OracleSage: Towards Unified Visual-Linguistic Understanding of Oracle Bone Scripts through Cross-Modal Knowledge Fusion
- URL: http://arxiv.org/abs/2411.17837v1
- Date: Tue, 26 Nov 2024 19:26:06 GMT
- Title: OracleSage: Towards Unified Visual-Linguistic Understanding of Oracle Bone Scripts through Cross-Modal Knowledge Fusion
- Authors: Hanqi Jiang, Yi Pan, Junhao Chen, Zhengliang Liu, Yifan Zhou, Peng Shu, Yiwei Li, Huaqin Zhao, Stephen Mihm, Lewis C Howe, Tianming Liu,
- Abstract summary: Oracle bone script (OBS), as China's earliest mature writing system, present significant challenges in automatic recognition.
We introduce OracleSage, a novel cross-modal framework that integrates hierarchical visual understanding with graph-based semantic reasoning.
- Score: 19.788896054132053
- License:
- Abstract: Oracle bone script (OBS), as China's earliest mature writing system, present significant challenges in automatic recognition due to their complex pictographic structures and divergence from modern Chinese characters. We introduce OracleSage, a novel cross-modal framework that integrates hierarchical visual understanding with graph-based semantic reasoning. Specifically, we propose (1) a Hierarchical Visual-Semantic Understanding module that enables multi-granularity feature extraction through progressive fine-tuning of LLaVA's visual backbone, (2) a Graph-based Semantic Reasoning Framework that captures relationships between visual components and semantic concepts through dynamic message passing, and (3) OracleSem, a semantically enriched OBS dataset with comprehensive pictographic and semantic annotations. Experimental results demonstrate that OracleSage significantly outperforms state-of-the-art vision-language models. This research establishes a new paradigm for ancient text interpretation while providing valuable technical support for archaeological studies.
Related papers
- Relation-aware Hierarchical Prompt for Open-vocabulary Scene Graph Generation [14.82606425343802]
Open-vocabulary Scene Graph Generation (OV-SGG) overcomes the limitations of the closed-set assumption by aligning visual relationship representations with open-vocabulary textual representations.
Existing OV-SGG methods are constrained by fixed text representations, limiting diversity and accuracy in image-text alignment.
We propose the Relation-Aware Hierarchical Prompting (RAHP) framework, which enhances text representation by integrating subject-object and region-specific relation information.
arXiv Detail & Related papers (2024-12-26T02:12:37Z) - ARPA: A Novel Hybrid Model for Advancing Visual Word Disambiguation Using Large Language Models and Transformers [1.6541870997607049]
We present ARPA, an architecture that fuses the unparalleled contextual understanding of large language models with the advanced feature extraction capabilities of transformers.
ARPA's introduction marks a significant milestone in visual word disambiguation, offering a compelling solution.
We invite researchers and practitioners to explore the capabilities of our model, envisioning a future where such hybrid models drive unprecedented advancements in artificial intelligence.
arXiv Detail & Related papers (2024-08-12T10:15:13Z) - Emergent Visual-Semantic Hierarchies in Image-Text Representations [13.300199242824934]
We study the knowledge of existing foundation models, finding that they exhibit emergent understanding of visual-semantic hierarchies.
We propose the Radial Embedding (RE) framework for probing and optimizing hierarchical understanding.
arXiv Detail & Related papers (2024-07-11T14:09:42Z) - Bridging Local Details and Global Context in Text-Attributed Graphs [62.522550655068336]
GraphBridge is a framework that bridges local and global perspectives by leveraging contextual textual information.
Our method achieves state-of-theart performance, while our graph-aware token reduction module significantly enhances efficiency and solves scalability issues.
arXiv Detail & Related papers (2024-06-18T13:35:25Z) - Hierarchical Text-to-Vision Self Supervised Alignment for Improved Histopathology Representation Learning [64.1316997189396]
We present a novel language-tied self-supervised learning framework, Hierarchical Language-tied Self-Supervision (HLSS) for histopathology images.
Our resulting model achieves state-of-the-art performance on two medical imaging benchmarks, OpenSRH and TCGA datasets.
arXiv Detail & Related papers (2024-03-21T17:58:56Z) - Towards More Unified In-context Visual Understanding [74.55332581979292]
We present a new ICL framework for visual understanding with multi-modal output enabled.
First, we quantize and embed both text and visual prompt into a unified representational space.
Then a decoder-only sparse transformer architecture is employed to perform generative modeling on them.
arXiv Detail & Related papers (2023-12-05T06:02:21Z) - Jointly Visual- and Semantic-Aware Graph Memory Networks for Temporal
Sentence Localization in Videos [67.12603318660689]
We propose a novel Hierarchical Visual- and Semantic-Aware Reasoning Network (HVSARN)
HVSARN enables both visual- and semantic-aware query reasoning from object-level to frame-level.
Experiments on three datasets demonstrate that our HVSARN achieves a new state-of-the-art performance.
arXiv Detail & Related papers (2023-03-02T08:00:22Z) - Knowledge Graph Augmented Network Towards Multiview Representation
Learning for Aspect-based Sentiment Analysis [96.53859361560505]
We propose a knowledge graph augmented network (KGAN) to incorporate external knowledge with explicitly syntactic and contextual information.
KGAN captures the sentiment feature representations from multiple perspectives, i.e., context-, syntax- and knowledge-based.
Experiments on three popular ABSA benchmarks demonstrate the effectiveness and robustness of our KGAN.
arXiv Detail & Related papers (2022-01-13T08:25:53Z) - A Dependency Syntactic Knowledge Augmented Interactive Architecture for
End-to-End Aspect-based Sentiment Analysis [73.74885246830611]
We propose a novel dependency syntactic knowledge augmented interactive architecture with multi-task learning for end-to-end ABSA.
This model is capable of fully exploiting the syntactic knowledge (dependency relations and types) by leveraging a well-designed Dependency Relation Embedded Graph Convolutional Network (DreGcn)
Extensive experimental results on three benchmark datasets demonstrate the effectiveness of our approach.
arXiv Detail & Related papers (2020-04-04T14:59:32Z)
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.