1 code implementation • 2 Oct 2023 • Wei-Hong Li, Steven McDonagh, Ales Leonardis, Hakan Bilen
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 segmentation thanks to their ability to capture complex correlations in high dimensional feature space across tasks.
2 code implementations • 6 Apr 2022 • Wei-Hong Li, Xialei Liu, Hakan Bilen
We propose a unified look at jointly learning multiple vision tasks and visual domains through universal representations, a single deep neural network.
1 code implementation • CVPR 2022 • 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.
4 code implementations • CVPR 2022 • 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.
Ranked #2 on Few-Shot Image Classification on Meta-Dataset
cross-domain few-shot learning Few-Shot Image Classification
3 code implementations • ICCV 2021 • 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.
Ranked #4 on Few-Shot Image Classification on Meta-Dataset
no code implementations • ECCV 2020 • Fa-Ting Hong, Xuanteng Huang, Wei-Hong Li, Wei-Shi Zheng
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 without expensive supervision of manually annotating highlight segments.
4 code implementations • 14 Jul 2020 • Wei-Hong Li, Hakan Bilen
We then learn the multi-task model for minimizing task-specific loss and for producing the same feature with task-specific models.
1 code implementation • CVPR 2020 • Fa-Ting Hong, Wei-Hong Li, Wei-Shi Zheng
Important people detection is to automatically detect the individuals who play the most important roles in a social event image, which requires the designed model to understand a high-level pattern.
2 code implementations • 22 Dec 2019 • Wei-Hong Li, Chuan-Sheng Foo, Hakan Bilen
Recent semi-supervised learning methods have shown to achieve comparable results to their supervised counterparts while using only a small portion of labels in image classification tasks thanks to their regularization strategies.
no code implementations • 30 May 2019 • Xiang Li, Chan Lu, Danni Cheng, Wei-Hong Li, Mei Cao, Bo Liu, Jiechao Ma, Wei-Shi Zheng
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.
1 code implementation • CVPR 2019 • Wei-Hong Li, Fa-Ting Hong, Wei-Shi Zheng
In this work, we propose a deep imPOrtance relatIon NeTwork (POINT) that combines both relation modeling and feature learning.
no code implementations • 9 Nov 2017 • Wei-Hong Li, Zhuowei Zhong, Wei-Shi Zheng
While there is a few work on discussing online re-id, most of them require considerable storage of all passed data samples that have been ever observed, and this could be unrealistic for processing data from a large camera network.
no code implementations • 6 Nov 2017 • Wei-Hong Li, Benchao Li, Wei-Shi Zheng
Always, some individuals in images are more important/attractive than others in some events such as presentation, basketball game or speech.