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TACo: Token-aware Cascade Contrastive Learning for Video-Text Alignment ...
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Few-shot Language Coordination by Modeling Theory of Mind ...
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An Empirical Study on the Generalization Power of Neural Representations Learned via Visual Guessing Games ...
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Dependency Induction Through the Lens of Visual Perception ...
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Dependency Induction Through the Lens of Visual Perception ...
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Imagining Grounded Conceptual Representations from Perceptual Information in Situated Guessing Games ...
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Abstract:
In visual guessing games, a Guesser has to identify a target object in a scene by asking questions to an Oracle. An effective strategy for the players is to learn conceptual representations of objects that are both discriminative and expressive enough to ask questions and guess correctly. However, as shown by Suglia et al. (2020), existing models fail to learn truly multi-modal representations, relying instead on gold category labels for objects in the scene both at training and inference time. This provides an unnatural performance advantage when categories at inference time match those at training time, and it causes models to fail in more realistic "zero-shot" scenarios where out-of-domain object categories are involved. To overcome this issue, we introduce a novel "imagination" module based on Regularized Auto-Encoders, that learns context-aware and category-aware latent embeddings without relying on category labels at inference time. Our imagination module outperforms state-of-the-art competitors by ... : Accepted to the International Conference on Computational Linguistics (COLING) 2020 ...
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Keyword:
Computation and Language cs.CL; Computer Vision and Pattern Recognition cs.CV; FOS Computer and information sciences; Machine Learning cs.LG
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URL: https://dx.doi.org/10.48550/arxiv.2011.02917 https://arxiv.org/abs/2011.02917
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Imagining Grounded Conceptual Representations from Perceptual Information in Situated Guessing Games ...
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The Return of Lexical Dependencies: Neural Lexicalized PCFGs ...
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Shifting the Baseline: Single Modality Performance on Visual Navigation & QA ...
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Unsupervised grammar induction with Combinatory Categorial Grammars
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