1 code implementation • 29 Feb 2024 • Chenghao Li, Lei Qi, Xin Geng
In this paper, considering these two critical factors, we propose a SAM-guided Two-stream Lightweight Model for unsupervised anomaly detection (STLM) that not only aligns with the two practical application requirements but also harnesses the robust generalization capabilities of SAM.
1 code implementation • 11 Jan 2024 • Chenghao Li, Boheng Zeng, Yi Lu, Pengbo Shi, Qingzi Chen, Jirui Liu, Lingyun Zhu
We revisit the relationship between attention mechanisms and large kernel ConvNets in visual transformers and propose a new spatial attention named Large Kernel Convolutional Attention (LKCA).
no code implementations • 22 Dec 2023 • Sam Alexander Martino, João Morado, Chenghao Li, Zhenghao Lu, Edina Rosta
In this work, we propose several GNN-based architectures to tackle the graph partitioning problem for Markov Chains described as kinetic networks.
1 code implementation • 13 Sep 2023 • Chenghao Li, Dake Chen, Yuke Zhang, Peter A. Beerel
While diffusion models demonstrate a remarkable capability for generating high-quality images, their tendency to `replicate' training data raises privacy concerns.
1 code implementation • 11 Sep 2023 • Chenghao Li, Chaoning Zhang
A straightforward way to locally adapt the self-attention matrix can be realized by an element-wise learnable weight mask (ELM), for which our preliminary results show promising results.
no code implementations • 19 Aug 2023 • Chenghao Li, Tonghan Wang, Chongjie Zhang, Qianchuan Zhao
In the realm of multi-agent reinforcement learning, intrinsic motivations have emerged as a pivotal tool for exploration.
Multi-agent Reinforcement Learning reinforcement-learning +2
no code implementations • 3 Jun 2023 • Chaoning Zhang, Yu Qiao, Shehbaz Tariq, Sheng Zheng, Chenshuang Zhang, Chenghao Li, Hyundong Shin, Choong Seon Hong
Different from label-oriented recognition tasks, the SAM is trained to predict a mask for covering the object shape based on a promt.
1 code implementation • 19 May 2023 • Yuhua Jiang, Qihan Liu, Xiaoteng Ma, Chenghao Li, Yiqin Yang, Jun Yang, Bin Liang, Qianchuan Zhao
In this paper, we aim to introduce diversity from the perspective that agents could have diverse risk preferences in the face of uncertainty.
no code implementations • 12 May 2023 • Chaoning Zhang, Fachrina Dewi Puspitasari, Sheng Zheng, Chenghao Li, Yu Qiao, Taegoo Kang, Xinru Shan, Chenshuang Zhang, Caiyan Qin, Francois Rameau, Lik-Hang Lee, Sung-Ho Bae, Choong Seon Hong
This is an ongoing project and we intend to update the manuscript on a regular basis.
no code implementations • 10 May 2023 • Chenghao Li, Chaoning Zhang
On top of that, this work presents an outlook on the development of AIGC in 3D from the data perspective.
no code implementations • 10 May 2023 • Chenghao Li, Chaoning Zhang, Atish Waghwase, Lik-Hang Lee, Francois Rameau, Yang Yang, Sung-Ho Bae, Choong Seon Hong
AI generated content) has made remarkable progress in the past few years, among which text-guided content generation is the most practical one since it enables the interaction between human instruction and AIGC.
no code implementations • 4 Apr 2023 • Chaoning Zhang, Chenshuang Zhang, Chenghao Li, Yu Qiao, Sheng Zheng, Sumit Kumar Dam, Mengchun Zhang, Jung Uk Kim, Seong Tae Kim, Jinwoo Choi, Gyeong-Moon Park, Sung-Ho Bae, Lik-Hang Lee, Pan Hui, In So Kweon, Choong Seon Hong
Overall, this work is the first to survey ChatGPT with a comprehensive review of its underlying technology, applications, and challenges.
no code implementations • 21 Mar 2023 • Chaoning Zhang, Chenshuang Zhang, Sheng Zheng, Yu Qiao, Chenghao Li, Mengchun Zhang, Sumit Kumar Dam, Chu Myaet Thwal, Ye Lin Tun, Le Luang Huy, Donguk Kim, Sung-Ho Bae, Lik-Hang Lee, Yang Yang, Heng Tao Shen, In So Kweon, Choong Seon Hong
As ChatGPT goes viral, generative AI (AIGC, a. k. a AI-generated content) has made headlines everywhere because of its ability to analyze and create text, images, and beyond.
no code implementations • ICCV 2023 • Yuke Zhang, Dake Chen, Souvik Kundu, Chenghao Li, Peter A. Beerel
Then, given our observation that external attention (EA) presents lower PI latency than widely-adopted self-attention (SA) at the cost of accuracy, we present a selective attention search (SAS) method to integrate the strength of EA and SA.
no code implementations • 12 Jul 2022 • Jianing Ye, Chenghao Li, Jianhao Wang, Chongjie Zhang
Decentralized execution is one core demand in cooperative multi-agent reinforcement learning (MARL).
Multi-agent Reinforcement Learning Policy Gradient Methods +2
no code implementations • 15 Oct 2021 • Siyang Wu, Tonghan Wang, Chenghao Li, Yang Hu, Chongjie Zhang
Multi-agent reinforcement learning tasks put a high demand on the volume of training samples.
1 code implementation • NeurIPS 2021 • Yiqin Yang, Xiaoteng Ma, Chenghao Li, Zewu Zheng, Qiyuan Zhang, Gao Huang, Jun Yang, Qianchuan Zhao
Moreover, we extend ICQ to multi-agent tasks by decomposing the joint-policy under the implicit constraint.
2 code implementations • NeurIPS 2021 • Chenghao Li, Tonghan Wang, Chengjie WU, Qianchuan Zhao, Jun Yang, Chongjie Zhang
Recently, deep multi-agent reinforcement learning (MARL) has shown the promise to solve complex cooperative tasks.
Multi-agent Reinforcement Learning reinforcement-learning +3
no code implementations • 10 Feb 2021 • Xiaoteng Ma, Yiqin Yang, Chenghao Li, Yiwen Lu, Qianchuan Zhao, Yang Jun
Value-based methods of multi-agent reinforcement learning (MARL), especially the value decomposition methods, have been demonstrated on a range of challenging cooperative tasks.
no code implementations • 26 Aug 2020 • Yiding Wang, Zhenyi Wang, Chenghao Li, Yilin Zhang, Haizhou Wang
In recent years, due to the mental burden of depression, the number of people who endanger their lives has been increasing rapidly.
no code implementations • 25 Jun 2020 • Chenghao Li, Xiaoteng Ma, Chongjie Zhang, Jun Yang, Li Xia, Qianchuan Zhao
In these tasks, our approach learns a diverse set of options, each of whose state-action space has strong coherence.