . a large language model synthesizes these inputs to generate detailed and context-aware captions .
Outcome: The proposed method is scalable and generates detailed and context-aware captions on large-scale audio datasets.

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