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Found 5 papers, 0 papers with code
Thinking Outside the Pool: Active Training Image Creation for Relative Attributes
Current wisdom suggests more labeled image data is always better, and obtaining labels is the bottleneck.
Semantic Jitter: Dense Supervision for Visual Comparisons via Synthetic Images
Distinguishing subtle differences in attributes is valuable, yet learning to make visual comparisons remains non-trivial.
Just Noticeable Differences in Visual Attributes
We develop a Bayesian local learning strategy to infer when images are indistinguishable for a given attribute.
Predicting Useful Neighborhoods for Lazy Local Learning
Lazy local learning methods train a classifier on the fly" at test time, using only a subset of the training instances that are most relevant to the novel test example.
Fine-Grained Visual Comparisons with Local Learning
Given two images, we want to predict which exhibits a particular visual attribute more than the other---even when the two images are quite similar.
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