activity
20222025
most citedEnhancement by Your Aesthetic: An Intelligible Unsupervised Personalized Enhancer for Low-Light Images

12 citations · 17 across the 12 of their papers we have counts for

collaborators

12 papers

cs.CV2025

VideoScaffold: Elastic-Scale Visual Hierarchies for Streaming Video Understanding in MLLMs

Naishan Zheng, Jie Huang, Qingpei Guo +1

Understanding long videos with multimodal large language models (MLLMs) remains challenging due to the heavy redundancy across frames and the need for temporally coherent represent…

cs.CV2025

WaterWave: Bridging Underwater Image Enhancement into Video Streams via Wavelet-based Temporal Consistency Field

Qi Zhu, Jingyi Zhang, Naishan Zheng +4

Underwater video pairs are fairly difficult to obtain due to the complex underwater imaging. In this case, most existing video underwater enhancement methods are performed by direc…

cs.LG2025

RLFR: Extending Reinforcement Learning for LLMs with Flow Environment

Jinghao Zhang, Naishan Zheng, Ruilin Li +4

Reinforcement Learning with Verifiable Rewards (RLVR) has recently emerged as a promising framework for improving reasoning abilities in Large Language Models (LLMs). However, poli…

cs.CV2025

InfoScale: Unleashing Training-free Variable-scaled Image Generation via Effective Utilization of Information

Guohui Zhang, Jiangtong Tan, Linjiang Huang +4

Diffusion models (DMs) have become dominant in visual generation but suffer performance drop when tested on resolutions that differ from the training scale, whether lower or higher…

cs.CV2024

Linearly-evolved Transformer for Pan-sharpening

Junming Hou, Zihan Cao, Naishan Zheng +6

Vision transformer family has dominated the satellite pan-sharpening field driven by the global-wise spatial information modeling mechanism from the core self-attention ingredient.…

cs.CV2023

Singular Regularization with Information Bottleneck Improves Model's Adversarial Robustness

Guanlin Li, Naishan Zheng, Man Zhou +2

Adversarial examples are one of the most severe threats to deep learning models. Numerous works have been proposed to study and defend adversarial examples. However, these works la…