no code implementations • 28 Mar 2024 • Weihao Jiang, Zhaozhi Xie, Yuxiang Lu, Longjie Qi, Jingyong Cai, Hiroyuki Uchiyama, Bin Chen, Yue Ding, Hongtao Lu
Our framework and model introduce the following key aspects: (1) to learn real-world adaptive semantic representation for objects with diverse and complex structures under real-world scenes, we introduce extra semantic segmentation and edge detection tasks on more diverse real-world data with segmentation annotations; (2) to avoid overfitting on low-level details, we propose a module to utilize the inconsistency between learned segmentation and matting representations to regularize detail refinement; (3) we propose a novel background line detection task into our auxiliary learning framework, to suppress interference of background lines or textures.
1 code implementation • 23 Jan 2024 • Zhaozhi Xie, Bochen Guan, Weihao Jiang, Muyang Yi, Yue Ding, Hongtao Lu, Lei Zhang
In this paper, we introduce a novel prompt-driven adapter into SAM, namely Prompt Adapter Segment Anything Model (PA-SAM), aiming to enhance the segmentation mask quality of the original SAM.
1 code implementation • 1 Dec 2021 • Weihao Jiang, Dongdong Yu, Zhaozhi Xie, Yaoyi Li, Zehuan Yuan, Hongtao Lu
For emerging content-based feature fusion, most existing matting methods only focus on local features which lack the guidance of a global feature with strong semantic information related to the interesting object.
Ranked #4 on Image Matting on Composition-1K
no code implementations • 4 Jun 2020 • Weihao Jiang, Zhaozhi Xie, Yaoyi Li, Chang Liu, Hongtao Lu
Many of these applications need to perform a real-time and efficient prediction for semantic segmentation with a light-weighted network.