Datasets › CAT: Context Adjustment Training
CAT: Context Adjustment Training
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.
Benchmarks archive 2025-07-28
No leaderboard in the archive resolves to this dataset.
Papers archive 2025-07-28
No paper in the archive has a leaderboard row on this dataset; the archive counts 1 paper for it but never published that list.
Dataset loaders archive 2025-07-28
No loader listed in the archive.
Tasks archive 2025-07-28
License archive 2025-07-28
Modalities archive 2025-07-28
Languages archive 2025-07-28
Variants archive 2025-07-28
- CAT: Context Adjustment Training
1 variant name, as the archive lists them.
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