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.
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.
Point cloud completion aims to recover the complete shape based on a partial observation.
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.
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.
Unsupervised point cloud completion aims to infer the whole geometry of a partial object observation without requiring partial-complete correspondence.
This study proposes an efficient algorithm for score computation for regime-switching models, and derived from which, an efficient expectation-maximization (EM) algorithm.
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.
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
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
As the basic task of point cloud analysis, classification is fundamental but always challenging.
Ranked #27 on 3D Point Cloud Classification on ModelNet40
This work addresses unconstrained fashion landmark detection, where clothing bounding boxes are not provided in both training and test.
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.
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.