1 code implementation • 21 Mar 2024 • Qihan Huang, Jie Song, Jingwen Hu, Haofei Zhang, Yong Wang, Mingli Song
Concept Bottleneck Models (CBMs), which break down the reasoning process into the input-to-concept mapping and the concept-to-label prediction, have garnered significant attention due to their remarkable interpretability achieved by the interpretable concept bottleneck.
no code implementations • 4 Feb 2024 • Yuxin Wang, Zunlei Feng, Haofei Zhang, Yang Gao, Jie Lei, Li Sun, Mingli Song
Due to the inability to receive signals from the Global Navigation Satellite System (GNSS) in extreme conditions, achieving accurate and robust navigation for Unmanned Aerial Vehicles (UAVs) is a challenging task.
no code implementations • 3 Jun 2023 • Wenda Li, KaiXuan Chen, Shunyu Liu, Wenjie Huang, Haofei Zhang, Yingjie Tian, Yun Su, Mingli Song
In this paper, we strive to develop an interpretable GNNs' inference paradigm, termed MSInterpreter, which can serve as a plug-and-play scheme readily applicable to various GNNs' baselines.
1 code implementation • 31 May 2023 • KaiXuan Chen, Shunyu Liu, Tongtian Zhu, Tongya Zheng, Haofei Zhang, Zunlei Feng, Jingwen Ye, Mingli Song
Graph Neural Networks (GNNs) have emerged as a powerful category of learning architecture for handling graph-structured data.
1 code implementation • CVPR 2023 • Tianli Zhang, Mengqi Xue, Jiangtao Zhang, Haofei Zhang, Yu Wang, Lechao Cheng, Jie Song, Mingli Song
Most existing online knowledge distillation(OKD) techniques typically require sophisticated modules to produce diverse knowledge for improving students' generalization ability.
1 code implementation • 12 Mar 2023 • Haofei Zhang, Mengqi Xue, Xiaokang Liu, KaiXuan Chen, Jie Song, Mingli Song
In this paper, we study a novel inference paradigm, termed as schema inference, that learns to deductively infer the explainable predictions by rebuilding the prior deep neural network (DNN) forwarding scheme, guided by the prevalent philosophical cognitive concept of schema.
1 code implementation • ICCV 2023 • Qihan Huang, Mengqi Xue, Wenqi Huang, Haofei Zhang, Jie Song, Yongcheng Jing, Mingli Song
Part-prototype networks (e. g., ProtoPNet, ProtoTree, and ProtoPool) have attracted broad research interest for their intrinsic interpretability and comparable accuracy to non-interpretable counterparts.
1 code implementation • 7 Sep 2022 • Haoling Li, Jie Song, Mengqi Xue, Haofei Zhang, Jingwen Ye, Lechao Cheng, Mingli Song
This survey aims to present a comprehensive review of NTs and attempts to identify how they enhance the model interpretability.
1 code implementation • 22 Aug 2022 • Mengqi Xue, Qihan Huang, Haofei Zhang, Lechao Cheng, Jie Song, Minghui Wu, Mingli Song
The global prototypes are adopted to provide the global view of objects to guide local prototypes to concentrate on the foreground while eliminating the influence of the background.
1 code implementation • CVPR 2022 • Mengqi Xue, Haofei Zhang, Jie Song, Mingli Song
Continual learning is a longstanding research topic due to its crucial role in tackling continually arriving tasks.
1 code implementation • 7 Mar 2022 • Haofei Zhang, Feng Mao, Mengqi Xue, Gongfan Fang, Zunlei Feng, Jie Song, Mingli Song
Moreover, the transformer-based students excel in learning amalgamated knowledge, as they have mastered heterogeneous detection tasks rapidly and achieved superior or at least comparable performance to those of the teachers in their specializations.
2 code implementations • 12 Dec 2021 • Gongfan Fang, Kanya Mo, Xinchao Wang, Jie Song, Shitao Bei, Haofei Zhang, Mingli Song
At the heart of our approach is a novel strategy to reuse the shared common features in training data so as to synthesize different data instances.
1 code implementation • CVPR 2022 • Haofei Zhang, Jiarui Duan, Mengqi Xue, Jie Song, Li Sun, Mingli Song
Recently, vision Transformers (ViTs) are developing rapidly and starting to challenge the domination of convolutional neural networks (CNNs) in the realm of computer vision (CV).
no code implementations • 6 Dec 2021 • Qihan Huang, Haofei Zhang, Mengqi Xue, Jie Song, Mingli Song
Although few-shot learning and zero-shot learning have been extensively explored in the field of image classification, it is indispensable to design new methods for object detection in the data-scarce scenario since object detection has an additional challenging localization task.
no code implementations • CVPR 2021 • Jie Song, Haofei Zhang, Xinchao Wang, Mengqi Xue, Ying Chen, Li Sun, DaCheng Tao, Mingli Song
Knowledge distillation pursues a diminutive yet well-behaved student network by harnessing the knowledge learned by a cumbersome teacher model.
no code implementations • 10 Jul 2020 • Gongfan Fang, Xinchao Wang, Haofei Zhang, Jie Song, Mingli Song
This network is referred to as the {\emph{Template Network}} because its filters will be used as templates to reconstruct images from the impression.