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Attaching attributes (such as color, shape, state, action) to objectcategories is an important computer vision problem. Attribute prediction hasseen exciting recent progress and is often formulated as a multi-labelclassification problem. Yet significant challenges remain in: 1) predictingdiverse attributes over multiple categories, 2) modeling attributes-categorydependency, 3) capturing both global and local scene context, and 4) predictingattributes of objects with low pixel-count. To address these issues, we proposea novel multi-category attribute prediction deep architecture named GlideNet,which contains three distinct feature extractors. A global feature extractorrecognizes what objects are present in a scene, whereas a local one focuses onthe area surrounding the object of interest. Meanwhile, an intrinsic featureextractor uses an extension of standard convolution dubbed Informed Convolutionto retrieve features of objects with low pixel-count. GlideNet uses gatingmechanisms with binary masks and its self-learned category embedding to combinethe dense embeddings. Collectively, the Global-Local-Intrinsic blockscomprehend the scene's global context while attending to the characteristics ofthe local object of interest. Finally, using the combined features, aninterpreter predicts the attributes, and the length of the output is determinedby the category, thereby removing unnecessary attributes. GlideNet can achievecompelling results on two recent and challenging datasets -- VAW and CAR -- forlarge-scale attribute prediction. For instance, it obtains more than 5\% gainover state of the art in the mean recall (mR) metric. GlideNet's advantages areespecially apparent when predicting attributes of objects with low pixel countsas well as attributes that demand global context understanding. Finally, weshow that GlideNet excels in training starved real-world scenarios.

GlideNet: Global, Local and Intrinsic based Dense Embedding NETwork for Multi-category Attributes Prediction