28 citations · 74 across the 5 of their papers we have counts for
6 papers · 1 filter
Knowledge Amalgamation for Object Detection with Transformers
Haofei Zhang, Feng Mao, Mengqi Xue +4
Knowledge amalgamation (KA) is a novel deep model reusing task aiming to transfer knowledge from several well-trained teachers to a multi-talented and compact student. Currently, m…
Personalized Image Semantic Segmentation
Yu Zhang, Chang-Bin Zhang, Peng-Tao Jiang +2
Semantic segmentation models trained on public datasets have achieved great success in recent years. However, these models didn't consider the personalization issue of segmentation…
DEPARA: Deep Attribution Graph for Deep Knowledge Transferability
Jie Song, Yixin Chen, Jingwen Ye +4
Exploring the intrinsic interconnections between the knowledge encoded in PRe-trained Deep Neural Networks (PR-DNNs) of heterogeneous tasks sheds light on their mutual transferabil…
Semantic Regularization: Improve Few-shot Image Classification by Reducing Meta Shift
Da Chen, Yongliang Yang, Zunlei Feng +6
Few-shot image classification requires the classifier to robustly cope with unseen classes even if there are only a few samples for each class. Recent advances benefit from the met…
Self-Supervised Learning For Few-Shot Image Classification
Da Chen, Yuefeng Chen, Yuhong Li +3
Few-shot image classification aims to classify unseen classes with limited labelled samples. Recent works benefit from the meta-learning process with episodic tasks and can fast ad…
Hierarchical Video Frame Sequence Representation with Deep Convolutional Graph Network
Feng Mao, Xiang Wu, Hui Xue +1
High accuracy video label prediction (classification) models are attributed to large scale data. These data could be frame feature sequences extracted by a pre-trained convolutiona…