Benchmarking Large Vision-Language Models via Directed Scene Graph for Comprehensive Image Captioning

• URL: http://arxiv.org/abs/2412.08614v3
• Date: Wed, 26 Mar 2025 06:10:20 GMT
• Title: Benchmarking Large Vision-Language Models via Directed Scene Graph for Comprehensive Image Captioning
• Authors: Fan Lu, Wei Wu, Kecheng Zheng, Shuailei Ma, Biao Gong, Jiawei Liu, Wei Zhai, Yang Cao, Yujun Shen, Zheng-Jun Zha,
• Abstract summary: In this paper, we introduce a detailed caption benchmark, termed as CompreCap, to evaluate the visual context from a directed scene graph view.<n>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.<n>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.
• Score: 77.2852342808769
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generating detailed captions comprehending text-rich visual content in images has received growing attention for Large Vision-Language Models (LVLMs). However, few studies have developed benchmarks specifically tailored for detailed captions to measure their accuracy and comprehensiveness. In this paper, we introduce a detailed caption benchmark, termed as CompreCap, to evaluate the visual context from a directed scene graph view. Concretely, we first manually segment the image into semantically meaningful regions (i.e., semantic segmentation mask) 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. Based on our directed scene graph, we develop a pipeline to assess the generated detailed captions from LVLMs on multiple levels, including the object-level coverage, the accuracy of attribute descriptions, the score of key relationships, etc. Experimental results on the CompreCap dataset confirm that our evaluation method aligns closely with human evaluation scores across LVLMs.

Related papers

• ImageSet2Text: Describing Sets of Images through Text [17.336422962134918]
  We introduce ImageSet2Text, a novel approach that leverages vision-language foundation models to automatically create natural language descriptions of image sets. ImageSet2Text iteratively extracts key concepts from image subsets, encodes them into a structured graph, and refines insights using an external knowledge graph and CLIP-based validation. We evaluate ImageSet2Text's descriptions on accuracy, completeness, readability and overall quality, benchmarking it against existing vision-language models and introducing new datasets for large-scale group image captioning.
  arXiv  Detail & Related papers  (2025-03-25T05:29:50Z)
• 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)
• FLAIR: VLM with Fine-grained Language-informed Image Representations [49.2684130383925]
  FLAIR is an approach that utilizes long and detailed image descriptions to learn localized image embeddings.<n>Our experiments demonstrate the effectiveness of FLAIR trained on 30M image-text pairs in capturing fine-grained visual information.
  arXiv  Detail & Related papers  (2024-12-04T18:56:04Z)
• FACTUAL: A Benchmark for Faithful and Consistent Textual Scene Graph Parsing [66.70054075041487]
  Existing scene graphs that convert image captions into scene graphs often suffer from two types of errors. First, the generated scene graphs fail to capture the true semantics of the captions or the corresponding images, resulting in a lack of faithfulness. Second, the generated scene graphs have high inconsistency, with the same semantics represented by different annotations.
  arXiv  Detail & Related papers  (2023-05-27T15:38:31Z)
• MOC-GAN: Mixing Objects and Captions to Generate Realistic Images [21.240099965546637]
  We introduce a more rational setting, generating a realistic image from the objects and captions. Under this setting, objects explicitly define the critical roles in the targeted images and captions implicitly describe their rich attributes and connections. A MOC-GAN is proposed to mix the inputs of two modalities to generate realistic images.
  arXiv  Detail & Related papers  (2021-06-06T14:04:07Z)
• Matching Visual Features to Hierarchical Semantic Topics for Image Paragraph Captioning [50.08729005865331]
  This paper develops a plug-and-play hierarchical-topic-guided image paragraph generation framework. To capture the correlations between the image and text at multiple levels of abstraction, we design a variational inference network. To guide the paragraph generation, the learned hierarchical topics and visual features are integrated into the language model.
  arXiv  Detail & Related papers  (2021-05-10T06:55:39Z)
• Exploring Explicit and Implicit Visual Relationships for Image Captioning [11.82805641934772]
  In this paper, we explore explicit and implicit visual relationships to enrich region-level representations for image captioning. Explicitly, we build semantic graph over object pairs and exploit gated graph convolutional networks (Gated GCN) to selectively aggregate local neighbors' information. Implicitly, we draw global interactions among the detected objects through region-based bidirectional encoder representations from transformers.
  arXiv  Detail & Related papers  (2021-05-06T01:47:51Z)
• Improving Image Captioning with Better Use of Captions [65.39641077768488]
  We present a novel image captioning architecture to better explore semantics available in captions and leverage that to enhance both image representation and caption generation. Our models first construct caption-guided visual relationship graphs that introduce beneficial inductive bias using weakly supervised multi-instance learning. During generation, the model further incorporates visual relationships using multi-task learning for jointly predicting word and object/predicate tag sequences.
  arXiv  Detail & Related papers  (2020-06-21T14:10:47Z)

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.