Search Results for author: Ke Cao

Found 6 papers, 5 papers with code

ID-Animator: Zero-Shot Identity-Preserving Human Video Generation

1 code implementation23 Apr 2024 Xuanhua He, Quande Liu, Shengju Qian, Xin Wang, Tao Hu, Ke Cao, Keyu Yan, Man Zhou, Jie Zhang

Based on this pipeline, a random face reference training method is further devised to precisely capture the ID-relevant embeddings from reference images, thus improving the fidelity and generalization capacity of our model for ID-specific video generation.

Attribute Video Generation

Pan-Mamba: Effective pan-sharpening with State Space Model

1 code implementation19 Feb 2024 Xuanhua He, Ke Cao, Keyu Yan, Rui Li, Chengjun Xie, Jie Zhang, Man Zhou

To the best of our knowledge, this work is the first attempt in exploring the potential of the Mamba model and establishes a new frontier in the pan-sharpening techniques.

Pansharpening

Fourier Prompt Tuning for Modality-Incomplete Scene Segmentation

1 code implementation30 Jan 2024 Ruiping Liu, Jiaming Zhang, Kunyu Peng, Yufan Chen, Ke Cao, Junwei Zheng, M. Saquib Sarfraz, Kailun Yang, Rainer Stiefelhagen

Integrating information from multiple modalities enhances the robustness of scene perception systems in autonomous vehicles, providing a more comprehensive and reliable sensory framework.

Autonomous Vehicles Scene Segmentation

Tightly-Coupled LiDAR-Visual SLAM Based on Geometric Features for Mobile Agents

no code implementations15 Jul 2023 Ke Cao, Ruiping Liu, Ze Wang, Kunyu Peng, Jiaming Zhang, Junwei Zheng, Zhifeng Teng, Kailun Yang, Rainer Stiefelhagen

On the other hand, the entire line segment detected by the visual subsystem overcomes the limitation of the LiDAR subsystem, which can only perform the local calculation for geometric features.

Autonomous Navigation Pose Estimation +2

Open Scene Understanding: Grounded Situation Recognition Meets Segment Anything for Helping People with Visual Impairments

1 code implementation15 Jul 2023 Ruiping Liu, Jiaming Zhang, Kunyu Peng, Junwei Zheng, Ke Cao, Yufan Chen, Kailun Yang, Rainer Stiefelhagen

Grounded Situation Recognition (GSR) is capable of recognizing and interpreting visual scenes in a contextually intuitive way, yielding salient activities (verbs) and the involved entities (roles) depicted in images.

Grounded Situation Recognition Navigate +1

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