Hongmin Liu
Biographic Data
| ID | 7308952 |
|---|---|
| NAME | Hongmin Liu |
| GIVEN NAMES | Hongmin |
| FAMILY NAME | Liu |
| SIGNATURE | LIU H |
| AFFILIATIONS | University of Science and Technology Beijing |
| ORCID | 0000-0001-9834-4087 |
| VERIFIED | Yes |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 3 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2019 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 0 |
DMotion
In autonomous driving, motion prediction is vital for anticipating the behaviors of surrounding vehicles, pedestrians, and other road users, enabling the system to make accurate decisions and plan driving paths effectively. Current motion prediction models typically utilize an encoder–decoder architecture, and many methods focus on the decoder’s design because the decoder is directly responsible for generating future trajectories. However, this o…
Robust Synthetic-to-Real Ensemble Dehazing Algorithm With the Intermediate Domain
Learning-based dehazing methods using synthetic datasets cannot generalize well on real-world hazy images due to the large domain discrepancy. To tackle this issue, we propose a robust synthetic-to-real dehazing framework with the construction of an intermediate domain and ensemble learning strategy. First, by mapping all examples to the intermediate domain, the bidirectional match strategy with adversarial training and the constraint of intermed…
Child compound Endothelium corneum attenuates gastrointestinal dysmotility through regulating the homeostasis of brain-gut-microbiota axis in functional dyspepsia rats
No prominent works on this page.
Child compound Endothelium corneum attenuates gastrointestinal dysmotility through regulating the homeostasis of brain-gut-microbiota axis in functional dyspepsia rats
Robust Synthetic-to-Real Ensemble Dehazing Algorithm With the Intermediate Domain
Learning-based dehazing methods using synthetic datasets cannot generalize well on real-world hazy images due to the large domain discrepancy. To tackle this issue, we propose a robust synthetic-to-real dehazing framework with the construction of an intermediate domain and ensemble learning strategy. First, by mapping all examples to the intermediate domain, the bidirectional match strategy with adversarial training and the constraint of intermed…
DMotion
In autonomous driving, motion prediction is vital for anticipating the behaviors of surrounding vehicles, pedestrians, and other road users, enabling the system to make accurate decisions and plan driving paths effectively. Current motion prediction models typically utilize an encoder–decoder architecture, and many methods focus on the decoder’s design because the decoder is directly responsible for generating future trajectories. However, this o…
Artificial Intelligence (2 works) · Computer Science (2 works) · Computer vision (2 works) · Advanced Image Processing Techniques (1 work) · Advanced Neural Network Applications (1 work) · Algorithm (1 work) · Biochemistry (1 work) · Biology (1 work) · Domain adaptation (1 work) · Endocrinology (1 work)