The Impact of Mask-Text Alignment and Multi-Scale Ensemble on Uni-OVSeg's Segmentation Accuracy | HackerNoon
Briefly

from Hackernoon3 months ago
Our proposed Uni-OVSeg achieves significant gains of 4.8% PQ and 9.5% mIoU on the COCO dataset, demonstrating effective alignment of objects in images with text descriptions.
Hackernoonhttps://hackernoon.com/the-impact-of-mask-text-alignment-and-multi-scale-ensemble-on-uni-ovsegs-segmentation-accuracy
By refining text descriptions, we improve the mIoU from 34.5% to 37.3% on the COCO dataset, showing the importance of correlating new texts with images.
Hackernoonhttps://hackernoon.com/the-impact-of-mask-text-alignment-and-multi-scale-ensemble-on-uni-ovsegs-segmentation-accuracy
The ChatGPT-based parser extracts more reliable entities from text, leading to an improvement of 3.1% and 3.7% mIoU on COCO and PASCAL datasets respectively.
Hackernoonhttps://hackernoon.com/the-impact-of-mask-text-alignment-and-multi-scale-ensemble-on-uni-ovsegs-segmentation-accuracy
The multi-scale ensemble strategy enhances the mask-text matching, achieving a performance gain of 1.8% PQ on the COCO datasets by stabilizing mask-text correspondence.
Hackernoonhttps://hackernoon.com/the-impact-of-mask-text-alignment-and-multi-scale-ensemble-on-uni-ovsegs-segmentation-accuracy
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#computer-vision #natural-language-processing #mask-text-alignment #ablation-study #multi-scale-ensemble
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