Search Results for author: Weihao Jiang

Found 5 papers, 3 papers with code

Boosting Meta-Training with Base Class Information for Few-Shot Learning

no code implementations6 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.

Few-Shot Learning

PA-SAM: Prompt Adapter SAM for High-Quality Image Segmentation

1 code implementation23 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.

Image Segmentation Segmentation +1

Accelerating Dynamic Network Embedding with Billions of Parameter Updates to Milliseconds

1 code implementation15 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.

Graph Reconstruction Graph Representation Learning +3

Trimap-guided Feature Mining and Fusion Network for Natural Image Matting

1 code implementation1 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.

Image Matting

LRNNet: A Light-Weighted Network with Efficient Reduced Non-Local Operation for Real-Time Semantic Segmentation

no code implementations4 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.

Real-Time Semantic Segmentation Segmentation

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