The proposed Uni-OVSeg framework utilizes a ConvNext-based CLIP model architecture to enhance image and text encoding efficiency, demonstrating improved performance in visual segmentation tasks.
Our experimentation reveals that the implementation of multi-scale features through the ConvNext encoder significantly boosts segmentation accuracy, showcasing the importance of diverse feature representations in computer vision.
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