3 accepted papers
Current wisdom suggests more labeled image data is always better, and obtaining labels is the bottleneck. Yet curating a pool of sufficiently diverse and informative images is itself a challenge. In particular, training image curation is problematic for fine-grained attributes, where the subtle visu…
Distinguishing subtle differences in attributes is valuable, yet learning to make visual comparisons remains nontrivial. Not only is the number of possible comparisons quadratic in the number of training images, but also access to images adequately spanning the space of fine-grained visual differenc…
We explore the problem of predicting "just noticeable differences" in a visual attribute. While some pairs of images have a clear ordering for an attribute (e.g., A is more sporty than B), for others the difference may be indistinguishable to human observers. However, existing relative attribute m…