Learning Deep Representations of Fine-grained Visual Descriptions

CVPR 2016 Scott ReedZeynep AkataBernt SchieleHonglak Lee

State-of-the-art methods for zero-shot visual recognition formulate learning as a joint embedding problem of images and side information. In these formulations the current best complement to visual features are attributes: manually encoded vectors describing shared characteristics among categories... (read more)

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