1 code implementation • 25 Apr 2024 • Guohao Li, Hongyu Yang, Di Huang, Yunhong Wang
Generative 3D face models featuring disentangled controlling factors hold immense potential for diverse applications in computer vision and computer graphics.
1 code implementation • 7 Feb 2024 • Chengxing Xie, Canyu Chen, Feiran Jia, Ziyu Ye, Kai Shu, Adel Bibi, Ziniu Hu, Philip Torr, Bernard Ghanem, Guohao Li
In addition, we probe into the biases in agent trust and the differences in agent trust towards agents and humans.
no code implementations • 19 Aug 2023 • Kun Wang, Guohao Li, Shilong Wang, Guibin Zhang, Kai Wang, Yang You, Xiaojiang Peng, Yuxuan Liang, Yang Wang
Despite Graph Neural Networks demonstrating considerable promise in graph representation learning tasks, GNNs predominantly face significant issues with over-fitting and over-smoothing as they go deeper as models of computer vision realm.
no code implementations • 26 May 2023 • Mingchen Zhuge, Haozhe Liu, Francesco Faccio, Dylan R. Ashley, Róbert Csordás, Anand Gopalakrishnan, Abdullah Hamdi, Hasan Abed Al Kader Hammoud, Vincent Herrmann, Kazuki Irie, Louis Kirsch, Bing Li, Guohao Li, Shuming Liu, Jinjie Mai, Piotr Piękos, Aditya Ramesh, Imanol Schlag, Weimin Shi, Aleksandar Stanić, Wenyi Wang, Yuhui Wang, Mengmeng Xu, Deng-Ping Fan, Bernard Ghanem, Jürgen Schmidhuber
What should be the social structure of an NLSOM?
no code implementations • 26 May 2023 • Junting Chen, Guohao Li, Suryansh Kumar, Bernard Ghanem, Fisher Yu
Our method propagates semantics on the scene graphs based on language priors and scene statistics to introduce semantic knowledge to the geometric frontiers.
2 code implementations • NeurIPS 2023 • Guohao Li, Hasan Abed Al Kader Hammoud, Hani Itani, Dmitrii Khizbullin, Bernard Ghanem
Our contributions include introducing a novel communicative agent framework, offering a scalable approach for studying the cooperative behaviors and capabilities of multi-agent systems, and open-sourcing our library to support research on communicative agents and beyond: https://github. com/camel-ai/camel.
1 code implementation • 21 Nov 2022 • Ling Yang, Zhilin Huang, Yang song, Shenda Hong, Guohao Li, Wentao Zhang, Bin Cui, Bernard Ghanem, Ming-Hsuan Yang
Generating images from graph-structured inputs, such as scene graphs, is uniquely challenging due to the difficulty of aligning nodes and connections in graphs with objects and their relations in images.
no code implementations • 7 Nov 2022 • Guohao Li, Hu Yang, Feng He, Zhifan Feng, Yajuan Lyu, Hua Wu, Haifeng Wang
To this end, we propose a Cross-modaL knOwledge-enhanced Pre-training (CLOP) method with Knowledge Regularizations.
no code implementations • 28 Oct 2022 • Wei Li, Xue Xu, Xinyan Xiao, Jiachen Liu, Hu Yang, Guohao Li, Zhanpeng Wang, Zhifan Feng, Qiaoqiao She, Yajuan Lyu, Hua Wu
Diffusion generative models have recently greatly improved the power of text-conditioned image generation.
no code implementations • 4 May 2022 • Zhen Dong, Kaicheng Zhou, Guohao Li, Qiang Zhou, Mingfei Guo, Bernard Ghanem, Kurt Keutzer, Shanghang Zhang
Neural architecture search (NAS) has shown great success in the automatic design of deep neural networks (DNNs).
1 code implementation • 11 Apr 2022 • Guocheng Qian, Xuanyang Zhang, Guohao Li, Chen Zhao, Yukang Chen, Xiangyu Zhang, Bernard Ghanem, Jian Sun
TNAS performs a modified bi-level Breadth-First Search in the proposed trees to discover a high-performance architecture.
no code implementations • 23 Mar 2022 • Bing Li, Cheng Zheng, Guohao Li, Bernard Ghanem
To provide an alternative, we propose a novel approach that utilizes monocular RGB images and point clouds to generate pseudo scene flow labels for training scene flow networks.
1 code implementation • NeurIPS 2021 • Guocheng Qian, Hasan Abed Al Kader Hammoud, Guohao Li, Ali Thabet, Bernard Ghanem
We then introduce a new Anisotropic Reduction function into our Separable SA module and propose an Anisotropic Separable SA (ASSA) module that substantially increases the network's accuracy.
