2023 ICCV ICCV 2023

Fine-grained Visible Watermark Removal

Abstract

Visible watermark removal aims to erase the watermark from watermarked image and recover the background image, which is a challenging task due to the diverse watermarks. Previous works have designed dynamic network to handle various types of watermarks adaptively, but they ignore that even the watermarked region in a single image can be divided into multiple local parts with distinct visual appearances. In this work, we advance image-specific dynamic network towards part-specific dynamic network, which discovers multiple local parts within the watermarked region and handle them adaptively. Specifically, we propose a query-based multi-task framework, in which part query embeddings are jointly used in two branches to predict part masks and restore watermarked parts. Extensive experiments demonstrate the effectiveness of our fine-grained watermark removal network.

🌉 Interdisciplinary Bridge — Computer Vision and Deep Learning
🧭 Keyword Pioneer — query-based multi-task framework
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio