Search Results for author: Wei Zhuo

Found 22 papers, 9 papers with code

DINOv2-powered Few-Shot Semantic Segmentation: A Unified Framework via Cross-Model Distillation and 4D Correlation Mining

no code implementations22 Apr 2025 Wei Zhuo, Zhiyue Tang, Wufeng Xue, Hao Ding, Linlin Shen

Few-shot semantic segmentation has gained increasing interest due to its generalization capability, i. e., segmenting pixels of novel classes requiring only a few annotated images.

Few-Shot Semantic Segmentation Meta-Learning +1

EchoONE: Segmenting Multiple echocardiography Planes in One Model

1 code implementation4 Dec 2024 Jiongtong Hu, Wei Zhuo, Jun Cheng, Yingying Liu, Wufeng Xue, Dong Ni

Effective solution for such a multi-plane segmentation (MPS) problem is highly demanded for medical images, yet has not been well investigated.

model

Partitioning Message Passing for Graph Fraud Detection

no code implementations16 Nov 2024 Wei Zhuo, Zemin Liu, Bryan Hooi, Bingsheng He, Guang Tan, Rizal Fathony, Jia Chen

Label imbalance and homophily-heterophily mixture are the fundamental problems encountered when applying Graph Neural Networks (GNNs) to Graph Fraud Detection (GFD) tasks.

Fraud Detection Inductive Bias

Orthogonal Hyper-category Guided Multi-interest Elicitation for Micro-video Matching

1 code implementation20 Jul 2024 Beibei Li, Beihong Jin, Yisong Yu, Yiyuan Zheng, Jiageng Song, Wei Zhuo, Tao Xiang

Moreover, OPAL employs a two-stage training strategy, in which the pre-train is to generate soft interests from historical interactions under the guidance of orthogonal hyper-categories of micro-videos and the fine-tune is to reinforce the degree of disentanglement among the interests and learn the temporal evolution of each interest of each user.

Disentanglement

Commute Graph Neural Networks

no code implementations30 Jun 2024 Wei Zhuo, Han Yu, Guang Tan, Xiaoxiao Li

However, their application to directed graphs (digraphs) presents unique challenges, primarily due to the inherent asymmetry in node relationships.

Benchmarking

Efficient Graph Similarity Computation with Alignment Regularization

1 code implementation21 Jun 2024 Wei Zhuo, Guang Tan

In the inference stage, the graph-level representations learned by the GNN encoder are directly used to compute the similarity score without using AReg again to speed up inference.

Graph Similarity

Question-Answer Cross Language Image Matching for Weakly Supervised Semantic Segmentation

1 code implementation18 Jan 2024 Songhe Deng, Wei Zhuo, Jinheng Xie, Linlin Shen

Class Activation Map (CAM) has emerged as a popular tool for weakly supervised semantic segmentation (WSSS), allowing the localization of object regions in an image using only image-level labels.

Contrastive Learning Prompt Engineering +4

Activating the Discriminability of Novel Classes for Few-shot Segmentation

no code implementations2 Dec 2022 Dianwen Mei, Wei Zhuo, Jiandong Tian, Guangming Lu, Wenjie Pei

To circumvent these two challenges, we propose to activate the discriminability of novel classes explicitly in both the feature encoding stage and the prediction stage for segmentation.

Segmentation

Improving Micro-video Recommendation via Contrastive Multiple Interests

1 code implementation19 May 2022 Beibei Li, Beihong Jin, Jiageng Song, Yisong Yu, Yiyuan Zheng, Wei Zhuo

With the rapid increase of micro-video creators and viewers, how to make personalized recommendations from a large number of candidates to viewers begins to attract more and more attention.

Contrastive Learning

Fully Self-Supervised Learning for Semantic Segmentation

no code implementations24 Feb 2022 YuAn Wang, Wei Zhuo, Yucong Li, Zhi Wang, Qi Ju, Wenwu Zhu

To solve this problem, we proposed a bootstrapped training scheme for semantic segmentation, which fully leveraged the global semantic knowledge for self-supervision with our proposed PGG strategy and CAE module.

Clustering Segmentation +2

Graph Neural Networks with Feature and Structure Aware Random Walk

no code implementations19 Nov 2021 Wei Zhuo, Guang Tan

Graph Neural Networks (GNNs) have received increasing attention for representation learning in various machine learning tasks.

Node Classification Representation Learning

Learning Inner-Group Relations on Point Clouds

1 code implementation ICCV 2021 Haoxi Ran, Wei Zhuo, Jun Liu, Li Lu

We further verify the expandability of RPNet, in terms of both depth and width, on the tasks of classification and segmentation.

3D Classification 3D Point Cloud Classification +4

Bootstrap Representation Learning for Segmentation on Medical Volumes and Sequences

no code implementations23 Jun 2021 Zejian Chen, Wei Zhuo, Tianfu Wang, Wufeng Xue, Dong Ni

Based on the continuity between slices/frames and the common spatial layout of organs across volumes/sequences, we introduced a novel bootstrap self-supervised representation learning method by leveraging the predictable possibility of neighboring slices.

Representation Learning Self-Supervised Learning

ST++: Make Self-training Work Better for Semi-supervised Semantic Segmentation

1 code implementation CVPR 2022 Lihe Yang, Wei Zhuo, Lei Qi, Yinghuan Shi, Yang Gao

In this work, we first construct a strong baseline of self-training (namely ST) for semi-supervised semantic segmentation via injecting strong data augmentations (SDA) on unlabeled images to alleviate overfitting noisy labels as well as decouple similar predictions between the teacher and student.

Semi-Supervised Semantic Segmentation

Self-Supervised Graph Learning with Proximity-based Views and Channel Contrast

no code implementations7 Jun 2021 Wei Zhuo, Guang Tan

Not restricted by connectivity in the original graph, the generated views allow the model to enhance its expressive power with new and complementary perspectives from which to look at the relationship between nodes.

Contrastive Learning Graph Learning +1

Improving Sequential Recommendation with Attribute-augmented Graph Neural Networks

no code implementations10 Mar 2021 Xinzhou Dong, Beihong Jin, Wei Zhuo, Beibei Li, Taofeng Xue

Many practical recommender systems provide item recommendation for different users only via mining user-item interactions but totally ignoring the rich attribute information of items that users interact with.

Attribute Graph Neural Network +1

Indoor Scene Parsing With Instance Segmentation, Semantic Labeling and Support Relationship Inference

no code implementations CVPR 2017 Wei Zhuo, Mathieu Salzmann, Xuming He, Miaomiao Liu

In particular, while some of them aim at segmenting the image into regions, such as object or surface instances, others aim at inferring the semantic labels of given regions, or their support relationships.

Instance Segmentation Scene Parsing +1

Indoor Scene Structure Analysis for Single Image Depth Estimation

no code implementations CVPR 2015 Wei Zhuo, Mathieu Salzmann, Xuming He, Miaomiao Liu

We tackle the problem of single image depth estimation, which, without additional knowledge, suffers from many ambiguities.

Depth Estimation

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