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Feilong Tang

4 papers hereh-index 5127 citations5 works total

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

author position
  • middle author3

Across the 3 of 4 papers where every author was matched, so the position is known.

fields
  • cs.CV3
  • cs.LG1
same name
  • Feilong Tang — 20 papers, h 8
  • Feilong Tang — 13 papers, h 11
  • Feilong Tang — 2 papers, h 2
  • Feilong Tang — 2 papers, h 2
  • Feilong Tang — 2 papers, h 3
  • Feilong Tang — 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

collaborators

4 papers

cs.CV2025

VideoGen-of-Thought: Step-by-step generating multi-shot video with minimal manual intervention

Mingzhe Zheng, Yongqi Xu, Haojian Huang +8

Current video generation models excel at short clips but fail to produce cohesive multi-shot narratives due to disjointed visual dynamics and fractured storylines. Existing solutio…

cs.LG2025

ScalingNoise: Scaling Inference-Time Search for Generating Infinite Videos

Haolin Yang, Feilong Tang, Ming Hu +8

Video diffusion models (VDMs) facilitate the generation of high-quality videos, with current research predominantly concentrated on scaling efforts during training through improvem…

cs.CV2025

VideoGen-of-Thought: Step-by-step generating multi-shot video with minimal manual intervention

Mingzhe Zheng, Yongqi Xu, Haojian Huang +8

Current video generation models excel at short clips but fail to produce cohesive multi-shot narratives due to disjointed visual dynamics and fractured storylines. Existing solutio…

cs.CV2025

Unveiling the Ignorance of MLLMs: Seeing Clearly, Answering Incorrectly

Yexin Liu, Zhengyang Liang, Yueze Wang +8

Multimodal Large Language Models (MLLMs) have displayed remarkable performance in multi-modal tasks, particularly in visual comprehension. However, we reveal that MLLMs often gener…

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