34 citations · 115 across the 15 of their papers we have counts for
15 papers
Boosting Value Decomposition via Unit-Wise Attentive State Representation for Cooperative Multi-Agent Reinforcement Learning
Qingpeng Zhao, Yuanyang Zhu, Zichuan Liu +2
In cooperative multi-agent reinforcement learning (MARL), the environmental stochasticity and uncertainties will increase exponentially when the number of agents increases, which p…
NQ: Neural Attention Additive Model for Interpretable Multi-Agent Q-Learning
Zichuan Liu, Yuanyang Zhu, Chunlin Chen
Value decomposition is widely used in cooperative multi-agent reinforcement learning, however, its implicit credit assignment mechanism is not yet fully understood due to black-box…
Recursive Reasoning in Minimax Games: A Level Gradient Play Method
Zichu Liu, Lacra Pavel
Despite the success of generative adversarial networks (GANs) in generating visually appealing images, they are notoriously challenging to train. In order to stabilize the learning…
Unguided Self-exploration in Narrow Spaces with Safety Region Enhanced Reinforcement Learning for Ackermann-steering Robots
Zhaofeng Tian, Zichuan Liu, Xingyu Zhou +1
In narrow spaces, motion planning based on the traditional hierarchical autonomous system could cause collisions due to mapping, localization, and control noises, especially for ca…
MIXRTs: Toward Interpretable Multi-Agent Reinforcement Learning via Mixing Recurrent Soft Decision Trees
Zichuan Liu, Yuanyang Zhu, Zhi Wang +2
While achieving tremendous success in various fields, existing multi-agent reinforcement learning (MARL) with a black-box neural network makes decisions in an opaque manner that hi…
Simulators for Mobile Social Robots:State-of-the-Art and Challenges
Prabhjot Kaur, Zichuan Liu, Weisong Shi
The future robots are expected to work in a shared physical space with humans [1], however, the presence of humans leads to a dynamic environment that is challenging for mobile rob…
Multi View Spatial-Temporal Model for Travel Time Estimation
ZiChuan Liu, Zhaoyang Wu, Meng Wang +1
Taxi arrival time prediction is essential for building intelligent transportation systems. Traditional prediction methods mainly rely on extracting features from traffic maps, whic…
Spatial-temporal Conv-sequence Learning with Accident Encoding for Traffic Flow Prediction
Zichuan Liu, Rui Zhang, Chen Wang +2
In an intelligent transportation system, the key problem of traffic forecasting is how to extract periodic temporal dependencies and complex spatial correlations. Current state-of-…
Progressive Self-Guided Loss for Salient Object Detection
Sheng Yang, Weisi Lin, Guosheng Lin +2
We present a simple yet effective progressive self-guided loss function to facilitate deep learning-based salient object detection (SOD) in images. The saliency maps produced by th…
Towards Robust Curve Text Detection with Conditional Spatial Expansion
Zichuan Liu, Guosheng Lin, Sheng Yang +3
It is challenging to detect curve texts due to their irregular shapes and varying sizes. In this paper, we first investigate the deficiency of the existing curve detection methods…
Compressed CSI Feedback With Learned Measurement Matrix for mmWave Massive MIMO
Pengxia Wu, Zichuan Liu, Julian Cheng
A major challenge to implement the compressed sensing method for channel state information (CSI) acquisition lies in the design of a well-performed measurement matrix to reduce the…
Correlation Propagation Networks for Scene Text Detection
Zichuan Liu, Guosheng Lin, Wang Ling Goh +3
In this work, we propose a novel hybrid method for scene text detection namely Correlation Propagation Network (CPN). It is an end-to-end trainable framework engined by advanced Co…
Learning Markov Clustering Networks for Scene Text Detection
Zichuan Liu, Guosheng Lin, Sheng Yang +3
A novel framework named Markov Clustering Network (MCN) is proposed for fast and robust scene text detection. MCN predicts instance-level bounding boxes by firstly converting an im…
A GPU-Outperforming FPGA Accelerator Architecture for Binary Convolutional Neural Networks
Yixing Li, Zichuan Liu, Kai Xu +2
FPGA-based hardware accelerators for convolutional neural networks (CNNs) have obtained great attentions due to their higher energy efficiency than GPUs. However, it is challenging…
A Binary Convolutional Encoder-decoder Network for Real-time Natural Scene Text Processing
Zichuan Liu, Yixing Li, Fengbo Ren +1
In this paper, we develop a binary convolutional encoder-decoder network (B-CEDNet) for natural scene text processing (NSTP). It converts a text image to a class-distinguished sali…