no code implementations • 6 May 2024 • Yunxiao Shi, Xing Zi, Zijing Shi, Haimin Zhang, Qiang Wu, Min Xu
The efficiency and personalization characteristics of ERAGent are supported by the Experiential Learner module which makes the AI assistant being capable of expanding its knowledge and modeling user profile incrementally.
no code implementations • 11 Apr 2024 • Zeng Yu, Yunxiao Shi
Importantly, in the alignment process of SAS and AAL, all the parameters are immediately optimized with optimization principles rather than training the whole network, which yields a better parameter training manner.
no code implementations • 19 Mar 2024 • Rajeev Yasarla, Manish Kumar Singh, Hong Cai, Yunxiao Shi, Jisoo Jeong, Yinhao Zhu, Shizhong Han, Risheek Garrepalli, Fatih Porikli
In this paper, we propose a novel video depth estimation approach, FutureDepth, which enables the model to implicitly leverage multi-frame and motion cues to improve depth estimation by making it learn to predict the future at training.
Ranked #2 on Monocular Depth Estimation on KITTI Eigen split
no code implementations • 18 Mar 2024 • Yunxiao Shi, Manish Kumar Singh, Hong Cai, Fatih Porikli
Leveraging the initial depths and features from this network, we uplift the 2D features to form a 3D point cloud and construct a 3D point transformer to process it, allowing the model to explicitly learn and exploit 3D geometric features.
no code implementations • 6 Mar 2024 • Li Wang, Min Xu, Quangui Zhang, Yunxiao Shi, Qiang Wu
Building upon this insight, we propose a disentangled encoder that focuses on disentangling user and item embeddings into interest and social influence embeddings.
no code implementations • IEEE/CVF International Conference on Computer Vision (ICCV) 2023 • Rajeev Yasarla, Hong Cai, Jisoo Jeong, Yunxiao Shi, Risheek Garrepalli, Fatih Porikli
We propose MAMo, a novel memory and attention frame-work for monocular video depth estimation.
Ranked #12 on Monocular Depth Estimation on KITTI Eigen split
no code implementations • 6 Apr 2023 • Yunxiao Shi, Hong Cai, Amin Ansari, Fatih Porikli
the number of views and frames.
no code implementations • 28 Sep 2019 • Yunxiao Shi, Jing Zhu, Yi Fang, Kuochin Lien, Junli Gu
Learning to predict scene depth and camera motion from RGB inputs only is a challenging task.
no code implementations • 10 Sep 2019 • Jing Zhu, Yunxiao Shi, Mengwei Ren, Yi Fang, Kuo-Chin Lien, Junli Gu
To this end, we introduce a new Structure-Oriented Memory (SOM) module to learn and memorize the structure-specific information between RGB image domain and the depth domain.
Ranked #48 on Monocular Depth Estimation on KITTI Eigen split