1 code implementation • 14 Mar 2024 • Yuhang Zheng, Xiangyu Chen, Yupeng Zheng, Songen Gu, Runyi Yang, Bu Jin, Pengfei Li, Chengliang Zhong, Zengmao Wang, Lina Liu, Chao Yang, Dawei Wang, Zhen Chen, Xiaoxiao Long, Meiqing Wang
In particular, we propose an Efficient Feature Distillation (EFD) module that employs contrastive learning to efficiently and accurately distill language embeddings derived from foundational models.
no code implementations • 14 Jan 2024 • Junyu Zhu, Lina Liu, Bofeng Jiang, Feng Wen, Hongbo Zhang, Wanlong Li, Yong liu
In this paper, to lower the annotation cost, we propose a self-supervised event-based monocular depth estimation framework named EMoDepth.
1 code implementation • 28 Aug 2023 • Junyu Zhu, Lina Liu, Yu Tang, Feng Wen, Wanlong Li, Yong liu
In this paper, we present a novel semi-supervised framework for visual BEV semantic segmentation to boost performance by exploiting unlabeled images during the training.
Autonomous Vehicles Bird's-Eye View Semantic Segmentation +2
no code implementations • 8 Aug 2023 • Chen Wang, Jiadai Sun, Lina Liu, Chenming Wu, Zhelun Shen, Dayan Wu, Yuchao Dai, Liangjun Zhang
However, the shape-radiance ambiguity of radiance fields remains a challenge, especially in the sparse viewpoints setting.
no code implementations • 20 Jan 2023 • Junyu Zhu, Lina Liu, Yong liu, Wanlong Li, Feng Wen, Hongbo Zhang
The great potential of unsupervised monocular depth estimation has been demonstrated by many works due to low annotation cost and impressive accuracy comparable to supervised methods.
2 code implementations • ICCV 2021 • Lina Liu, Xibin Song, Mengmeng Wang, Yong liu, Liangjun Zhang
Meanwhile, to guarantee that the day and night images contain the same information, the domain-separated network takes the day-time images and corresponding night-time images (generated by GAN) as input, and the private and invariant feature extractors are learned by orthogonality and similarity loss, where the domain gap can be alleviated, thus better depth maps can be expected.
no code implementations • 15 Dec 2020 • Lina Liu, Xibin Song, Xiaoyang Lyu, Junwei Diao, Mengmeng Wang, Yong liu, Liangjun Zhang
Then, a refined depth map is further obtained using a residual learning strategy in the coarse-to-fine stage with a coarse depth map and color image as input.
1 code implementation • 14 Dec 2020 • Xiaoyang Lyu, Liang Liu, Mengmeng Wang, Xin Kong, Lina Liu, Yong liu, Xinxin Chen, Yi Yuan
To obtainmore accurate depth estimation in large gradient regions, itis necessary to obtain high-resolution features with spatialand semantic information.
Ranked #7 on Unsupervised Monocular Depth Estimation on KITTI-C