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Xiao Tan

4 papers here

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author4

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • cs.CV4
ORCID 0000-0003-3109-9114
same name
  • Xiao Tan — 22 papers, h 32
  • Xiao Tan — 8 papers
  • Xiao Tan — 5 papers
  • Xiao Tan — 3 papers, h 11
  • Xiao Tan — 1 paper, h 2
  • Xiao Tan — 1 paper

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

most citedByteTrackV2: 2D and 3D Multi-Object Tracking by Associating Every Detection Box

5 citations · 7 across the 4 of their papers we have counts for

collaborators

4 papers

cs.CV2024

Explore the LiDAR-Camera Dynamic Adjustment Fusion for 3D Object Detection

Yiran Yang, Xu Gao, Tong Wang +5

Camera and LiDAR serve as informative sensors for accurate and robust autonomous driving systems. However, these sensors often exhibit heterogeneous natures, resulting in distribut…

cs.CV2024

OPEN: Object-wise Position Embedding for Multi-view 3D Object Detection

Jinghua Hou, Tong Wang, Xiaoqing Ye +6

Accurate depth information is crucial for enhancing the performance of multi-view 3D object detection. Despite the success of some existing multi-view 3D detectors utilizing pixel-…

cs.CV2023★ 5 cited

ByteTrackV2: 2D and 3D Multi-Object Tracking by Associating Every Detection Box

Yifu Zhang, Xinggang Wang, Xiaoqing Ye +6

Multi-object tracking (MOT) aims at estimating bounding boxes and identities of objects across video frames. Detection boxes serve as the basis of both 2D and 3D MOT. The inevitabl…

cs.CV2023★ 2 cited

Ambiguity-Resistant Semi-Supervised Learning for Dense Object Detection

Chang Liu, Weiming Zhang, Xiangru Lin +6

With basic Semi-Supervised Object Detection (SSOD) techniques, one-stage detectors generally obtain limited promotions compared with two-stage clusters. We experimentally find that…

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