Expand BERT Representation with Visual Information via Grounded Language Learning with Multimodal Partial Alignment

• URL: http://arxiv.org/abs/2312.01592v2
• Date: Tue, 9 Jan 2024 10:44:52 GMT
• Title: Expand BERT Representation with Visual Information via Grounded Language Learning with Multimodal Partial Alignment
• Authors: Cong-Duy Nguyen, The-Anh Vu-Le, Thong Nguyen, Tho Quan, Luu Anh Tuan
• Abstract summary: GroundedBERT is a grounded language learning method that enhances the BERT representation with visually grounded information. Our proposed method significantly outperforms the baseline language models on various language tasks of the GLUE and SQuAD datasets.
• Score: 11.148099070407431
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Language models have been supervised with both language-only objective and visual grounding in existing studies of visual-grounded language learning. However, due to differences in the distribution and scale of visual-grounded datasets and language corpora, the language model tends to mix up the context of the tokens that occurred in the grounded data with those that do not. As a result, during representation learning, there is a mismatch between the visual information and the contextual meaning of the sentence. To overcome this limitation, we propose GroundedBERT - a grounded language learning method that enhances the BERT representation with visually grounded information. GroundedBERT comprises two components: (i) the original BERT which captures the contextual representation of words learned from the language corpora, and (ii) a visual grounding module which captures visual information learned from visual-grounded datasets. Moreover, we employ Optimal Transport (OT), specifically its partial variant, to solve the fractional alignment problem between the two modalities. Our proposed method significantly outperforms the baseline language models on various language tasks of the GLUE and SQuAD datasets.

Related papers

• Leverage Points in Modality Shifts: Comparing Language-only and Multimodal Word Representations [0.8594140167290097]
  Multimodal embeddings aim to enrich the semantic information in neural representations of language compared to text-only models. Our paper compares word embeddings from three vision-and-language models and three text-only models, with static and contextual representations. This is the first large-scale study of the effect of visual grounding on language representations, including 46 semantic parameters.
  arXiv  Detail & Related papers  (2023-06-04T12:53:12Z)
• Explainable Semantic Space by Grounding Language to Vision with Cross-Modal Contrastive Learning [3.441021278275805]
  We design a two-stream model for grounding language learning in vision. The model first learns to align visual and language representations with the MS COCO dataset. After training, the language stream of this model is a stand-alone language model capable of embedding concepts in a visually grounded semantic space.
  arXiv  Detail & Related papers  (2021-11-13T19:54:15Z)
• On the Language-specificity of Multilingual BERT and the Impact of Fine-tuning [7.493779672689531]
  The knowledge acquired by multilingual BERT (mBERT) has two components: a language-specific and a language-neutral one. This paper analyses the relationship between them, in the context of fine-tuning on two tasks.
  arXiv  Detail & Related papers  (2021-09-14T19:28:31Z)
• From Two to One: A New Scene Text Recognizer with Visual Language Modeling Network [70.47504933083218]
  We propose a Visual Language Modeling Network (VisionLAN), which views the visual and linguistic information as a union. VisionLAN significantly improves the speed by 39% and adaptively considers the linguistic information to enhance the visual features for accurate recognition.
  arXiv  Detail & Related papers  (2021-08-22T07:56:24Z)
• VidLanKD: Improving Language Understanding via Video-Distilled Knowledge Transfer [76.3906723777229]
  We present VidLanKD, a video-language knowledge distillation method for improving language understanding. We train a multi-modal teacher model on a video-text dataset, and then transfer its knowledge to a student language model with a text dataset. In our experiments, VidLanKD achieves consistent improvements over text-only language models and vokenization models.
  arXiv  Detail & Related papers  (2021-07-06T15:41:32Z)
• UC2: Universal Cross-lingual Cross-modal Vision-and-Language Pre-training [52.852163987208826]
  UC2 is the first machine translation-augmented framework for cross-lingual cross-modal representation learning. We propose two novel pre-training tasks, namely Masked Region-to-Token Modeling (MRTM) and Visual Translation Language Modeling (VTLM) Our proposed framework achieves new state-of-the-art on diverse non-English benchmarks while maintaining comparable performance to monolingual pre-trained models on English tasks.
  arXiv  Detail & Related papers  (2021-04-01T08:30:53Z)
• Vokenization: Improving Language Understanding with Contextualized, Visual-Grounded Supervision [110.66085917826648]
  We develop a technique that extrapolates multimodal alignments to language-only data by contextually mapping language tokens to their related images. "vokenization" is trained on relatively small image captioning datasets and we then apply it to generate vokens for large language corpora. Trained with these contextually generated vokens, our visually-supervised language models show consistent improvements over self-supervised alternatives on multiple pure-language tasks.
  arXiv  Detail & Related papers  (2020-10-14T02:11:51Z)
• Visual Grounding in Video for Unsupervised Word Translation [91.47607488740647]
  We use visual grounding to improve unsupervised word mapping between languages. We learn embeddings from unpaired instructional videos narrated in the native language. We apply these methods to translate words from English to French, Korean, and Japanese.
  arXiv  Detail & Related papers  (2020-03-11T02:03:37Z)
• Object Relational Graph with Teacher-Recommended Learning for Video Captioning [92.48299156867664]
  We propose a complete video captioning system including both a novel model and an effective training strategy. Specifically, we propose an object relational graph (ORG) based encoder, which captures more detailed interaction features to enrich visual representation. Meanwhile, we design a teacher-recommended learning (TRL) method to make full use of the successful external language model (ELM) to integrate the abundant linguistic knowledge into the caption model.
  arXiv  Detail & Related papers  (2020-02-26T15:34:52Z)

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.