activity
20242026
most citedEvTexture++: Event-Driven Texture Enhancement for Video Super-Resolution

4 citations · 4 across the 1 of their papers we have counts for

collaborators

9 papers

cs.CV20264 cited

EvTexture++: Event-Driven Texture Enhancement for Video Super-Resolution

Dachun Kai, Jiayao Lu, Yueyi Zhang +1

Event-based vision has drawn increasing attention owing to its distinctive properties, including ultra-high temporal resolution and extreme dynamic range. Recent works have introdu…

cs.CV2026

Seeing the Unseen: Zooming in the Dark with Event Cameras

Dachun Kai, Zeyu Xiao, Huyue Zhu +3

This paper addresses low-light video super-resolution (LVSR), aiming to restore high-resolution videos from low-light, low-resolution (LR) inputs. Existing LVSR methods often strug…

cs.CV2025

Efficient Event-Based Semantic Segmentation via Exploiting Frame-Event Fusion: A Hybrid Neural Network Approach

Hebei Li, Yansong Peng, Jiahui Yuan +4

Event cameras have recently been introduced into image semantic segmentation, owing to their high temporal resolution and other advantageous properties. However, existing event-bas…

cs.CV2025

Create Anything Anywhere: Layout-Controllable Personalized Diffusion Model for Multiple Subjects

Wei Li, Hebei Li, Yansong Peng +3

Diffusion models have significantly advanced text-to-image generation, laying the foundation for the development of personalized generative frameworks. However, existing methods la…

cs.CV2025

Event-Enhanced Blurry Video Super-Resolution

Dachun Kai, Yueyi Zhang, Jin Wang +3

In this paper, we tackle the task of blurry video super-resolution (BVSR), aiming to generate high-resolution (HR) videos from low-resolution (LR) and blurry inputs. Current BVSR m…

cs.CV2025

EE-MLLM: A Data-Efficient and Compute-Efficient Multimodal Large Language Model

Feipeng Ma, Yizhou Zhou, Zheyu Zhang +7

Recent advancements in Multimodal Large Language Models (MLLMs) have demonstrated satisfactory performance across various vision-language tasks. Current approaches for vision and l…