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Found 2 papers, 1 papers with code
Dialog-based Interactive Image Retrieval
Experiments on both simulated and real-world data show that 1) our proposed learning framework achieves better accuracy than other supervised and reinforcement learning baselines and 2) user feedback based on natural language rather than pre-specified attributes leads to more effective retrieval results, and a more natural and expressive communication interface.
Walk and Learn: Facial Attribute Representation Learning from Egocentric Video and Contextual Data
These image pairs are then fed into a deep network that preserves similarity of images connected by the same track, in order to capture identity-related attribute features, and optimizes for location and weather prediction to capture additional facial attribute features.
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