What Is a Good Caption? A Comprehensive Visual Caption Benchmark for Evaluating Both Correctness and Thoroughness

• URL: http://arxiv.org/abs/2502.14914v2
• Date: Tue, 15 Apr 2025 12:58:38 GMT
• Title: What Is a Good Caption? A Comprehensive Visual Caption Benchmark for Evaluating Both Correctness and Thoroughness
• Authors: Zhihang Liu, Chen-Wei Xie, Bin Wen, Feiwu Yu, Jixuan Chen, Boqiang Zhang, Nianzu Yang, Pandeng Li, Yinglu Li, Zuan Gao, Yun Zheng, Hongtao Xie,
• Abstract summary: CAPability is a comprehensive benchmark for evaluating visual captioning across 12 dimensions spanning six critical views.<n>We curate nearly 11K human-annotated images and videos with visual element annotations to evaluate the generated captions.
• Score: 30.44039177018447
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Visual captioning benchmarks have become outdated with the emergence of modern multimodal large language models (MLLMs), as the brief ground-truth sentences and traditional metrics fail to assess detailed captions effectively. While recent benchmarks attempt to address this by focusing on keyword extraction or object-centric evaluation, they remain limited to vague-view or object-view analyses and incomplete visual element coverage. In this paper, we introduce CAPability, a comprehensive multi-view benchmark for evaluating visual captioning across 12 dimensions spanning six critical views. We curate nearly 11K human-annotated images and videos with visual element annotations to evaluate the generated captions. CAPability stably assesses both the correctness and thoroughness of captions using F1-score. By converting annotations to QA pairs, we further introduce a heuristic metric, \textit{know but cannot tell} ($K\bar{T}$), indicating a significant performance gap between QA and caption capabilities. Our work provides the first holistic analysis of MLLMs' captioning abilities, as we identify their strengths and weaknesses across various dimensions, guiding future research to enhance specific aspects of capabilities.

Related papers

• The Devil is in the Distributions: Explicit Modeling of Scene Content is Key in Zero-Shot Video Captioning [89.64905703368255]
  We propose a novel progressive multi-granularity textual prompting strategy for zero-shot video captioning. Our approach constructs three distinct memory banks, encompassing noun phrases, scene graphs of noun phrases, and entire sentences.
  arXiv  Detail & Related papers  (2025-03-31T03:00:19Z)
• Painting with Words: Elevating Detailed Image Captioning with Benchmark and Alignment Learning [56.31096024472269]
  We introduce DeCapBench along with a novel metric, DCScore, specifically designed for detailed captioning tasks. DCScore evaluates hallucinations and fine-grained comprehensiveness by deconstructing responses into the smallest self-sufficient units. DeCapBench exhibits a high correlation with VLM arena results on descriptive tasks, surpassing existing benchmarks for vision-language models.
  arXiv  Detail & Related papers  (2025-03-10T22:53:56Z)
• Toward Robust Hyper-Detailed Image Captioning: A Multiagent Approach and Dual Evaluation Metrics for Factuality and Coverage [50.84150600032693]
  Multimodal large language models (MLLMs) excel at generating highly detailed captions but often produce hallucinations.<n>We propose a multiagent approach that leverages LLM-MLLM collaboration to correct given captions.<n>Our proposed method significantly enhances the factual accuracy of captions, even improving those generated by GPT-4V.
  arXiv  Detail & Related papers  (2024-12-20T01:37:22Z)
• G-VEval: A Versatile Metric for Evaluating Image and Video Captions Using GPT-4o [15.929657348606018]
  We introduce G-VEval, a novel metric inspired by G-Eval and powered by the new GPT-4o.<n>G-VEval uses chain-of-thought reasoning in large multimodal models and supports three modes: reference-free, reference-only, and combined.<n>We also propose MSVD-Eval, a new dataset for video captioning evaluation, to establish a more transparent and consistent framework for both human experts and evaluation metrics.
  arXiv  Detail & Related papers  (2024-12-18T09:23:12Z)
• Benchmarking Large Vision-Language Models via Directed Scene Graph for Comprehensive Image Captioning [77.2852342808769]
  In this paper, we introduce a detailed caption benchmark, termed as CompreCap, to evaluate the visual context from a directed scene graph view. We first manually segment the image into semantically meaningful regions according to common-object vocabulary, while also distinguishing attributes of objects within all those regions. Then directional relation labels of these objects are annotated to compose a directed scene graph that can well encode rich compositional information of the image.
  arXiv  Detail & Related papers  (2024-12-11T18:37:42Z)
• MMIE: Massive Multimodal Interleaved Comprehension Benchmark for Large Vision-Language Models [71.36392373876505]
  We introduce MMIE, a large-scale benchmark for evaluating interleaved multimodal comprehension and generation in Large Vision-Language Models (LVLMs) MMIE comprises 20K meticulously curated multimodal queries, spanning 3 categories, 12 fields, and 102 subfields, including mathematics, coding, physics, literature, health, and arts. It supports both interleaved inputs and outputs, offering a mix of multiple-choice and open-ended question formats to evaluate diverse competencies.
  arXiv  Detail & Related papers  (2024-10-14T04:15:00Z)
• Benchmarking and Improving Detail Image Caption [12.078715675876674]
  Large vision-language model (LVLM) has long been regarded as a fundamental task in visual understanding. We propose to benchmark detail image caption task by curating high-quality evaluation datasets annotated by human experts. We also design a more reliable caption evaluation metric called CAPTURE.
  arXiv  Detail & Related papers  (2024-05-29T13:54:12Z)
• Vision Language Model-based Caption Evaluation Method Leveraging Visual Context Extraction [27.00018283430169]
  This paper presents VisCE$2$, a vision language model-based caption evaluation method. Our method focuses on visual context, which refers to the detailed content of images, including objects, attributes, and relationships.
  arXiv  Detail & Related papers  (2024-02-28T01:29:36Z)
• CODIS: Benchmarking Context-Dependent Visual Comprehension for Multimodal Large Language Models [58.95889895912716]
  We introduce a new benchmark, named as CODIS, designed to assess the ability of models to use context provided in free-form text to enhance visual comprehension. Our findings indicate that MLLMs consistently fall short of human performance on this benchmark. This underscores the pressing need to enhance the ability of MLLMs to comprehend visuals in a context-dependent manner.
  arXiv  Detail & Related papers  (2024-02-21T08:21:12Z)
• Transparent Human Evaluation for Image Captioning [70.03979566548823]
  We develop a rubric-based human evaluation protocol for image captioning models. We show that human-generated captions show substantially higher quality than machine-generated ones. We hope that this work will promote a more transparent evaluation protocol for image captioning.
  arXiv  Detail & Related papers  (2021-11-17T07:09:59Z)
• Intrinsic Image Captioning Evaluation [53.51379676690971]
  We propose a learning based metrics for image captioning, which we call Intrinsic Image Captioning Evaluation(I2CE) Experiment results show that our proposed method can keep robust performance and give more flexible scores to candidate captions when encountered with semantic similar expression or less aligned semantics.
  arXiv  Detail & Related papers  (2020-12-14T08:36:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.