@inproceedings{varuna-krishna-etal-2023-imaginator,
title = "{IMAGINATOR}: Pre-Trained {I}mage+{T}ext Joint Embeddings using Word-Level Grounding of Images",
author = "Varuna Krishna, Kolla and
Suryavardan, Suresh and
Shreyash, Mishra and
Sathyanarayanan, Ramamoorthy and
Parth, Patwa and
Megha, Chakraborty and
Aman, Chadha and
Amitava, Das and
Amit, Sheth",
editor = "Jyoti, D. Pawar and
Sobha, Lalitha Devi",
booktitle = "Proceedings of the 20th International Conference on Natural Language Processing (ICON)",
month = dec,
year = "2023",
address = "Goa University, Goa, India",
publisher = "NLP Association of India (NLPAI)",
url = "https://aclanthology.org/2023.icon-1.1",
pages = "1--18",
abstract = "Word embeddings, i.e., semantically meaningful vector representation of words, are largely influenced by the distributional hypothesis You shall know a word by the company it keeps (Harris, 1954), whereas modern prediction- based neural network embeddings rely on de- sign choices and hyperparameter optimization. Word embeddings like Word2Vec, GloVe etc. well capture the contextuality and real-world analogies but contemporary convolution-based image embeddings such as VGGNet, AlexNet, etc. do not capture contextual knowledge. The popular king-queen analogy does not hold true for most commonly used vision embeddings. In this paper, we introduce a pre-trained joint embedding (JE), named IMAGINATOR, trained on 21K distinct image objects. JE is a way to encode multimodal data into a vec- tor space where the text modality serves as the grounding key, which the complementary modality (in this case, the image) is anchored with. IMAGINATOR encapsulates three in- dividual representations: (i) object-object co- location, (ii) word-object co-location, and (iii) word-object correlation. These three ways cap- ture complementary aspects of the two modal- ities which are further combined to obtain the final object-word JEs. Generated JEs are intrinsically evaluated to assess how well they capture the contextual- ity and real-world analogies. We also evalu- ate pre-trained IMAGINATOR JEs on three downstream tasks: (i) image captioning, (ii) Im- age2Tweet, and (iii) text-based image retrieval. IMAGINATOR establishes a new standard on the aforementioned downstream tasks by out- performing the current SoTA on all the selected tasks. The code is available at https:// github.com/varunakk/IMAGINATOR.",
}
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%0 Conference Proceedings
%T IMAGINATOR: Pre-Trained Image+Text Joint Embeddings using Word-Level Grounding of Images
%A Varuna Krishna, Kolla
%A Suryavardan, Suresh
%A Shreyash, Mishra
%A Sathyanarayanan, Ramamoorthy
%A Parth, Patwa
%A Megha, Chakraborty
%A Aman, Chadha
%A Amitava, Das
%A Amit, Sheth
%Y Jyoti, D. Pawar
%Y Sobha, Lalitha Devi
%S Proceedings of the 20th International Conference on Natural Language Processing (ICON)
%D 2023
%8 December
%I NLP Association of India (NLPAI)
%C Goa University, Goa, India
%F varuna-krishna-etal-2023-imaginator
%X Word embeddings, i.e., semantically meaningful vector representation of words, are largely influenced by the distributional hypothesis You shall know a word by the company it keeps (Harris, 1954), whereas modern prediction- based neural network embeddings rely on de- sign choices and hyperparameter optimization. Word embeddings like Word2Vec, GloVe etc. well capture the contextuality and real-world analogies but contemporary convolution-based image embeddings such as VGGNet, AlexNet, etc. do not capture contextual knowledge. The popular king-queen analogy does not hold true for most commonly used vision embeddings. In this paper, we introduce a pre-trained joint embedding (JE), named IMAGINATOR, trained on 21K distinct image objects. JE is a way to encode multimodal data into a vec- tor space where the text modality serves as the grounding key, which the complementary modality (in this case, the image) is anchored with. IMAGINATOR encapsulates three in- dividual representations: (i) object-object co- location, (ii) word-object co-location, and (iii) word-object correlation. These three ways cap- ture complementary aspects of the two modal- ities which are further combined to obtain the final object-word JEs. Generated JEs are intrinsically evaluated to assess how well they capture the contextual- ity and real-world analogies. We also evalu- ate pre-trained IMAGINATOR JEs on three downstream tasks: (i) image captioning, (ii) Im- age2Tweet, and (iii) text-based image retrieval. IMAGINATOR establishes a new standard on the aforementioned downstream tasks by out- performing the current SoTA on all the selected tasks. The code is available at https:// github.com/varunakk/IMAGINATOR.
%U https://aclanthology.org/2023.icon-1.1
%P 1-18
Markdown (Informal)
[IMAGINATOR: Pre-Trained Image+Text Joint Embeddings using Word-Level Grounding of Images](https://aclanthology.org/2023.icon-1.1) (Varuna Krishna et al., ICON 2023)
ACL
- Kolla Varuna Krishna, Suresh Suryavardan, Mishra Shreyash, Ramamoorthy Sathyanarayanan, Patwa Parth, Chakraborty Megha, Chadha Aman, Das Amitava, and Sheth Amit. 2023. IMAGINATOR: Pre-Trained Image+Text Joint Embeddings using Word-Level Grounding of Images. In Proceedings of the 20th International Conference on Natural Language Processing (ICON), pages 1–18, Goa University, Goa, India. NLP Association of India (NLPAI).