In this paper, we address the challenge of open-vocabulary segmentation, crucial for applications where flexibility in understanding and segmenting diverse objects is required.
The proposed Uni-OVSeg framework advances state-of-the-art segmentation techniques by efficiently generating masks and aligning them with textual descriptions, enhancing interpretability.
Our experimental results demonstrate the effectiveness of Uni-OVSeg, showing significant improvements over existing methods in both accuracy and computational efficiency.
Ablation studies reveal that key components of our framework, such as mask-text alignment, play a critical role in achieving high performance.
#open-vocabulary-segmentation #ai-frameworks #machine-learning #computer-vision #experimental-results
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