2015/01/12 by Angeliki Lazaridou, Georgiana Dinu, Lazaridou, Angeliki +5
Computer Science · #Advanced Image and Video Retrieval Techniques #Computation and Language (cs.CL) #Computer Vision and Pattern Recognition (cs.CV) #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #Multimodal Machine Learning Applications #cs.CL #cs.CV
paper · pdf · doi:10.48550/arxiv.1501.02714
accepted at Transactions of the Association for Computational Linguistics (TACL), 3/2015
openalex publication_date 2015/01/12 · arxiv created 2015/03/24 · arxiv updated 2015/03/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
As automated image analysis progresses, there is increasing interest in richer linguistic annotation of pictures, with attributes of objects (e.g., furry, brown...) attracting most attention. By building on the recent "zero-shot learning" approach, and paying attention to the linguistic nature of attributes as noun modifiers, and specifically adjectives, we show that it is possible to tag images with attribute-denoting adjectives even when no training data containing the relevant annotation are available. Our approach relies on two key observations. First, objects can be seen as bundles of attributes, typically expressed as adjectival modifiers (a dog is something furry, brown, etc.), and thus a function trained to map visual representations of objects to nominal labels can implicitly learn to map attributes to adjectives. Second, objects and attributes come together in pictures (the same thing is a dog and it is brown). We can thus achieve better attribute (and object) label retrieval by treating images as "visual phrases", and decomposing their linguistic representation into an attribute-denoting adjective and an object-denoting noun. Our approach performs comparably to a method exploiting manual attribute annotation, it outperforms various competitive alternatives in both attribute and object annotation, and it automatically constructs attribute-centric representations that significantly improve performance in supervised object recognition.