53 citations · 113 across the 14 of their papers we have counts for
7 papers · 1 filter
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…
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…
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…
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 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)…