What do Models Learn From Training on More Than Text? Measuring Visual Commonsense Knowledge

• URL: http://arxiv.org/abs/2205.07065v1
• Date: Sat, 14 May 2022 13:37:50 GMT
• Title: What do Models Learn From Training on More Than Text? Measuring Visual Commonsense Knowledge
• Authors: Lovisa Hagstr\"om and Richard Johansson
• Abstract summary: We introduce two evaluation tasks for measuring visual commonsense knowledge in language models. We find that the visual commonsense knowledge is not significantly different between the multimodal models and unimodal baseline models trained on visual text data.
• Score: 0.13706331473063876
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: There are limitations in learning language from text alone. Therefore, recent focus has been on developing multimodal models. However, few benchmarks exist that can measure what language models learn about language from multimodal training. We hypothesize that training on a visual modality should improve on the visual commonsense knowledge in language models. Therefore, we introduce two evaluation tasks for measuring visual commonsense knowledge in language models and use them to evaluate different multimodal models and unimodal baselines. Primarily, we find that the visual commonsense knowledge is not significantly different between the multimodal models and unimodal baseline models trained on visual text data.

Related papers

• The Less the Merrier? Investigating Language Representation in Multilingual Models [8.632506864465501]
  We investigate the linguistic representation of different languages in multilingual models. We observe from our experiments that community-centered models perform better at distinguishing between languages in the same family for low-resource languages.
  arXiv  Detail & Related papers  (2023-10-20T02:26:34Z)
• VLIS: Unimodal Language Models Guide Multimodal Language Generation [23.094728230459125]
  We introduce Visual-Language models as Importance Sampling weights (VLIS) It combines the visual conditioning capability of vision-language models with the language understanding of unimodal text-only language models without further training. VLIS improves vision-language models on diverse tasks, including commonsense understanding and complex text generation.
  arXiv  Detail & Related papers  (2023-10-15T07:58:52Z)
• TextBind: Multi-turn Interleaved Multimodal Instruction-following in the Wild [102.93338424976959]
  We introduce TextBind, an almost annotation-free framework for empowering larger language models with the multi-turn interleaved instruction-following capabilities. Our approach requires only image-caption pairs and generates multi-turn multimodal instruction-response conversations from a language model. To accommodate interleaved image-text inputs and outputs, we devise MIM, a language model-centric architecture that seamlessly integrates image encoder and decoder models.
  arXiv  Detail & Related papers  (2023-09-14T15:34:01Z)
• Large Multilingual Models Pivot Zero-Shot Multimodal Learning across Languages [76.35234803589412]
  MPM is an effective training paradigm for training large multimodal models in non-English languages. We build large multimodal models VisCPM in image-to-text and text-to-image generation, which achieve state-of-the-art (open-source) performance in Chinese.
  arXiv  Detail & Related papers  (2023-08-23T09:55:41Z)
• Images in Language Space: Exploring the Suitability of Large Language Models for Vision & Language Tasks [17.97052348690598]
  Large language models have demonstrated robust performance on various language tasks using zero-shot or few-shot learning paradigms. multimodal models that can additionally handle images as input have yet to catch up in size and generality with language-only models. We make visual information accessible to the language model using separate verbalisation models.
  arXiv  Detail & Related papers  (2023-05-23T07:50:36Z)
• Localization vs. Semantics: Visual Representations in Unimodal and Multimodal Models [57.08925810659545]
  We conduct a comparative analysis of the visual representations in existing vision-and-language models and vision-only models. Our empirical observations suggest that vision-and-language models are better at label prediction tasks. We hope our study sheds light on the role of language in visual learning, and serves as an empirical guide for various pretrained models.
  arXiv  Detail & Related papers  (2022-12-01T05:00:18Z)
• Visually-Augmented Language Modeling [137.36789885105642]
  We propose a novel pre-training framework, named VaLM, to Visually-augment text tokens with retrieved relevant images for Language Modeling. With the visually-augmented context, VaLM uses a visual knowledge fusion layer to enable multimodal grounded language modeling. We evaluate the proposed model on various multimodal commonsense reasoning tasks, which require visual information to excel.
  arXiv  Detail & Related papers  (2022-05-20T13:41:12Z)
• Large-scale Bilingual Language-Image Contrastive Learning [17.19890778916312]
  We collect 1.1 billion image-text pairs (708 million Korean and 476 million English) and train a bilingual multimodal model named KELIP. We introduce simple yet effective training schemes, including MAE pre-training and multi-crop augmentation. Experiments demonstrate that a model trained with such training schemes shows competitive performance in both languages.
  arXiv  Detail & Related papers  (2022-03-28T03:02:03Z)
• 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)
• InfoXLM: An Information-Theoretic Framework for Cross-Lingual Language Model Pre-Training [135.12061144759517]
  We present an information-theoretic framework that formulates cross-lingual language model pre-training. We propose a new pre-training task based on contrastive learning. By leveraging both monolingual and parallel corpora, we jointly train the pretext to improve the cross-lingual transferability of pre-trained models.
  arXiv  Detail & Related papers  (2020-07-15T16:58:01Z)

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.