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Found 8 papers, 5 papers with code
Evaluating the Acquisition of Semantic Knowledge from Cross-situational Learning in Artificial Neural Networks
When learning their native language, children acquire the meanings of words and sentences from highly ambiguous input without much explicit supervision.
Modeling the Interaction Between Perception-Based and Production-Based Learning in Children’s Early Acquisition of Semantic Knowledge
In this work, we propose a model integrating both perception- and production-based learning using artificial neural networks which we train on a large corpus of crowd-sourced images with corresponding descriptions.
Automatic Annotation of Grammaticality in Child-Caregiver Conversations
The acquisition of grammar has been a central question to adjudicate between theories of language acquisition.
Modality-Agnostic fMRI Decoding of Vision and Language
Previous studies have shown that it is possible to map brain activation data of subjects viewing images onto the feature representation space of not only vision models (modality-specific decoding) but also language models (cross-modal decoding).
Do Vision-and-Language Transformers Learn Grounded Predicate-Noun Dependencies?
Recent advances in vision-and-language modeling have seen the development of Transformer architectures that achieve remarkable performance on multimodal reasoning tasks.
Learning English with Peppa Pig
In the real world the coupling between the linguistic and the visual modality is loose, and often confounded by correlations with non-semantic aspects of the speech signal.
Compositional Generalization in Image Captioning
Image captioning models are usually evaluated on their ability to describe a held-out set of images, not on their ability to generalize to unseen concepts.
On the Realization of Compositionality in Neural Networks
We present a detailed comparison of two types of sequence to sequence models trained to conduct a compositional task.
