SegGPT Meets Co-Saliency Scene
This work addresses co-salient object detection for computer vision researchers, but it is incremental as it adapts an existing model to a new task.
The study applied SegGPT, a generalist segmentation model, to co-salient object detection, finding that it struggles with context discrepancies in co-saliency image groups, as evidenced by performance evaluations on three datasets.
Co-salient object detection targets at detecting co-existed salient objects among a group of images. Recently, a generalist model for segmenting everything in context, called SegGPT, is gaining public attention. In view of its breakthrough for segmentation, we can hardly wait to probe into its contribution to the task of co-salient object detection. In this report, we first design a framework to enable SegGPT for the problem of co-salient object detection. Proceed to the next step, we evaluate the performance of SegGPT on the problem of co-salient object detection on three available datasets. We achieve a finding that co-saliency scenes challenges SegGPT due to context discrepancy within a group of co-saliency images.