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researcher

Jia Zeng

9 papers hereh-index 6474 citations9 works total

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

author position
  • first author1
  • middle author8

Across the 9 of 9 papers where every author was matched, so the position is known.

fields
  • cs.RO5
  • cs.CV4
same name
  • Jia Zeng — 21 papers, h 10
  • Jia Zeng — 11 papers, h 19
  • Jia Zeng — 10 papers, h 19
  • Jia Zeng — 2 papers, h 3
  • Jia Zeng — 2 papers, h 4
  • Jia Zeng — 1 paper, h 1

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
20232026
most citedFully Sparse 3D Occupancy Prediction

6 citations · 11 across the 9 of their papers we have counts for

collaborators
Showing cs.CVShow all

4 papers · 1 filter

cs.CV2025

FlowPortal: Residual-Corrected Flow for Training-Free Video Relighting and Background Replacement

Wenshuo Gao, Junyi Fan, Jiangyue Zeng +1

Video relighting with background replacement is a challenging task critical for applications in film production and creative media. Existing methods struggle to balance temporal co…

cs.CV2024★ 2 cited

Embodied Understanding of Driving Scenarios

Yunsong Zhou, Linyan Huang, Qingwen Bu +7

Embodied scene understanding serves as the cornerstone for autonomous agents to perceive, interpret, and respond to open driving scenarios. Such understanding is typically founded…

cs.CV2024

GenAD: Generalized Predictive Model for Autonomous Driving

Jiazhi Yang, Shenyuan Gao, Yihang Qiu +11

In this paper, we introduce the first large-scale video prediction model in the autonomous driving discipline. To eliminate the restriction of high-cost data collection and empower…

cs.CV2023★ 6 cited

Fully Sparse 3D Occupancy Prediction

Haisong Liu, Yang Chen, Haiguang Wang +6

Occupancy prediction plays a pivotal role in autonomous driving. Previous methods typically construct dense 3D volumes, neglecting the inherent sparsity of the scene and suffering…

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