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Ming Li

5 papers here

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

author position
  • middle author4
  • last author1

Across the 5 of 5 papers where every author was matched, so the position is known.

fields
  • cs.CV4
  • cs.SD1
same name
  • Ming Li — 36 papers, h 82
  • Ming Li — 36 papers
  • Ming Li — 26 papers, h 34
  • Ming Li — 18 papers, h 18
  • Ming Li — 14 papers
  • Ming Li — 14 papers, h 15

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
20242026
collaborators

5 papers

cs.CV2026

EventFlash: Towards Efficient MLLMs for Event-Based Vision

Shaoyu Liu, Jianing Li, Guanghui Zhao +4

Event-based multimodal large language models (MLLMs) enable robust perception in high-speed and low-light scenarios, addressing key limitations of frame-based MLLMs. However, curre…

cs.CV2026

LoL: Longer than Longer, Scaling Video Generation to Hour

Justin Cui, Jie Wu, Ming Li +6

Recent research in long-form video generation has shifted from bidirectional to autoregressive models, yet these methods commonly suffer from error accumulation and a loss of long-…

cs.CV2025

Self-Forcing++: Towards Minute-Scale High-Quality Video Generation

Justin Cui, Jie Wu, Ming Li +6

Diffusion models have revolutionized image and video generation, achieving unprecedented visual quality. However, their reliance on transformer architectures incurs prohibitively h…

cs.CV2025

RewardDance: Reward Scaling in Visual Generation

Jie Wu, Yu Gao, Zilyu Ye +9

Reward Models (RMs) are critical for improving generation models via Reinforcement Learning (RL), yet the RM scaling paradigm in visual generation remains largely unexplored. It pr…

cs.SD2024

Efficient Video to Audio Mapper with Visual Scene Detection

Mingjing Yi, Ming Li

Video-to-audio (V2A) generation aims to produce corresponding audio given silent video inputs. This task is particularly challenging due to the cross-modality and sequential nature…

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