Zobrazeno 1 - 10
of 434
pro vyhledávání: '"chen, Yuanhong"'
Recent advances in prototypical learning have shown remarkable potential to provide useful decision interpretations associating activation maps and predictions with class-specific training prototypes. Such prototypical learning has been well-studied
Externí odkaz:
http://arxiv.org/abs/2411.04607
The costly and time-consuming annotation process to produce large training sets for modelling semantic LiDAR segmentation methods has motivated the development of semi-supervised learning (SSL) methods. However, such SSL approaches often concentrate
Externí odkaz:
http://arxiv.org/abs/2407.07171
Audio-visual segmentation (AVS) is an emerging task that aims to accurately segment sounding objects based on audio-visual cues. The success of AVS learning systems depends on the effectiveness of cross-modal interaction. Such a requirement can be na
Externí odkaz:
http://arxiv.org/abs/2407.05358
Autor:
Wang, Hu, Hassan, Salma, Liu, Yuyuan, Ma, Congbo, Chen, Yuanhong, Xie, Yutong, Salem, Mostafa, Tian, Yu, Avery, Jodie, Hull, Louise, Reid, Ian, Yaqub, Mohammad, Carneiro, Gustavo
In multi-modal learning, some modalities are more influential than others, and their absence can have a significant impact on classification/segmentation accuracy. Addressing this challenge, we propose a novel approach called Meta-learned Modality-we
Externí odkaz:
http://arxiv.org/abs/2405.07155
Autor:
Wang, Chong, Chen, Yuanhong, Liu, Fengbei, Liu, Yuyuan, McCarthy, Davis James, Frazer, Helen, Carneiro, Gustavo
Prototypical-part methods, e.g., ProtoPNet, enhance interpretability in image recognition by linking predictions to training prototypes, thereby offering intuitive insights into their decision-making. Existing methods, which rely on a point-based lea
Externí odkaz:
http://arxiv.org/abs/2312.00092
Publikováno v:
Medical Image Computing and Computer-Assisted Intervention 2023 (MICCAI 2023)
The problem of missing modalities is both critical and non-trivial to be handled in multi-modal models. It is common for multi-modal tasks that certain modalities contribute more compared to other modalities, and if those important modalities are mis
Externí odkaz:
http://arxiv.org/abs/2310.01035
Noisy label learning has been tackled with both discriminative and generative approaches. Despite the simplicity and efficiency of discriminative methods, generative models offer a more principled way of disentangling clean and noisy labels and estim
Externí odkaz:
http://arxiv.org/abs/2308.01184
Autor:
Cheng, Xinquan, Chen, Yuanhong, Wang, Pingfan, Zhou, YanXi, Wei, Xiaojing, Luo, Wenjiang, Duan, Qingxin
Publikováno v:
Journal of Hospitality and Tourism Technology, 2024, Vol. 15, Issue 4, pp. 592-609.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/JHTT-06-2023-0170
Publikováno v:
CVPR2023
The missing modality issue is critical but non-trivial to be solved by multi-modal models. Current methods aiming to handle the missing modality problem in multi-modal tasks, either deal with missing modalities only during evaluation or train separat
Externí odkaz:
http://arxiv.org/abs/2307.14126
Autor:
Chen, Yuanhong, Liu, Yuyuan, Wang, Hu, Liu, Fengbei, Wang, Chong, Frazer, Helen, Carneiro, Gustavo
Audio-visual segmentation (AVS) is a challenging task that involves accurately segmenting sounding objects based on audio-visual cues. The effectiveness of audio-visual learning critically depends on achieving accurate cross-modal alignment between s
Externí odkaz:
http://arxiv.org/abs/2304.02970