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Found 12 papers, 7 papers with code
HOLM: Hallucinating Objects with Language Models for Referring Expression Recognition in Partially-Observed Scenes
AI systems embodied in the physical world face a fundamental challenge of partial observability; operating with only a limited view and knowledge of the environment.
Refer360$^\circ$: A Referring Expression Recognition Dataset in 360$^\circ$ Images
We propose a novel large-scale referring expression recognition dataset, Refer360{\mbox{$^\circ$}}, consisting of 17, 137 instruction sequences and ground-truth actions for completing these instructions in 360{\mbox{$^\circ$}} scenes.
Speaker-Follower Models for Vision-and-Language Navigation
We use this speaker model to (1) synthesize new instructions for data augmentation and to (2) implement pragmatic reasoning, which evaluates how well candidate action sequences explain an instruction.
Visual Referring Expression Recognition: What Do Systems Actually Learn?
We present an empirical analysis of the state-of-the-art systems for referring expression recognition -- the task of identifying the object in an image referred to by a natural language expression -- with the goal of gaining insight into how these systems reason about language and vision.
Using Syntax to Ground Referring Expressions in Natural Images
We introduce GroundNet, a neural network for referring expression recognition -- the task of localizing (or grounding) in an image the object referred to by a natural language expression.
SearchQA: A New Q&A Dataset Augmented with Context from a Search Engine
We publicly release a new large-scale dataset, called SearchQA, for machine comprehension, or question-answering.
Learning grammatical categories using paradigmatic representations: Substitute words for language acquisition
Learning syntactic categories is a fundamental task in language acquisition.
Visualizing and Understanding Curriculum Learning for Long Short-Term Memory Networks
Curriculum Learning emphasizes the order of training instances in a computational learning setup.
Substitute Based SCODE Word Embeddings in Supervised NLP Tasks
The results show that the proposed method achieves as good as or better results compared to the other word embeddings in the tasks we investigate.
