no code implementations • 28 Feb 2024 • Haoyu Xie, Changqi Wang, Jian Zhao, Yang Liu, Jun Dan, Chong Fu, Baigui Sun
To address this issue, we propose a robust contrastive-based S4 framework, termed the Probabilistic Representation Contrastive Learning (PRCL) framework to enhance the robustness of the unsupervised training process.
1 code implementation • 17 Oct 2023 • Xinhao Liu, Moonjun Gong, Qi Fang, Haoyu Xie, Yiming Li, Hang Zhao, Chen Feng
In this paper, we introduce a novel LiDAR perception task of Occupancy Completion and Forecasting (OCF) in the context of autonomous driving to unify these aspects into a cohesive framework.
1 code implementation • 28 Aug 2023 • Yang Liu, Cheng Yu, Lei Shang, Yongyi He, Ziheng Wu, Xingjun Wang, Chao Xu, Haoyu Xie, Weida Wang, Yuze Zhao, Lin Zhu, Chen Cheng, Weitao Chen, Yuan YAO, Wenmeng Zhou, Jiaqi Xu, Qiang Wang, Yingda Chen, Xuansong Xie, Baigui Sun
In this paper, we present FaceChain, a personalized portrait generation framework that combines a series of customized image-generation model and a rich set of face-related perceptual understanding models (\eg, face detection, deep face embedding extraction, and facial attribute recognition), to tackle aforementioned challenges and to generate truthful personalized portraits, with only a handful of portrait images as input.
1 code implementation • ICCV 2023 • Jun Dan, Yang Liu, Haoyu Xie, Jiankang Deng, Haoran Xie, Xuansong Xie, Baigui Sun
We investigate the reasons for this phenomenon and discover that the existing data augmentation approach and hard sample mining strategy are incompatible with ViTs-based FR backbone due to the lack of tailored consideration on preserving face structural information and leveraging each local token information.
no code implementations • ICCV 2023 • Changqi Wang, Haoyu Xie, Yuhui Yuan, Chong Fu, Xiangyu Yue
To improve the robustness of representations, powerful methods introduce a pixel-wise contrastive learning approach in latent space (i. e., representation space) that aggregates the representations to their prototypes in a fully supervised manner.
1 code implementation • 26 Oct 2022 • Haoyu Xie, Changqi Wang, Mingkai Zheng, Minjing Dong, Shan You, Chong Fu, Chang Xu
In prevalent pixel-wise contrastive learning solutions, the model maps pixels to deterministic representations and regularizes them in the latent space.
no code implementations • 23 Nov 2021 • Xu Zheng, Chong Fu, Haoyu Xie, Jialei Chen, Xingwei Wang, Chiu-Wing Sham
However, due to the scarcity of labeled data, the features extracted by the models are limited in supervised learning, and the quality of predictions for unlabeled data also cannot be guaranteed.
no code implementations • 1 Jan 2021 • Wenchao Zhang, Haoyu Xie, Mai Zhu, Chong Fu
RoIPool/RoIAlign is an indispensable process for the typical two-stage object detection algorithm, it is used to rescale the object proposal cropped from the feature pyramid to generate a fixed size feature map.
no code implementations • 4 Dec 2020 • Wenchao Zhang, Chong Fu, Haoyu Xie, Mai Zhu, Ming Tie, Junxin Chen
The core component of our GCA framework is a context aware mechanism, in which both global feature pyramid and attention strategies are used for feature extraction and feature refinement, respectively.