137 citations · 412 across the 61 of their papers we have counts for
20 papers · 1 filter
Multi-Agent Reinforcement Learning with Shared Resources for Inventory Management
Yuandong Ding, Mingxiao Feng, Guozi Liu +7
In this paper, we consider the inventory management (IM) problem where we need to make replenishment decisions for a large number of stock keeping units (SKUs) to balance their sup…
Hand-Object Interaction Image Generation
Hezhen Hu, Weilun Wang, Wengang Zhou +1
In this work, we are dedicated to a new task, i.e., hand-object interaction image generation, which aims to conditionally generate the hand-object image under the given hand, objec…
CLIP2GAN: Towards Bridging Text with the Latent Space of GANs
Yixuan Wang, Wengang Zhou, Jianmin Bao +3
In this work, we are dedicated to text-guided image generation and propose a novel framework, i.e., CLIP2GAN, by leveraging CLIP model and StyleGAN. The key idea of our CLIP2GAN is…
SinDiffusion: Learning a Diffusion Model from a Single Natural Image
Weilun Wang, Jianmin Bao, Wengang Zhou +4
We present SinDiffusion, leveraging denoising diffusion models to capture internal distribution of patches from a single natural image. SinDiffusion significantly improves the qual…
DanZero: Mastering GuanDan Game with Reinforcement Learning
Yudong Lu, Jian Zhao, Youpeng Zhao +2
Card game AI has always been a hot topic in the research of artificial intelligence. In recent years, complex card games such as Mahjong, DouDizhu and Texas Hold'em have been solve…
Geometric Representation Learning for Document Image Rectification
Hao Feng, Wengang Zhou, Jiajun Deng +2
In document image rectification, there exist rich geometric constraints between the distorted image and the ground truth one. However, such geometric constraints are largely ignore…
CMD: Self-supervised 3D Action Representation Learning with Cross-modal Mutual Distillation
Yunyao Mao, Wengang Zhou, Zhenbo Lu +2
In 3D action recognition, there exists rich complementary information between skeleton modalities. Nevertheless, how to model and utilize this information remains a challenging pro…
Unified 2D and 3D Pre-Training of Molecular Representations
Jinhua Zhu, Yingce Xia, Lijun Wu +5
Molecular representation learning has attracted much attention recently. A molecule can be viewed as a 2D graph with nodes/atoms connected by edges/bonds, and can also be represent…
Self-Adaptive Label Augmentation for Semi-supervised Few-shot Classification
Xueliang Wang, Jianyu Cai, Shuiwang Ji +3
Few-shot classification aims to learn a model that can generalize well to new tasks when only a few labeled samples are available. To make use of unlabeled data that are more abund…
TransVG++: End-to-End Visual Grounding with Language Conditioned Vision Transformer
Jiajun Deng, Zhengyuan Yang, Daqing Liu +5
In this work, we explore neat yet effective Transformer-based frameworks for visual grounding. The previous methods generally address the core problem of visual grounding, i.e., mu…
Stabilizing Voltage in Power Distribution Networks via Multi-Agent Reinforcement Learning with Transformer
Minrui Wang, Mingxiao Feng, Wengang Zhou +1
The increased integration of renewable energy poses a slew of technical challenges for the operation of power distribution networks. Among them, voltage fluctuations caused by the…
A Self-Paced Mixed Distillation Method for Non-Autoregressive Generation
Weizhen Qi, Yeyun Gong, Yelong Shen +6
Non-Autoregressive generation is a sequence generation paradigm, which removes the dependency between target tokens. It could efficiently reduce the text generation latency with pa…
Simultaneous Double Q-learning with Conservative Advantage Learning for Actor-Critic Methods
Qing Li, Wengang Zhou, Zhenbo Lu +1
Actor-critic Reinforcement Learning (RL) algorithms have achieved impressive performance in continuous control tasks. However, they still suffer two nontrivial obstacles, i.e., low…
LDSA: Learning Dynamic Subtask Assignment in Cooperative Multi-Agent Reinforcement Learning
Mingyu Yang, Jian Zhao, Xunhan Hu +3
Cooperative multi-agent reinforcement learning (MARL) has made prominent progress in recent years. For training efficiency and scalability, most of the MARL algorithms make all age…
Domain-Agnostic Prior for Transfer Semantic Segmentation
Xinyue Huo, Lingxi Xie, Hengtong Hu +3
Unsupervised domain adaptation (UDA) is an important topic in the computer vision community. The key difficulty lies in defining a common property between the source and target dom…
DouZero+: Improving DouDizhu AI by Opponent Modeling and Coach-guided Learning
Youpeng Zhao, Jian Zhao, Xunhan Hu +2
Recent years have witnessed the great breakthrough of deep reinforcement learning (DRL) in various perfect and imperfect information games. Among these games, DouDizhu, a popular c…
Large-Scale Pre-training for Person Re-identification with Noisy Labels
Dengpan Fu, Dongdong Chen, Hao Yang +6
This paper aims to address the problem of pre-training for person re-identification (Re-ID) with noisy labels. To setup the pre-training task, we apply a simple online multi-object…
Coach-assisted Multi-Agent Reinforcement Learning Framework for Unexpected Crashed Agents
Jian Zhao, Youpeng Zhao, Weixun Wang +5
Multi-agent reinforcement learning is difficult to be applied in practice, which is partially due to the gap between the simulated and real-world scenarios. One reason for the gap…
CTDS: Centralized Teacher with Decentralized Student for Multi-Agent Reinforcement Learning
Jian Zhao, Xunhan Hu, Mingyu Yang +3
Due to the partial observability and communication constraints in many multi-agent reinforcement learning (MARL) tasks, centralized training with decentralized execution (CTDE) has…
MCMARL: Parameterizing Value Function via Mixture of Categorical Distributions for Multi-Agent Reinforcement Learning
Jian Zhao, Mingyu Yang, Youpeng Zhao +4
In cooperative multi-agent tasks, a team of agents jointly interact with an environment by taking actions, receiving a team reward and observing the next state. During the interact…