53 citations · 113 across the 14 of their papers we have counts for
16 papers
HyCoRec: Hypergraph-Enhanced Multi-Preference Learning for Alleviating Matthew Effect in Conversational Recommendation
Yongsen Zheng, Ruilin Xu, Ziliang Chen +4
The Matthew effect is a notorious issue in Recommender Systems (RSs), \emph{i.e.}, the rich get richer and the poor get poorer, wherein popular items are overexposed while less pop…
Beyond the Destination: A Novel Benchmark for Exploration-Aware Embodied Question Answering
Kaixuan Jiang, Yang Liu, Weixing Chen +5
Embodied Question Answering (EQA) is a challenging task in embodied intelligence that requires agents to dynamically explore 3D environments, actively gather visual information, an…
LAW-Diffusion: Complex Scene Generation by Diffusion with Layouts
Binbin Yang, Yi Luo, Ziliang Chen +3
Thanks to the rapid development of diffusion models, unprecedented progress has been witnessed in image synthesis. Prior works mostly rely on pre-trained linguistic models, but a t…
Open Set Domain Adaptation By Novel Class Discovery
Jingyu Zhuang, Ziliang Chen, Pengxu Wei +2
In Open Set Domain Adaptation (OSDA), large amounts of target samples are drawn from the implicit categories that never appear in the source domain. Due to the lack of their specif…
Geometry-Aware Unsupervised Domain Adaptation
You-Wei Luo, Chuan-Xian Ren, Zi-Ying Chen
Unsupervised Domain Adaptation (UDA) aims to transfer the knowledge from the labeled source domain to the unlabeled target domain in the presence of dataset shift. Most existing me…
Graph-Evolving Meta-Learning for Low-Resource Medical Dialogue Generation
Shuai Lin, Pan Zhou, Xiaodan Liang +4
Human doctors with well-structured medical knowledge can diagnose a disease merely via a few conversations with patients about symptoms. In contrast, existing knowledge-grounded di…
Towards Causality-Aware Inferring: A Sequential Discriminative Approach for Medical Diagnosis
Junfan Lin, Keze Wang, Ziliang Chen +2
Medical diagnosis assistant (MDA) aims to build an interactive diagnostic agent to sequentially inquire about symptoms for discriminating diseases. However, since the dialogue reco…
Graph Neural Reasoning May Fail in Certifying Boolean Unsatisfiability
Ziliang Chen, Zhanfu Yang
It is feasible and practically-valuable to bridge the characteristics between graph neural networks (GNNs) and logical reasoning. Despite considerable efforts and successes witness…
Meta R-CNN : Towards General Solver for Instance-level Few-shot Learning
Xiaopeng Yan, Ziliang Chen, Anni Xu +3
Resembling the rapid learning capability of human, few-shot learning empowers vision systems to understand new concepts by training with few samples. Leading approaches derived fro…
Multivariate-Information Adversarial Ensemble for Scalable Joint Distribution Matching
Ziliang Chen, Zhanfu Yang, Xiaoxi Wang +4
A broad range of cross--domain generation researches boil down to matching a joint distribution by deep generative models (DGMs). Hitherto algorithms excel in pairwise domains w…
Blending-target Domain Adaptation by Adversarial Meta-Adaptation Networks
Ziliang Chen, Jingyu Zhuang, Xiaodan Liang +1
(Unsupervised) Domain Adaptation (DA) seeks for classifying target instances when solely provided with source labeled and target unlabeled examples for training. Learning domain-in…
Graph Neural Reasoning for 2-Quantified Boolean Formula Solvers
Zhanfu Yang, Fei Wang, Ziliang Chen +2
In this paper, we investigate the feasibility of learning GNN (Graph Neural Network) based solvers and GNN-based heuristics for specified QBF (Quantified Boolean Formula) problems.…
FRAME Revisited: An Interpretation View Based on Particle Evolution
Xu Cai, Yang Wu, Guanbin Li +2
FRAME (Filters, Random fields, And Maximum Entropy) is an energy-based descriptive model that synthesizes visual realism by capturing mutual patterns from structural input signals.…
Cost-effective Object Detection: Active Sample Mining with Switchable Selection Criteria
Keze Wang, Liang Lin, Xiaopeng Yan +3
Though quite challenging, leveraging large-scale unlabeled or partially labeled data in learning systems (e.g., model/classifier training) has attracted increasing attentions due t…
Deep Cocktail Network: Multi-source Unsupervised Domain Adaptation with Category Shift
Ruijia Xu, Ziliang Chen, Wangmeng Zuo +2
Unsupervised domain adaptation (UDA) conventionally assumes labeled source samples coming from a single underlying source distribution. Whereas in practical scenario, labeled data…
Deep Co-Space: Sample Mining Across Feature Transformation for Semi-Supervised Learning
Ziliang Chen, Keze Wang, Xiao Wang +3
Aiming at improving performance of visual classification in a cost-effective manner, this paper proposes an incremental semi-supervised learning paradigm called Deep Co-Space (DCS)…