Learning Visually-Grounded Semantics from Contrastive Adversarial Samples (C18-1)
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| Challenge: | Existing frameworks for grounding distributional representations of texts on the visual domain are limited . effective and efficient grounding of distributional embeddings remains challenging . |
| Approach: | They propose to ground distributional representations of texts on the visual domain using visual-semantic embeddings. |
| Outcome: | The proposed model improves on a diverse set of downstream tasks and defends known-type adversarial attacks. |
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| Challenge: | Visual language grounding is widely studied in modern neural image captioning systems . a novel algorithm for crafting adversarial examples in image captions is proposed . |
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Zijian Zhang, Chang Shu, Ya Xiao, Yuan Shen, Di Zhu, Youxin Chen, Jing Xiao, Jey Han Lau, Qian Zhang, Zheng Lu
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| Challenge: | Unsupervised sentence representation models suffer from the grounding problem because of lack of association between symbols and external information. |
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Measuring Social Biases in Grounded Vision and Language Embeddings (2021.naacl-main)
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| Challenge: | Existing methods to measure social biases in word embeddings are limited to visually grounded word embeds . a new study generalizes word embedment associations to visually ground word embeddas . |
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SimCSE: Simple Contrastive Learning of Sentence Embeddings (2021.emnlp-main)
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| Challenge: | Existing methods for learning universal sentence embeddings are based on unsupervised approaches with only dropout as noise. |
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| Challenge: | a large-scale lookup operation to ground language via ‘snapshots’ of our physical world accessed through image search is currently used to learn word representations. |
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Adversarial Contrastive Estimation (P18-1)
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| Challenge: | Noise contrastive estimation (NCE) is a general strategy used in word embeddings and translations for knowledge graphs. |
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Visually Grounded Compound PCFGs (2020.emnlp-main)
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| Challenge: | Existing work on visual groundings for language understanding has been drawing much attention. |
| Approach: | They propose to use an extension of probabilistic context-free grammar model to do fully-differentiable end-to-end visually grounded learning. |
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