133 citations · 135 across the 5 of their papers we have counts for
5 papers
Why Is Prompt Tuning for Vision-Language Models Robust to Noisy Labels?
Cheng-En Wu, Yu Tian, Haichao Yu +4
Vision-language models such as CLIP learn a generic text-image embedding from large-scale training data. A vision-language model can be adapted to a new classification task through…
Self-supervised Video Representation Learning with Cascade Positive Retrieval
Cheng-En Wu, Farley Lai, Yu Hen Hu +1
Self-supervised video representation learning has been shown to effectively improve downstream tasks such as video retrieval and action recognition. In this paper, we present the C…
Gram-SLD: Automatic Self-labeling and Detection for Instance Objects
Rui Wang, Chengtun Wu, Jiawen Xin +1
Instance object detection plays an important role in intelligent monitoring, visual navigation, human-computer interaction, intelligent services and other fields. Inspired by the g…
Compacting, Picking and Growing for Unforgetting Continual Learning
Steven C. Y. Hung, Cheng-Hao Tu, Cheng-En Wu +3
Continual lifelong learning is essential to many applications. In this paper, we propose a simple but effective approach to continual deep learning. Our approach leverages the prin…
IMMVP: An Efficient Daytime and Nighttime On-Road Object Detector
Cheng-En Wu, Yi-Ming Chan, Chien-Hung Chen +2
It is hard to detect on-road objects under various lighting conditions. To improve the quality of the classifier, three techniques are used. We define subclasses to separate daytim…