no code implementations • ICCV 2023 • Na Dong, Yongqiang Zhang, Mingli Ding, Gim Hee Lee
Real-world data tends to follow a long-tailed distribution, where the class imbalance results in dominance of the head classes during training.
no code implementations • 6 Dec 2022 • Na Dong, Yongqiang Zhang, Mingli Ding, Gim Hee Lee
Open world object detection aims at detecting objects that are absent in the object classes of the training data as unknown objects without explicit supervision.
no code implementations • 9 May 2022 • Na Dong, Yongqiang Zhang, Mingli Ding, Gim Hee Lee
Incremental few-shot object detection aims at detecting novel classes without forgetting knowledge of the base classes with only a few labeled training data from the novel classes.
1 code implementation • 26 Mar 2022 • Guanglei Yang, Enrico Fini, Dan Xu, Paolo Rota, Mingli Ding, Moin Nabi, Xavier Alameda-Pineda, Elisa Ricci
This problem has been widely investigated in the research community and several Incremental Learning (IL) approaches have been proposed in the past years.
1 code implementation • 1 Feb 2022 • Guanglei Yang, Enrico Fini, Dan Xu, Paolo Rota, Mingli Ding, Hao Tang, Xavier Alameda-Pineda, Elisa Ricci
To fill this gap, in this paper we introduce a novel attentive feature distillation approach to mitigate catastrophic forgetting while accounting for semantic spatial- and channel-level dependencies.
1 code implementation • 19 Nov 2021 • Guanglei Yang, Hao Tang, Humphrey Shi, Mingli Ding, Nicu Sebe, Radu Timofte, Luc van Gool, Elisa Ricci
The global alignment network aims to transfer the input image from the source domain to the target domain.
1 code implementation • 19 Nov 2021 • Guanglei Yang, Zhun Zhong, Hao Tang, Mingli Ding, Nicu Sebe, Elisa Ricci
Specifically, in the image translation stage, Bi-Mix leverages the knowledge of day-night image pairs to improve the quality of nighttime image relighting.
1 code implementation • NeurIPS 2021 • Na Dong, Yongqiang Zhang, Mingli Ding, Gim Hee Lee
In view of this limitation, we consider a more practical setting of complete absence of co-occurrence of the base and novel classes for the object detection task.
1 code implementation • 28 May 2021 • Guanglei Yang, Hao Tang, Zhun Zhong, Mingli Ding, Ling Shao, Nicu Sebe, Elisa Ricci
In this paper, we study the task of source-free domain adaptation (SFDA), where the source data are not available during target adaptation.
1 code implementation • ICCV 2021 • Guanglei Yang, Hao Tang, Mingli Ding, Nicu Sebe, Elisa Ricci
While convolutional neural networks have shown a tremendous impact on various computer vision tasks, they generally demonstrate limitations in explicitly modeling long-range dependencies due to the intrinsic locality of the convolution operation.
Ranked #8 on Depth Estimation on NYU-Depth V2
1 code implementation • 5 Mar 2021 • Guanglei Yang, Paolo Rota, Xavier Alameda-Pineda, Dan Xu, Mingli Ding, Elisa Ricci
Specifically, we integrate the estimation and the interaction of the attentions within a probabilistic representation learning framework, leading to Variational STructured Attention networks (VISTA-Net).
1 code implementation • 1 Jan 2021 • Guanglei Yang, Paolo Rota, Xavier Alameda-Pineda, Dan Xu, Mingli Ding, Elisa Ricci
State-of-the-art performances in dense pixel-wise prediction tasks are obtained with specifically designed convolutional networks.
no code implementations • 12 Feb 2020 • Guanglei Yang, Haifeng Xia, Mingli Ding, Zhengming Ding
To balance the mitigation of domain gap and the preservation of the inherent structure, we propose a Bi-Directional Generation domain adaptation model with consistent classifiers interpolating two intermediate domains to bridge source and target domains.
no code implementations • ECCV 2018 • Yancheng Bai, Yongqiang Zhang, Mingli Ding, Bernard Ghanem
In the MTGAN, the generator is a super-resolution network, which can up-sample small blurred images into fine-scale ones and recover detailed information for more accurate detection.
no code implementations • CVPR 2018 • Yongqiang Zhang, Yancheng Bai, Mingli Ding, Yongqiang Li, Bernard Ghanem
Finally, we use these pseudo ground-truths to train a fully-supervised detector.
no code implementations • CVPR 2018 • Yancheng Bai, Yongqiang Zhang, Mingli Ding, Bernard Ghanem
In this paper, we proposed an algorithm to directly generate a clear high-resolution face from a blurry small one by adopting a generative adversarial network (GAN).