A common use of language is to refer to visually present objects. Modelling it in computers requires modelling the link between language and perception. The "words as classifiers" model of grounded semantics views words as classifiers of perceptual contexts, and composes the meaning of a phrase through composition of the denotations of its component words. It was recently shown to perform well in a game-playing scenario with a small number of object types. We apply it to two large sets of real-world photographs that contain a much larger variety of types and for which referring expressions are available. Using a pre-trained convolutional neural network to extract image features, and augmenting these with in-picture positional information, we show that the model achieves performance competitive with the state of the art in a reference resolution task (given expression, find bounding box of its referent), while, as we argue, being conceptually simpler and more flexible.
This suggestion has variously been taken up in computational work. An early example is Deb Roy's work from the early 2000s @cite_0 @cite_9 @cite_14 . In @cite_0 , computer vision techniques are used to detect object boundaries in a video feed, and to compute colour features (mean colour pixel value), positional features, and features encoding the relative spatial configuration of objects. These features are then associated in a learning process with certain words, resulting in an association of colour features with colour words, spatial features with prepositions, etc., and based on this, these words can be interpreted with reference to the scene currently presented to the video feed.
@cite_0 @cite_9 @cite_14
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We present a trainable, visually-grounded, spoken language understanding system. The system acquires a grammar and vocabulary from a “show-and-tell” procedure in which visual scenes are paired with verbal descriptions. The system is embodied in a table-top mounted active vision platform. During training, a set of objects is placed in front of the vision system. Using a laser pointer, the system points to objects in random sequence, prompting a human teacher to provide spoken descriptions of the selected objects. The descriptions are transcribed and used to automatically acquire a visually-grounded vocabulary and grammar. Once trained, a person can interact with the system by verbally describing objects placed in front of the system. The system recognizes and robustly parses the speech and points, in real-time, to the object which best fits the visual semantics of the spoken description. A spoken language generation system has been developed that learns to describe objects in computer-generated visual scenes. The system is trained by a ‘show-and-tell" procedure in which visual scenes are paired with natural language descriptions. Learning algorithms acquire probabilistic structures which encode the visual semantics of phrase structure, word classes, and individual words. Using these structures, a planning algorithm integrates syntactic, semantic, and contextual constraints to generate natural and unambiguous descriptions of objects in novel scenes. The system generates syntactically well-formed compound adjective noun phrases, as well as relative spatial clauses. The acquired linguistic structures generalize from training data, enabling the production of novel word sequences which were never observed during training. The output of the generation system is synthesized using word-based concatenative synthesis drawing from the original training speech corpus. In evaluations of semantic comprehension by human judges, the performance of automatically generated spoken descriptions was comparable to human-generated descriptions. This work is motivated by our long-term goal of developing spoken language processing systems which grounds semantics in machine perception and action. ! 2002 Elsevier Science Ltd. All rights reserved. We use words to communicate about things and kinds of things, their properties, relations and actions. Researchers are now creating robotic and simulated systems that ground language in machine perception and action, mirroring human abilities. A new kind of computational model is emerging from this work that bridges the symbolic realm of language with the physical realm of real-world referents. It explains aspects of context-dependent shifts of word meaning that cannot easily be explained by purely symbolic models. An exciting implication for cognitive modeling is the use of grounded systems to ‘step into the shoes’ of humans by directly processing first-personperspective sensory data, providing a new methodology for testing various hypotheses of situated communication and learning.