1 code implementation • 5 Jan 2024 • Haobo Yuan, Xiangtai Li, Chong Zhou, Yining Li, Kai Chen, Chen Change Loy
The CLIP and Segment Anything Model (SAM) are remarkable vision foundation models (VFMs).
1 code implementation • 11 Dec 2023 • Chong Zhou, Xiangtai Li, Chen Change Loy, Bo Dai
It is also the first SAM variant that can run at over 30 FPS on an iPhone 14.
no code implementations • 19 Sep 2023 • 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.
1 code implementation • 2 Dec 2021 • 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.
1 code implementation • 27 Jul 2020 • Penghao Zhou, Chong Zhou, Pai Peng, Junlong Du, Xing Sun, Xiaowei Guo, Feiyue Huang
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.
Ranked #12 on Object Detection on CrowdHuman (full body)
36 code implementations • 3 Dec 2019 • Daniel Bolya, Chong Zhou, Fanyi Xiao, Yong Jae Lee
Then we produce instance masks by linearly combining the prototypes with the mask coefficients.
Ranked #15 on Real-time Instance Segmentation on MSCOCO (using extra training data)
48 code implementations • ICCV 2019 • Daniel Bolya, Chong Zhou, Fanyi Xiao, Yong Jae Lee
Then we produce instance masks by linearly combining the prototypes with the mask coefficients.
Ranked #21 on Real-time Instance Segmentation on MSCOCO (using extra training data)
1 code implementation • 10 Nov 2018 • Haitao Liu, Randy C. Paffenroth, Jian Zou, Chong Zhou
Accordingly, we propose a novel optimization problem that is similar in spirit to Robust Principal Component Analysis (RPCA) and splits the sample covariance matrix $M$ into two parts, $M=F+S$, where $F$ is the cleaned sample covariance whose inverse is sparse and computable by Graphical Lasso, and $S$ contains the outliers in $M$.
2 code implementations • 28 Sep 2018 • Yezheng Liu, Zhe Li, Chong Zhou, Yuanchun Jiang, Jianshan Sun, Meng Wang, Xiangnan He
In this paper, we approach outlier detection as a binary-classification issue by sampling potential outliers from a uniform reference distribution.
1 code implementation • KDD '17 Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 2017 • Chong Zhou, Randy C. Paffenroth
Deep autoencoders, and other deep neural networks, have demonstrated their effectiveness in discovering non-linear features across many problem domains.