1 code implementation • 14 Sep 2023 • Wangchunshu Zhou, Yuchen Eleanor Jiang, Long Li, Jialong Wu, Tiannan Wang, Shi Qiu, Jintian Zhang, Jing Chen, Ruipu Wu, Shuai Wang, Shiding Zhu, Jiyu Chen, Wentao Zhang, Ningyu Zhang, Huajun Chen, Peng Cui, Mrinmaya Sachan
Recent advances on large language models (LLMs) enable researchers and developers to build autonomous language agents that can automatically solve various tasks and interact with environments, humans, and other agents using natural language interfaces.
no code implementations • 10 Aug 2023 • Ruikai Cui, Siyuan He, Shi Qiu
Foundation models, such as OpenAI's GPT-3 and GPT-4, Meta's LLaMA, and Google's PaLM2, have revolutionized the field of artificial intelligence.
1 code implementation • 27 Jul 2023 • Ruikai Cui, Shi Qiu, Saeed Anwar, Jiawei Liu, Chaoyue Xing, Jing Zhang, Nick Barnes
Point cloud completion aims to recover the complete shape based on a partial observation.
1 code implementation • 12 Jul 2023 • Humza Naveed, Asad Ullah Khan, Shi Qiu, Muhammad Saqib, Saeed Anwar, Muhammad Usman, Naveed Akhtar, Nick Barnes, Ajmal Mian
Considering the rapidly emerging plethora of literature on LLMs, it is imperative that the research community is able to benefit from a concise yet comprehensive overview of the recent developments in this field.
1 code implementation • 5 Dec 2022 • Jie Hong, Shi Qiu, Weihao Li, Saeed Anwar, Mehrtash Harandi, Nick Barnes, Lars Petersson
Specifically, we use the Unknown-Point Simulator to simulate out-of-distribution data in the training stage by manipulating the geometric context of partial known data.
1 code implementation • 13 Nov 2022 • Ruikai Cui, Shi Qiu, Saeed Anwar, Jing Zhang, Nick Barnes
Unsupervised point cloud completion aims to infer the whole geometry of a partial object observation without requiring partial-complete correspondence.
no code implementations • 3 May 2022 • Chaojun Li, Shi Qiu
This study proposes an efficient algorithm for score computation for regime-switching models, and derived from which, an efficient expectation-maximization (EM) algorithm.
2 code implementations • 24 Nov 2021 • Shi Qiu, Saeed Anwar, Nick Barnes
Given the rapid development of 3D scanners, point clouds are becoming popular in AI-driven machines.
1 code implementation • 16 Aug 2021 • Shi Qiu, Saeed Anwar, Nick Barnes
With the help of the deep learning paradigm, many point cloud networks have been invented for visual analysis.
1 code implementation • 2 Aug 2021 • Shi Qiu, Yunfan Wu, Saeed Anwar, Chongyi Li
Object detection in three-dimensional (3D) space attracts much interest from academia and industry since it is an essential task in AI-driven applications such as robotics, autonomous driving, and augmented reality.
2 code implementations • CVPR 2021 • Shi Qiu, Saeed Anwar, Nick Barnes
Given the prominence of current 3D sensors, a fine-grained analysis on the basic point cloud data is worthy of further investigation.
Ranked #6 on
Semantic Segmentation
on Semantic3D
1 code implementation • 14 May 2020 • Shi Qiu, Saeed Anwar, Nick Barnes
Our DRNet is designed to learn local point features from the point cloud in different resolutions.
Ranked #22 on
3D Part Segmentation
on ShapeNet-Part
2 code implementations • 28 Nov 2019 • Shi Qiu, Saeed Anwar, Nick Barnes
As the basic task of point cloud analysis, classification is fundamental but always challenging.
Ranked #27 on
3D Point Cloud Classification
on ModelNet40
2 code implementations • 7 Aug 2017 • Sijie Yan, Ziwei Liu, Ping Luo, Shi Qiu, Xiaogang Wang, Xiaoou Tang
This work addresses unconstrained fashion landmark detection, where clothing bounding boxes are not provided in both training and test.
no code implementations • CVPR 2016 • Ziwei Liu, Ping Luo, Shi Qiu, Xiaogang Wang, Xiaoou Tang
To demonstrate the advantages of DeepFashion, we propose a new deep model, namely FashionNet, which learns clothing features by jointly predicting clothing attributes and landmarks.
no code implementations • CVPR 2015 • Wanli Ouyang, Xiaogang Wang, Xingyu Zeng, Shi Qiu, Ping Luo, Yonglong Tian, Hongsheng Li, Shuo Yang, Zhe Wang, Chen-Change Loy, Xiaoou Tang
In this paper, we propose deformable deep convolutional neural networks for generic object detection.
no code implementations • 11 Sep 2014 • Wanli Ouyang, Ping Luo, Xingyu Zeng, Shi Qiu, Yonglong Tian, Hongsheng Li, Shuo Yang, Zhe Wang, Yuanjun Xiong, Chen Qian, Zhenyao Zhu, Ruohui Wang, Chen-Change Loy, Xiaogang Wang, Xiaoou Tang
In the proposed new deep architecture, a new deformation constrained pooling (def-pooling) layer models the deformation of object parts with geometric constraint and penalty.