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- YOLACT: Real-Time Instance Segmentation2,274
A simple, fully-convolutional model for real-time instance segmentation that achieves 29.8 mAP on MS COCO at 33.5 fps evaluated on a single Titan Xp, which is significantly faster than any previous competitive approach.
- Extract Free Dense Labels from CLIP856
The finding suggests that MaskCLIP can serve as a new reliable source of supervision for dense prediction tasks to achieve annotation-free segmentation in pixel-level dense prediction, specifically in semantic segmentation.
- YOLACT++ Better Real-Time Instance Segmentation544
A simple, fully-convolutional model for real-time instance segmentation that achieves competitive results on MS COCO evaluated on a single Titan Xp, which is significantly faster than any previous state-of-the-art approach.
- Open-Vocabulary SAM: Segment and Recognize Twenty-Thousand Classes Interactively114
The Open-Vocabulary SAM is introduced, a SAM-inspired model designed for simultaneous interactive segmentation and recognition, leveraging two unique knowledge transfer modules: SAM2CLIP and CLIP2SAM.
- EdgeSAM: Prompt-In-the-Loop Distillation for SAM71
The approach involves distilling the original ViT-based SAM image encoder into a purely CNN-based architecture, better suited for edge devices, and incorporates a lightweight module within the encoder, which achieves a 37-fold speed increase compared to the original SAM.
- NOH-NMS51
This work proposes \heatmapname (\heatmapnameshort ), which pinpoints the objects nearby each proposal with a Gaussian distribution, together with \nmsname, which dynamically eases the suppression for the space that might contain other objects with a high likelihood.
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Publication data from OpenAlex; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-11. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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