activity
20152026
most citedDeep-Masking Generative Network: A Unified Framework for Background Restoration from Superimposed Images

42 citations · 214 across the 65 of their papers we have counts for

collaborators
Showing 2025Show all

6 papers · 1 filter

cs.CV2025

EditInfinity: Image Editing with Binary-Quantized Generative Models

Jiahuan Wang, Yuxin Chen, Jun Yu +2

Adapting pretrained diffusion-based generative models for text-driven image editing with negligible tuning overhead has demonstrated remarkable potential. A classical adaptation pa…

cs.CV2025

Enhancing Spatial Reasoning in Multimodal Large Language Models through Reasoning-based Segmentation

Zhenhua Ning, Zhuotao Tian, Shaoshuai Shi +4

Recent advances in point cloud perception have demonstrated remarkable progress in scene understanding through vision-language alignment leveraging large language models (LLMs). Ho…

cs.SD2025

Lightweight Joint Audio-Visual Deepfake Detection via Single-Stream Multi-Modal Learning Framework

Kuiyuan Zhang, Wenjie Pei, Rushi Lan +2

Deepfakes are AI-synthesized multimedia data that may be abused for spreading misinformation. Deepfake generation involves both visual and audio manipulation. To detect audio-visua…

cs.CV2025

Learning Compatible Multi-Prize Subnetworks for Asymmetric Retrieval

Yushuai Sun, Zikun Zhou, Dongmei Jiang +4

Asymmetric retrieval is a typical scenario in real-world retrieval systems, where compatible models of varying capacities are deployed on platforms with different resource configur…

cs.CV2025

Prototype Perturbation for Relaxing Alignment Constraints in Backward-Compatible Learning

Zikun Zhou, Yushuai Sun, Wenjie Pei +2

The traditional paradigm to update retrieval models requires re-computing the embeddings of the gallery data, a time-consuming and computationally intensive process known as backfi…

cs.CV2025

Recognition-Synergistic Scene Text Editing

Zhengyao Fang, Pengyuan Lyu, Jingjing Wu +4

Scene text editing aims to modify text content within scene images while maintaining style consistency. Traditional methods achieve this by explicitly disentangling style and conte…