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Image Captioning Evaluation in the Age of Multimodal LLMs: Challenges and Future Perspectives
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Image Captioning Evaluation in the Age of Multimodal LLMs: Challenges and Future Perspectives
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The evaluation of machine-generated image captions is a complex and evolving challenge. With the advent of Multimodal Large Language Models (MLLMs), image captioning has become a core task, increasing the need for robust and reliable evaluation metrics. This survey provides a comprehensive overview of advancements in image captioning evaluation, analyzing the evolution, strengths, and limitations of existing metrics. We assess these metrics across multiple dimensions, including correlation with human judgment, ranking accuracy, and sensitivity to hallucinations. Additionally, we explore the challenges posed by the longer and more detailed captions generated by MLLMs and examine the adaptability of current metrics to these stylistic variations. Our analysis highlights some limitations of standard evaluation approaches and suggests promising directions for future research in image captioning assessment.
Forward citations
Cited by 7 Pith papers
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PPE-Bench: A Benchmark for Evaluating MLLM Unlearning under Private-Public Entanglement
Existing MLLM unlearning methods reduce private-attribute leakage on entangled images but substantially harm co-occurring public figures and landmarks, with private knowledge often re-emerging after public finetuning.
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ITIScore: An Image-to-Text-to-Image Rating Framework for the Image Captioning Ability of MLLMs
ITIScore evaluates MLLM image captions via image-to-text-to-image reconstruction consistency and aligns with human judgments on a new 40K-caption benchmark.
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FPBench: A Comprehensive Benchmark of Multimodal Large Language Models for Fingerprint Analysis
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A Reconstruction-Based Framework for Caption Evaluation Beyond Reference Captions
Reference-free caption quality is scored by the downstream vision-language accuracy of a caption-conditioned reconstructed image, via a new CTTD benchmark.
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LenGuard-GPC: Length Guarding with Guided-Prompt Consistency for Spatial Reasoning Reinforce Learning
LenGuard-GPC adds a token-level KL consistency reward between standard and guided prompts, plus a staged length bonus, to GRPO training of Qwen3-VL-8B and reports better accuracy with shorter responses on multi-view s...
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Spotlight and Shadow: Attention-Guided Dual-Anchor Introspective Decoding for MLLM Hallucination Mitigation
DaID mitigates MLLM hallucinations by attention-guided selection of dual layers that calibrate token generation using internal perceptual discrepancies.
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TCAP: Tri-Component Attention Profiling for Unsupervised Backdoor Detection in MLLM Fine-Tuning
TCAP detects backdoor samples in MLLM fine-tuning via tri-component attention profiling, GMM-based head identification, and EM vote aggregation.
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