ARMADA: Attribute-Based Multimodal Data Augmentation

• URL: http://arxiv.org/abs/2408.10086v1
• Date: Mon, 19 Aug 2024 15:27:25 GMT
• Title: ARMADA: Attribute-Based Multimodal Data Augmentation
• Authors: Xiaomeng Jin, Jeonghwan Kim, Yu Zhou, Kuan-Hao Huang, Te-Lin Wu, Nanyun Peng, Heng Ji,
• Abstract summary: Attribute-based Multimodal Data Augmentation (ARMADA) is a novel multimodal data augmentation method via knowledge-guided manipulation of visual attributes. ARMADA is a novel multimodal data generation framework that: (i) extracts knowledge-grounded attributes from symbolic KBs for semantically consistent yet distinctive image-text pair generation. This also highlights the need to leverage external knowledge proxies for enhanced interpretability and real-world grounding.
• Score: 93.05614922383822
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In Multimodal Language Models (MLMs), the cost of manually annotating high-quality image-text pair data for fine-tuning and alignment is extremely high. While existing multimodal data augmentation frameworks propose ways to augment image-text pairs, they either suffer from semantic inconsistency between texts and images, or generate unrealistic images, causing knowledge gap with real world examples. To address these issues, we propose Attribute-based Multimodal Data Augmentation (ARMADA), a novel multimodal data augmentation method via knowledge-guided manipulation of visual attributes of the mentioned entities. Specifically, we extract entities and their visual attributes from the original text data, then search for alternative values for the visual attributes under the guidance of knowledge bases (KBs) and large language models (LLMs). We then utilize an image-editing model to edit the images with the extracted attributes. ARMADA is a novel multimodal data generation framework that: (i) extracts knowledge-grounded attributes from symbolic KBs for semantically consistent yet distinctive image-text pair generation, (ii) generates visually similar images of disparate categories using neighboring entities in the KB hierarchy, and (iii) uses the commonsense knowledge of LLMs to modulate auxiliary visual attributes such as backgrounds for more robust representation of original entities. Our empirical results over four downstream tasks demonstrate the efficacy of our framework to produce high-quality data and enhance the model performance. This also highlights the need to leverage external knowledge proxies for enhanced interpretability and real-world grounding.

Related papers

• Web-Scale Visual Entity Recognition: An LLM-Driven Data Approach [56.55633052479446]
  Web-scale visual entity recognition presents significant challenges due to the lack of clean, large-scale training data. We propose a novel methodology to curate such a dataset, leveraging a multimodal large language model (LLM) for label verification, metadata generation, and rationale explanation. Experiments demonstrate that models trained on this automatically curated data achieve state-of-the-art performance on web-scale visual entity recognition tasks.
  arXiv  Detail & Related papers  (2024-10-31T06:55:24Z)
• Generating Faithful and Salient Text from Multimodal Data [24.866158772311522]
  We develop a framework to generate faithful and salient text from mixed-modal data. We train a small vision critic model to identify hallucinated and non-salient features from the image modality. Experiments on two datasets show that our framework improves LMMs' generation quality on both faithfulness and saliency.
  arXiv  Detail & Related papers  (2024-09-06T00:59:10Z)
• Img-Diff: Contrastive Data Synthesis for Multimodal Large Language Models [32.57246173437492]
  This study introduces a novel dataset named Img-Diff, designed to enhance fine-grained image recognition in MLLMs. By analyzing object differences between similar images, we challenge models to identify both matching and distinct components. We utilize the Stable-Diffusion-XL model and advanced image editing techniques to create pairs of similar images that highlight object replacements.
  arXiv  Detail & Related papers  (2024-08-08T17:10:16Z)
• TRINS: Towards Multimodal Language Models that Can Read [61.17806538631744]
  TRINS is a Text-Rich image INStruction dataset. It contains 39,153 text-rich images, captions, and 102,437 questions. We introduce a Language-vision Reading Assistant (LaRA) which is good at understanding textual content within images.
  arXiv  Detail & Related papers  (2024-06-10T18:52:37Z)
• Towards Retrieval-Augmented Architectures for Image Captioning [81.11529834508424]
  This work presents a novel approach towards developing image captioning models that utilize an external kNN memory to improve the generation process. Specifically, we propose two model variants that incorporate a knowledge retriever component that is based on visual similarities. We experimentally validate our approach on COCO and nocaps datasets and demonstrate that incorporating an explicit external memory can significantly enhance the quality of captions.
  arXiv  Detail & Related papers  (2024-05-21T18:02:07Z)
• A Dual-way Enhanced Framework from Text Matching Point of View for Multimodal Entity Linking [17.847936914174543]
  Multimodal Entity Linking (MEL) aims at linking ambiguous mentions with multimodal information to entity in Knowledge Graph (KG) such as Wikipedia. We formulate multimodal entity linking as a neural text matching problem where each multimodal information (text and image) is treated as a query. This paper introduces a dual-way enhanced (DWE) framework for MEL.
  arXiv  Detail & Related papers  (2023-12-19T03:15:50Z)
• JourneyDB: A Benchmark for Generative Image Understanding [89.02046606392382]
  We introduce a comprehensive dataset, referred to as JourneyDB, that caters to the domain of generative images. Our meticulously curated dataset comprises 4 million distinct and high-quality generated images. On our dataset, we have devised four benchmarks to assess the performance of generated image comprehension.
  arXiv  Detail & Related papers  (2023-07-03T02:39:08Z)
• 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.