From Concrete to Abstract: A Multimodal Generative Approach to Abstract Concept Learning
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Abstract (arXiv)
Understanding and manipulating concrete and abstract concepts is fundamental to human intelligence. Yet, they remain challenging for artificial agents. This paper introduces a multimodal generative approach to high order abstract concept learning, which integrates visual and categorical linguistic information from concrete ones. Our model initially grounds subordinate level concrete concepts, combines them to form basic level concepts, and finally abstracts to superordinate level concepts via the grounding of basic-level concepts. We evaluate the model language learning ability through language-to-visual and visual-to-language tests with high order abstract concepts. Experimental results demonstrate the proficiency of the model in both language understanding and language naming tasks.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 18 of its 41 references that are in the map. First posted on arXiv 3 Oct 2024.
Builds on
18 of 41 references.
References · 18
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
- 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
- 2014ImageNet Large Scale Visual Recognition Challengecited by 8,441
- 2021CLIPScore: A Reference-free Evaluation Metric for Image Captioningcited by 2,388
- 2017AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networkscited by 664
- 2017Unsupervised Image-to-Image Translation Networkscited by 602
- 2016StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networkscited by 575
- 2018Multimodal Generative Models for Scalable Weakly-Supervised Learningcited by 200
- 2019Variational Mixture-of-Experts Autoencoders for Multi-Modal Deep Generative Modelscited by 177
- 2016Joint Multimodal Learning with Deep Generative Modelscited by 87
- 2021Multimodal Conditional Image Synthesis with Product-of-Experts GANscited by 75
6 more
- 2020Multimodal Generative Learning Utilizing Jensen-Shannon-Divergencecited by 48
- 2022A survey of multimodal deep generative modelscited by 47
- 2017Generative Models of Visually Grounded Imaginationcited by 44
- 2021M6-UFC: Unifying Multi-Modal Controls for Conditional Image Synthesis via Non-Autoregressive Generative Transformerscited by 36
- 2020Relating by Contrasting: A Data-efficient Framework for Multimodal Generative Modelscited by 27
- 2019Cross-modal Variational Auto-encoder with Distributed Latent Spaces and Associatorscited by 5
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Citing papers per year: 2026: 2.
2 papers.