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Jing Shi

4 papers hereh-index 374 citations5 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.CV3
  • cs.LG1
same name
  • Jing Shi — 25 papers, h 50
  • Jing Shi — 11 papers, h 16
  • Jing Shi — 10 papers
  • Jing Shi — 6 papers, h 11
  • Jing Shi — 5 papers, h 8
  • Jing Shi — 5 papers, 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

most citedAV-DiT: Efficient Audio-Visual Diffusion Transformer for Joint Audio and Video Generation

1 citations · 1 across the 2 of their papers we have counts for

collaborators

4 papers

cs.CV2026

VeriPhy: Agentic Physical Reasoning for World Model Evaluation and Refinement

Wenzhuo Xu, Yuchen Zhu, Chongjian Ge +8

Visual fluency in generated video does not imply physical reliability, and a scalar quality score alone is incapable of indicating the obligation a clip violates or the moment it f…

cs.CV2026

Chimera: Designing and Chinchilla-Scaling Hybrid Visual Diffusion Transformers

Chongjian Ge, Hanwen Jiang, Tianyu Wang +9

Visual generation increasingly requires high-resolution images, long videos, and multimodal context, making the quadratic cost of full attention prohibitive. We introduce Chimera,…

cs.LG2026

FLARE: Diffusion for Hybrid Language Model

Yuchen Zhu, Jing Shi, Chongjian Ge +9

Autoregressive (AR) large language models (LLMs) have achieved broad practical success, but sequential decoding remains a key bottleneck for low-latency deployment. Recent efficien…

cs.CV2024★ 1 cited

AV-DiT: Efficient Audio-Visual Diffusion Transformer for Joint Audio and Video Generation

Kai Wang, Shijian Deng, Jing Shi +2

Recent Diffusion Transformers (DiTs) have shown impressive capabilities in generating high-quality single-modality content, including images, videos, and audio. However, it is stil…

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