CAT: Context Adjustment Training
Introduced by Kong et al. in Free Lunch for Co-Saliency Detection: Context Adjustment
CAT is a specialized dataset for co-saliency detection. This dataset is intended for both helping to assess the performance of vision algorithms and supporting research that aims to exploit large volumes of annotated data, e.g., for training deep neural networks.
Scale & Features - A total number of 33500 image samples. - 280 semantic groups affiliated to 15 superclasses. - High-quality mask annotations. - Diverse visual context with multiple foreground objects.
Papers
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