Demo Data from VLDB 2026 / reviewers in the wild / expert
Jingxian Sun 0003
dblp:257/8526
· DBLP profile ↗
2ranked-venue papers
1as first author
2since 2021 · last 2021
0000-0002-9398-9617ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-author · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Artificial intelligence
1 paper |
Efficient and distributed learning · 61% Video understanding and tracking · 39% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Efficient and distributed learning › model compression › knowledge distillation
cross-modal distillation |
0.5 | 1 | 2021 | Unsupervised Cross-Modal Distillation for Thermal Infrared Tracking · ACM Multimedia 2021 |
Computer vision › Video understanding and tracking › object tracking
thermal infrared tracking |
0.5 | 1 | 2021 | Unsupervised Cross-Modal Distillation for Thermal Infrared Tracking · ACM Multimedia 2021 |
Machine learning › Efficient and distributed learning › distillation
unsupervised distillation |
0.5 | 1 | 2021 | Unsupervised Cross-Modal Distillation for Thermal Infrared Tracking · ACM Multimedia 2021 |
Computer vision › Video understanding and tracking › object tracking
target representation |
0.1 | 1 | 2021 | Unsupervised Cross-Modal Distillation for Thermal Infrared Tracking · ACM Multimedia 2021 |
Methods — techniques the papers use, named apart from their topics
unsupervised cross-modal distillation · 0.5convolutional neural network · 0.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2021 | Unsupervised Cross-Modal Distillation for Thermal Infrared TrackingabstractThe target representation learned by convolutional neural networks plays an important role in Thermal Infrared (TIR) tracking. Currently, most of the top-performing TIR trackers are still employing representations learned by the model trained on the RGB data. However, this representation does not take into account the information in the TIR modality itself, limiting the performance of TIR tracking. Jingxian Sun 0003, Lichao Zhang 0001, Yufei Zha, Abel Gonzalez-Garcia, Peng Zhang 0005, Wei Huang 0013, Yanning Zhang 0001 |
ACM Multimedia | 1 |
| 2021 | Learning spatial-channel regularization jointly with correlation filter for visual tracking
Yufei Zha, Zhuling Qiu, Jingxian Sun 0003, Peng Zhang 0005, Wei Huang 0013 |
Neurocomputing | 3 |