7 citations · 22 across the 11 of their papers we have counts for
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cs.CV2023★ 1 cited
TinySAM: Pushing the Envelope for Efficient Segment Anything Model
Han Shu, Wenshuo Li, Yehui Tang +5
Recently segment anything model (SAM) has shown powerful segmentation capability and has drawn great attention in computer vision fields. Massive following works have developed var…
cs.CV2023★ 1 cited
DECO: Unleashing the Potential of ConvNets for Query-based Detection and Segmentation
Xinghao Chen, Siwei Li, Yijing Yang +1
Transformer and its variants have shown great potential for various vision tasks in recent years, including image classification, object detection and segmentation. Meanwhile, rece…
cs.CV2023★ 6 cited
PPT: Token Pruning and Pooling for Efficient Vision Transformers
Xinjian Wu, Fanhu Zeng, Xiudong Wang +1
Vision Transformers (ViTs) have emerged as powerful models in the field of computer vision, delivering superior performance across various vision tasks. However, the high computati…