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Zijian He

19 papers hereh-index 183.1k citations25 works total

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

author position
  • middle author18
  • last author1

Across the 19 of 19 papers where every author was matched, so the position is known.

fields
  • cs.CV19
same name
  • Zijian He — 9 papers, h 3
  • Zijian He — 7 papers, h 4
  • Zijian He — 4 papers, h 4
  • Zijian He — 3 papers, h 2
  • Zijian He — 2 papers, h 6
  • Zijian He — 1 paper, h 3

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
20192025
most citedEmu: Enhancing Image Generation Models Using Photogenic Needles in a Haystack

30 citations · 164 across the 19 of their papers we have counts for

collaborators
Showing 2023Show all

4 papers · 1 filter

cs.CV2023

ControlRoom3D: Room Generation using Semantic Proxy Rooms

Jonas Schult, Sam Tsai, Lukas Höllein +11

Manually creating 3D environments for AR/VR applications is a complex process requiring expert knowledge in 3D modeling software. Pioneering works facilitate this process by genera…

cs.CV2023★ 30 cited

Emu: Enhancing Image Generation Models Using Photogenic Needles in a Haystack

Xiaoliang Dai, Ji Hou, Chih-Yao Ma +23

Training text-to-image models with web scale image-text pairs enables the generation of a wide range of visual concepts from text. However, these pre-trained models often face chal…

cs.CV2023★ 4 cited

NeRF-Det: Learning Geometry-Aware Volumetric Representation for Multi-View 3D Object Detection

Chenfeng Xu, Bichen Wu, Ji Hou +8

We present NeRF-Det, a novel method for indoor 3D detection with posed RGB images as input. Unlike existing indoor 3D detection methods that struggle to model scene geometry, our m…

cs.CV2023

Mask3D: Pre-training 2D Vision Transformers by Learning Masked 3D Priors

Ji Hou, Xiaoliang Dai, Zijian He +2

Current popular backbones in computer vision, such as Vision Transformers (ViT) and ResNets are trained to perceive the world from 2D images. However, to more effectively understan…

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