Ranked #33 on 3D Part Segmentation on ShapeNet-Part
no code implementations • 14 Oct 2021 • Guohao Li, Feng He, Zhifan Feng
This technical report summarizes our method for the Video-And-Language Understanding Evaluation (VALUE) challenge (https://value-benchmark. github. io/challenge\_2021. html).
4 code implementations • 14 Jun 2021 • Guohao Li, Matthias Müller, Bernard Ghanem, Vladlen Koltun
Deep graph neural networks (GNNs) have achieved excellent results on various tasks on increasingly large graph datasets with millions of nodes and edges.
Ranked #1 on Node Property Prediction on ogbn-proteins
no code implementations • 1 Jan 2021 • Guohao Li, Chenxin Xiong, Ali Thabet, Bernard Ghanem
We add our generalized aggregation into a deep GCN framework and show it achieves state-of-the-art results in six benchmarks from OGB.
3 code implementations • CVPR 2022 • Kezhi Kong, Guohao Li, Mucong Ding, Zuxuan Wu, Chen Zhu, Bernard Ghanem, Gavin Taylor, Tom Goldstein
Data augmentation helps neural networks generalize better by enlarging the training set, but it remains an open question how to effectively augment graph data to enhance the performance of GNNs (Graph Neural Networks).
Ranked #1 on Graph Property Prediction on ogbg-ppa
no code implementations • 24 Aug 2020 • Guohao Li, Mengmeng Xu, Silvio Giancola, Ali Thabet, Bernard Ghanem
In this paper, we introduce a new NAS framework, dubbed LC-NAS, where we search for point cloud architectures that are constrained to a target latency.
3 code implementations • 13 Jun 2020 • Guohao Li, Chenxin Xiong, Ali Thabet, Bernard Ghanem
Graph Convolutional Networks (GCNs) have been drawing significant attention with the power of representation learning on graphs.
Ranked #1 on Node Property Prediction on ogbn-proteins
1 code implementation • CVPR 2020 • Guohao Li, Guocheng Qian, Itzel C. Delgadillo, Matthias Müller, Ali Thabet, Bernard Ghanem
Architecture design has become a crucial component of successful deep learning.
Ranked #4 on Node Classification on PPI
1 code implementation • CVPR 2021 • Guocheng Qian, Abdulellah Abualshour, Guohao Li, Ali Thabet, Bernard Ghanem
We combine Inception DenseGCN with NodeShuffle into a new point upsampling pipeline called PU-GCN.
4 code implementations • 15 Oct 2019 • Guohao Li, Matthias Müller, Guocheng Qian, Itzel C. Delgadillo, Abdulellah Abualshour, Ali Thabet, Bernard Ghanem
This work transfers concepts such as residual/dense connections and dilated convolutions from CNNs to GCNs in order to successfully train very deep GCNs.
Ranked #5 on 3D Semantic Segmentation on PartNet
no code implementations • 27 May 2019 • Haiping Zhu, Yuheng Zhang, Guohao Li, Junping Zhang, Hongming Shan
This paper proposes an ordinal distribution regression with a global and local convolutional neural network for gait-based age estimation.
no code implementations • 18 Apr 2019 • Matthias Müller, Guohao Li, Vincent Casser, Neil Smith, Dominik L. Michels, Bernard Ghanem
A common approach is to learn an end-to-end policy that directly predicts controls from raw images by imitating an expert.
1 code implementation • ICCV 2019 • Guohao Li, Matthias Müller, Ali Thabet, Bernard Ghanem
Finally, we use these new concepts to build a very deep 56-layer GCN, and show how it significantly boosts performance (+3. 7% mIoU over state-of-the-art) in the task of point cloud semantic segmentation.
no code implementations • 3 Mar 2018 • Guohao Li, Matthias Müller, Vincent Casser, Neil Smith, Dominik L. Michels, Bernard Ghanem
Recent work has explored the problem of autonomous navigation by imitating a teacher and learning an end-to-end policy, which directly predicts controls from raw images.
no code implementations • 2 Jan 2018 • Shuheng Wang, Guohao Li, Yifan Bao
Support vector machine is a new type of machine learning method proposed in 1990s.
no code implementations • 3 Dec 2017 • Guohao Li, Hang Su, Wenwu Zhu
To address this issue, we propose a novel framework which endows the model capabilities in answering more complex questions by leveraging massive external knowledge with dynamic memory networks.