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For many computer vision applications, such as image description and humanidentification, recognizing the visual attributes of humans is an essential yetchallenging problem. Its challenges originate from its multi-label nature, thelarge underlying class imbalance and the lack of spatial annotations. Existingmethods follow either a computer vision approach while failing to account forclass imbalance, or explore machine learning solutions, which disregard thespatial and semantic relations that exist in the images. With that in mind, wepropose an effective method that extracts and aggregates visual attention masksat different scales. We introduce a loss function to handle class imbalanceboth at class and at an instance level and further demonstrate that penalizingattention masks with high prediction variance accounts for the weak supervisionof the attention mechanism. By identifying and addressing these challenges, weachieve state-of-the-art results with a simple attention mechanism in both PETAand WIDER-Attribute datasets without additional context or side information.

Deep Imbalanced Attribute Classification using Visual Attention Aggregation