no code implementations • ECCV 2020 • Jiayan Qiu, Yiding Yang, Xinchao Wang, DaCheng Tao
This seemingly minor difference in fact makes the HVITA a much challenging task, as the restoration algorithm would have to not only infer the category of the object in total absentia, but also hallucinate an object of which the appearance is consistent with the background.
no code implementations • 27 Feb 2024 • Mo Zhou, Yiding Yang, Haoxiang Li, Vishal M. Patel, Gang Hua
With a strong alignment between the training and test distributions, object relation as a context prior facilitates object detection.
1 code implementation • 28 Nov 2023 • Jiaxin Lu, Hao Kang, Haoxiang Li, Bo Liu, Yiding Yang, QiXing Huang, Gang Hua
Generation-based methods that generate grasping postures conditioned on the object can often produce diverse grasping, but they are insufficient for high grasping success due to lack of discriminative information.
no code implementations • CVPR 2023 • Yongcheng Jing, Chongbin Yuan, Li Ju, Yiding Yang, Xinchao Wang, DaCheng Tao
In this paper, we explore a novel model reusing task tailored for graph neural networks (GNNs), termed as "deep graph reprogramming".
no code implementations • 24 Jul 2022 • Yongcheng Jing, Yining Mao, Yiding Yang, Yibing Zhan, Mingli Song, Xinchao Wang, DaCheng Tao
To this end, we develop an elaborated GNN model with content and style local patches as the graph vertices.
no code implementations • ICCV 2021 • Yongcheng Jing, Yiding Yang, Xinchao Wang, Mingli Song, DaCheng Tao
In this paper, we study a novel meta aggregation scheme towards binarizing graph neural networks (GNNs).
no code implementations • CVPR 2021 • Jiayan Qiu, Yiding Yang, Xinchao Wang, DaCheng Tao
What scene elements, if any, are indispensable for recognizing a scene?
1 code implementation • CVPR 2021 • Yongcheng Jing, Yiding Yang, Xinchao Wang, Mingli Song, DaCheng Tao
In this paper, we study a novel knowledge transfer task in the domain of graph neural networks (GNNs).
no code implementations • CVPR 2021 • Yongcheng Jing, Yiding Yang, Xinchao Wang, Mingli Song, DaCheng Tao
To this end, we propose an Event-based VSR framework (E-VSR), of which the key component is an asynchronous interpolation (EAI) module that reconstructs a high-frequency (HF) video stream with uniform and tiny pixel displacements between neighboring frames from an event stream.
no code implementations • CVPR 2021 • Yiding Yang, Zhou Ren, Haoxiang Li, Chunluan Zhou, Xinchao Wang, Gang Hua
In this paper, we propose a novel online approach to learning the pose dynamics, which are independent of pose detections in current fame, and hence may serve as a robust estimation even in challenging scenarios including occlusion.
Multi-Person Pose Estimation Multi-Person Pose Estimation and Tracking +1
1 code implementation • CVPR 2020 • Zhixiang Min, Yiding Yang, Enrique Dunn
We propose a dense indirect visual odometry method taking as input externally estimated optical flow fields instead of hand-crafted feature correspondences.
1 code implementation • 10 Jan 2021 • Yiding Yang, Xinchao Wang, Mingli Song, Junsong Yuan, DaCheng Tao
SPAGAN therefore allows for a more informative and intact exploration of the graph structure and further {a} more effective aggregation of information from distant neighbors into the center node, as compared to node-based GCN methods.
1 code implementation • 10 Dec 2020 • Huihui Liu, Yiding Yang, Xinchao Wang
Catastrophic forgetting refers to the tendency that a neural network "forgets" the previous learned knowledge upon learning new tasks.
no code implementations • ECCV 2020 • Yiding Yang, Jiayan Qiu, Mingli Song, DaCheng Tao, Xinchao Wang
Prior gradient-based attribution-map methods rely on handcrafted propagation rules for the non-linear/activation layers during the backward pass, so as to produce gradients of the input and then the attribution map.
1 code implementation • NeurIPS 2020 • Yiding Yang, Zunlei Feng, Mingli Song, Xinchao Wang
In this paper, we introduce a novel graph convolutional network (GCN), termed as factorizable graph convolutional network(FactorGCN), that explicitly disentangles such intertwined relations encoded in a graph.
Ranked #3 on Node Classification on PATTERN 100k
1 code implementation • CVPR 2020 • Yiding Yang, Jiayan Qiu, Mingli Song, DaCheng Tao, Xinchao Wang
To enable the knowledge transfer from the teacher GCN to the student, we propose a local structure preserving module that explicitly accounts for the topological semantics of the teacher.
no code implementations • 12 Nov 2016 • Fuqaing Liu, Chenwei Deng, Fukun Bi, Yiding Yang
Semi-supervised wrapper methods are concerned with building effective supervised classifiers from partially labeled data.
no code implementations • 18 Feb 2016 • Fuqiang Liu, Fukun Bi, Yiding Yang, Liang Chen
It is theoretically proved that Boost Picking could train a supervised model mainly by un-labeled data as effectively as the same model trained by 100% labeled data, only if recalls of the two weak classifiers are all greater than zero and the sum of precisions is greater than one.