The Uni-OVSeg framework effectively integrates visual and textual features for weakly-supervised open-vocabulary segmentation, addressing challenges in linking image-level tasks to pixel-level predictions.
By employing a CLIP model and employing techniques such as mask-text bipartite matching, we enhance mask generation enabling effective open-vocabulary segmentation.
#artificial-intelligence #image-processing #open-vocabulary-segmentation #clip-model #weakly-supervised-learning
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