no code implementations • 6 May 2024 • Weihao Jiang, Chang Liu, Kun He
Specifically, we swap the class (CLS) token and patch tokens between the support and query sets to have the mutual attention, which enables each set to focus on the most useful information.
no code implementations • 6 May 2024 • Weihao Jiang, Haoyang Cui, Kun He
Subsequently, we filter patch embeddings using class embeddings to retain only the class-relevant ones.
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.
no code implementations • 6 Mar 2024 • Weihao Jiang, Guodong Liu, Di He, Kun He
However, as a non-end-to-end training method, indicating the meta-training stage can only begin after the completion of pre-training, Meta-Baseline suffers from higher training cost and suboptimal performance due to the inherent conflicts of the two training stages.
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 • 15 Jun 2023 • Haoran Deng, Yang Yang, Jiahe Li, Haoyang Cai, ShiLiang Pu, Weihao Jiang
Network embedding, a graph representation learning method illustrating network topology by mapping nodes into lower-dimension vectors, is challenging to accommodate the ever-changing dynamic graphs in practice.
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.