11 citations · 39 across the 9 of their papers we have counts for
10 papers
Multi-task Learning with 3D-Aware Regularization
Wei-Hong Li, Steven McDonagh, Ales Leonardis +1
Deep neural networks have become a standard building block for designing models that can perform multiple dense computer vision tasks such as depth estimation and semantic segmenta…
PointCaM: Cut-and-Mix for Open-Set Point Cloud Learning
Jie Hong, Shi Qiu, Weihao Li +4
Point cloud learning is receiving increasing attention. However, most existing point cloud models lack the practical ability to deal with the unavoidable presence of unknown object…
GOSS: Towards Generalized Open-set Semantic Segmentation
Jie Hong, Weihao Li, Junlin Han +4
In this paper, we present and study a new image segmentation task, called Generalized Open-set Semantic Segmentation (GOSS). Previously, with the well-known open-set semantic segme…
You Only Cut Once: Boosting Data Augmentation with a Single Cut
Junlin Han, Pengfei Fang, Weihao Li +5
We present You Only Cut Once (YOCO) for performing data augmentations. YOCO cuts one image into two pieces and performs data augmentations individually within each piece. Applying…
Learning Multiple Dense Prediction Tasks from Partially Annotated Data
Wei-Hong Li, Xialei Liu, Hakan Bilen
Despite the recent advances in multi-task learning of dense prediction problems, most methods rely on expensive labelled datasets. In this paper, we present a label efficient appro…
Cross-domain Few-shot Learning with Task-specific Adapters
Wei-Hong Li, Xialei Liu, Hakan Bilen
In this paper, we look at the problem of cross-domain few-shot classification that aims to learn a classifier from previously unseen classes and domains with few labeled samples. R…
Universal Representation Learning from Multiple Domains for Few-shot Classification
Wei-Hong Li, Xialei Liu, Hakan Bilen
In this paper, we look at the problem of few-shot classification that aims to learn a classifier for previously unseen classes and domains from few labeled samples. Recent methods…
MINI-Net: Multiple Instance Ranking Network for Video Highlight Detection
Fa-Ting Hong, Xuanteng Huang, Wei-Hong Li +1
We address the weakly supervised video highlight detection problem for learning to detect segments that are more attractive in training videos given their video event label but wit…
Knowledge Distillation for Multi-task Learning
Wei-Hong Li, Hakan Bilen
Multi-task learning (MTL) is to learn one single model that performs multiple tasks for achieving good performance on all tasks and lower cost on computation. Learning such a model…
Towards Photo-Realistic Visible Watermark Removal with Conditional Generative Adversarial Networks
Xiang Li, Chan Lu, Danni Cheng +5
Visible watermark plays an important role in image copyright protection and the robustness of a visible watermark to an attack is shown to be essential. To evaluate and improve the…