551 citations · 628 across the 6 of their papers we have counts for
7 papers
Open-Vocabulary SAM: Segment and Recognize Twenty-thousand Classes Interactively
Haobo Yuan, Xiangtai Li, Chong Zhou +3
The CLIP and Segment Anything Model (SAM) are remarkable vision foundation models (VFMs). SAM excels in segmentation tasks across diverse domains, whereas CLIP is renowned for its…
EdgeSAM: Prompt-In-the-Loop Distillation for SAM
Chong Zhou, Xiangtai Li, Chen Change Loy +1
This paper presents EdgeSAM, an accelerated variant of the Segment Anything Model (SAM), optimized for efficient execution on edge devices with minimal compromise in performance. O…
Interpret Vision Transformers as ConvNets with Dynamic Convolutions
Chong Zhou, Chen Change Loy, Bo Dai
There has been a debate about the superiority between vision Transformers and ConvNets, serving as the backbone of computer vision models. Although they are usually considered as t…
Extract Free Dense Labels from CLIP
Chong Zhou, Chen Change Loy, Bo Dai
Contrastive Language-Image Pre-training (CLIP) has made a remarkable breakthrough in open-vocabulary zero-shot image recognition. Many recent studies leverage the pre-trained CLIP…
NOH-NMS: Improving Pedestrian Detection by Nearby Objects Hallucination
Penghao Zhou, Chong Zhou, Pai Peng +4
Greedy-NMS inherently raises a dilemma, where a lower NMS threshold will potentially lead to a lower recall rate and a higher threshold introduces more false positives. This proble…
YOLACT++: Better Real-time Instance Segmentation
Daniel Bolya, Chong Zhou, Fanyi Xiao +1
We present a simple, fully-convolutional model for real-time (>30 fps) instance segmentation that achieves competitive results on MS COCO evaluated on a single Titan Xp, which is s…
YOLACT: Real-time Instance Segmentation
Daniel Bolya, Chong Zhou, Fanyi Xiao +1
We present 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 significan…