6 citations · 19 across the 10 of their papers we have counts for
10 papers · 1 filter
Class Relevance Learning For Out-of-distribution Detection
Butian Xiong, Liguang Zhou, Tin Lun Lam +1
Image classification plays a pivotal role across diverse applications, yet challenges persist when models are deployed in real-world scenarios. Notably, these models falter in dete…
Peer Learning for Unbiased Scene Graph Generation
Liguang Zhou, Junjie Hu, Yuhongze Zhou +2
Unbiased scene graph generation (USGG) is a challenging task that requires predicting diverse and heavily imbalanced predicates between objects in an image. To address this, we pro…
Lifelong-MonoDepth: Lifelong Learning for Multi-Domain Monocular Metric Depth Estimation
Junjie Hu, Chenyou Fan, Liguang Zhou +3
With the rapid advancements in autonomous driving and robot navigation, there is a growing demand for lifelong learning models capable of estimating metric (absolute) depth. Lifelo…
Attentional Graph Convolutional Network for Structure-aware Audio-Visual Scene Classification
Liguang Zhou, Yuhongze Zhou, Xiaonan Qi +3
Audio-Visual scene understanding is a challenging problem due to the unstructured spatial-temporal relations that exist in the audio signals and spatial layouts of different object…
Context-aware Mixture-of-Experts for Unbiased Scene Graph Generation
Liguang Zhou, Yuhongze Zhou, Tin Lun Lam +1
Scene graph generation (SGG) has gained tremendous progress in recent years. However, its underlying long-tailed distribution of predicate classes is a challenging problem. For ext…
Object-to-Scene: Learning to Transfer Object Knowledge to Indoor Scene Recognition
Bo Miao, Liguang Zhou, Ajmal Mian +2
Accurate perception of the surrounding scene is helpful for robots to make reasonable judgments and behaviours. Therefore, developing effective scene representation and recognition…