59 citations · 164 across the 33 of their papers we have counts for
4 papers · 1 filter
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…
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…
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…
Less is More: Focus Attention for Efficient DETR
Dehua Zheng, Wenhui Dong, Hailin Hu +2
DETR-like models have significantly boosted the performance of detectors and even outperformed classical convolutional models. However, all tokens are treated equally without discr…