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Tsung-Han Wu

4 papers hereh-index 7869 citations19 works total

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
same name
  • Tsung-Han Wu — 3 papers
  • Tsung-Han Wu — 2 papers
  • Tsung-Han Wu — 1 paper
  • Tsung-Han Wu — 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

activity
20202022
most citedMonoDTR: Monocular 3D Object Detection with Depth-Aware Transformer

10 citations · 11 across the 3 of their papers we have counts for

collaborators

4 papers

cs.CV2022

Learning Fine-Grained Visual Understanding for Video Question Answering via Decoupling Spatial-Temporal Modeling

Hsin-Ying Lee, Hung-Ting Su, Bing-Chen Tsai +3

While recent large-scale video-language pre-training made great progress in video question answering, the design of spatial modeling of video-language models is less fine-grained t…

cs.CV2022★ 10 cited

MonoDTR: Monocular 3D Object Detection with Depth-Aware Transformer

Kuan-Chih Huang, Tsung-Han Wu, Hung-Ting Su +1

Monocular 3D object detection is an important yet challenging task in autonomous driving. Some existing methods leverage depth information from an off-the-shelf depth estimator to…

cs.CV2021

S3: Learnable Sparse Signal Superdensity for Guided Depth Estimation

Yu-Kai Huang, Yueh-Cheng Liu, Tsung-Han Wu +5

Dense depth estimation plays a key role in multiple applications such as robotics, 3D reconstruction, and augmented reality. While sparse signal, e.g., LiDAR and Radar, has been le…

cs.CV2020★ 1 cited

Expanding Sparse Guidance for Stereo Matching

Yu-Kai Huang, Yueh-Cheng Liu, Tsung-Han Wu +2

The performance of image based stereo estimation suffers from lighting variations, repetitive patterns and homogeneous appearance. Moreover, to achieve good performance, stereo sup…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.