52 citations · 176 across the 14 of their papers we have counts for
14 papers
Fast Disentangled Slim Tensor Learning for Multi-view Clustering
Deng Xu, Chao Zhang, Zechao Li +2
Tensor-based multi-view clustering has recently received significant attention due to its exceptional ability to explore cross-view high-order correlations. However, most existing…
Attention-Guided Contrastive Role Representations for Multi-Agent Reinforcement Learning
Zican Hu, Zongzhang Zhang, Huaxiong Li +3
Real-world multi-agent tasks usually involve dynamic team composition with the emergence of roles, which should also be a key to efficient cooperation in multi-agent reinforcement…
Joint Projection Learning and Tensor Decomposition Based Incomplete Multi-view Clustering
Wei Lv, Chao Zhang, Huaxiong Li +2
Incomplete multi-view clustering (IMVC) has received increasing attention since it is often that some views of samples are incomplete in reality. Most existing methods learn simila…
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…
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…
Adaptive Marginalized Semantic Hashing for Unpaired Cross-Modal Retrieval
Kaiyi Luo, Chao Zhang, Huaxiong Li +2
In recent years, Cross-Modal Hashing (CMH) has aroused much attention due to its fast query speed and efficient storage. Previous literatures have achieved promising results for Cr…
Model-Aware Contrastive Learning: Towards Escaping the Dilemmas
Zizheng Huang, Haoxing Chen, Ziqi Wen +4
Contrastive learning (CL) continuously achieves significant breakthroughs across multiple domains. However, the most common InfoNCE-based methods suffer from some dilemmas, such as…
Shaping Visual Representations with Attributes for Few-Shot Recognition
Haoxing Chen, Huaxiong Li, Yaohui Li +1
Few-shot recognition aims to recognize novel categories under low-data regimes. Some recent few-shot recognition methods introduce auxiliary semantic modality, i.e., category attri…
Sparse Spatial Transformers for Few-Shot Learning
Haoxing Chen, Huaxiong Li, Yaohui Li +1
Learning from limited data is challenging because data scarcity leads to a poor generalization of the trained model. A classical global pooled representation will probably lose use…
Perspective-corrected Spatial Referring Expression Generation for Human-Robot Interaction
Mingjiang Liu, Chengli Xiao, Chunlin Chen
Intelligent robots designed to interact with humans in real scenarios need to be able to refer to entities actively by natural language. In spatial referring expression generation,…
Hierarchical Representation based Query-Specific Prototypical Network for Few-Shot Image Classification
Yaohui Li, Huaxiong Li, Haoxing Chen +1
Few-shot image classification aims at recognizing unseen categories with a small number of labeled training data. Recent metric-based frameworks tend to represent a support class b…
Multi-level Metric Learning for Few-shot Image Recognition
Haoxing Chen, Huaxiong Li, Yaohui Li +1
Few-shot learning is devoted to training a model on few samples. Most of these approaches learn a model based on a pixel-level or global-level feature representation. However, usin…
Multi-scale Adaptive Task Attention Network for Few-Shot Learning
Haoxing Chen, Huaxiong Li, Yaohui Li +1
The goal of few-shot learning is to classify unseen categories with few labeled samples. Recently, the low-level information metric-learning based methods have achieved satisfying…