Visual attribute labeling of images
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In this work, we analyze and apply various recent techniques in visual attribute recognition and labeling on a common benchmark dataset in order to motivate the design of a novel framework for this task. Using the large scale COCO Attributes dataset as our benchmark, we systematically investigate recent techniques and advances in the attribute recognition literature in a unified fashion, drawing comparisons and insights from the results. We leverage these insights to propose a new models and loss function to better model the function space of attribute prediction models. Our proposed techniques are based on standard efficient building blocks readily available to researchers and practitioners, are conceptually simple, and are theoretically grounded, while giving state-of-the-art results, and generalises to various sub-domains of attribute recognition. Experiments and ablation studies performed on our model and other methods further corroborate the design decisions for our framework and shed light on possible future avenues of investigation. Our hope is that our model serves as a tool to embed strong visual attribute recognition in more complex visual reasoning tasks and pipelines.