6 citations · 9 across the 20 of their papers we have counts for
21 papers
Depth-guided Geometric Adaptation for Few-shot Image Classification
Zhiqi Miao, Estefanía Talavera, Weichuan Zhang +1
Few-shot image classification (FSIC) frequently suffers from visual ambiguity, where background clutter and RGB appearance similarities hinder discriminative feature matching in da…
Phase-Aware Spatial-Frequency Fusion for Few-Shot Fine-Grained Image Classification
Ruiling Liu, Linyue Zhang, Wenyi Zeng +5
Few-shot fine-grained image classification (FSFGIC) aims to classify similar images with limited labeled examples. This work highlights the critical yet underutilized role of phase…
GloResNet: A lightweight 3D CNN with global topological features for preterm brain injury prediction
Boyu Yuan, Jiamiao Lu, Weichuan Zhang +5
This study introduces an automated deep learning framework for predicting brain injury (BI) in preterm infants from T2-weighted MRI (dHCP dataset). We propose GloResNet, a lightwei…
Frequency-Enhanced Dual-Subspace Networks for Few-Shot Fine-Grained Image Classification
Meijia Wang, Guochao Wang, Haozhen Chu +4
Few-shot fine-grained image classification aims to recognize subcategories with high visual similarity using only a limited number of annotated samples. Existing metric learning-ba…
Adaptive receptive field-based spatial-frequency feature reconstruction network for fine-grained few-shot image classification
Linyue Zhang, Wenyi Zeng, Zicheng Pan +6
Feature reconstruction techniques are widely applied for few-shot fine-grained image classification (FSFGIC). Our research indicates that one of the main challenges facing existing…
Meningioma Analysis and Diagnosis using Limited Labeled Samples
Jiamiao Lu, Wei Wu, Ke Gao +8
The biological behavior and treatment response of meningiomas depend on their grade, making an accurate diagnosis essential for treatment planning and prognosis assessment. We obse…
Deep learning-based neurodevelopmental assessment in preterm infants
Lexin Ren, Jiamiao Lu, Weichuan Zhang +6
Preterm infants (born between 28 and 37 weeks of gestation) face elevated risks of neurodevelopmental delays, making early identification crucial for timely intervention. While dee…
Spatial frequency information fusion network for few-shot learning
Wenqing Zhao, Guojia Xie, Han Pan +2
The objective of Few-shot learning is to fully leverage the limited data resources for exploring the latent correlations within the data by applying algorithms and training a model…
Feature Complementation Architecture for Visual Place Recognition
Weiwei Wang, Meijia Wang, Haoyi Wang +5
Visual place recognition (VPR) plays a crucial role in robotic localization and navigation. The key challenge lies in constructing feature representations that are robust to enviro…
Visual Explanation via Similar Feature Activation for Metric Learning
Yi Liao, Ugochukwu Ejike Akpudo, Jue Zhang +4
Visual explanation maps enhance the trustworthiness of decisions made by deep learning models and offer valuable guidance for developing new algorithms in image recognition tasks.…
Frequency-Adaptive Discrete Cosine-ViT-ResNet Architecture for Sparse-Data Vision
Ziyue Kang, Weichuan Zhang
A major challenge in rare animal image classification is the scarcity of data, as many species usually have only a small number of labeled samples. To address this challenge, we de…
Dynamic Accumulated Attention Map for Interpreting Evolution of Decision-Making in Vision Transformer
Yi Liao, Yongsheng Gao, Weichuan Zhang
Various Vision Transformer (ViT) models have been widely used for image recognition tasks. However, existing visual explanation methods can not display the attention flow hidden in…
Propensity-driven Uncertainty Learning for Sample Exploration in Source-Free Active Domain Adaptation
Zicheng Pan, Xiaohan Yu, Weichuan Zhang +1
Source-free active domain adaptation (SFADA) addresses the challenge of adapting a pre-trained model to new domains without access to source data while minimizing the need for targ…
Neuron Abandoning Attention Flow: Visual Explanation of Dynamics inside CNN Models
Yi Liao, Yongsheng Gao, Weichuan Zhang
In this paper, we present a Neuron Abandoning Attention Flow (NAFlow) method to address the open problem of visually explaining the attention evolution dynamics inside CNNs when ma…
A novel spatial-frequency domain network for zero-shot incremental learning
Jie Ren, Yang Zhao, Weichuan Zhang +1
Zero-shot incremental learning aims to enable the model to generalize to new classes without forgetting previously learned classes. However, the semantic gap between old and new sa…
Feature Activation Map: Visual Explanation of Deep Learning Models for Image Classification
Yi Liao, Yongsheng Gao, Weichuan Zhang
Decisions made by convolutional neural networks(CNN) can be understood and explained by visualizing discriminative regions on images. To this end, Class Activation Map (CAM) based…
Automotive Radar Mutual Interference Mitigation Based on Hough Transform in Time-Frequency Domain
Yanbing Li, Weichuan Zhang, Lianying Ji
With the development of autonomous driving technology, automotive radar has received unprecedented attention due to its day-and-night and all-weather working capability. It is wort…
Second-order Anisotropic Gaussian Directional Derivative Filters for Blob Detection
Jie Ren, Wenya Yu, Jiapan Guo +2
Interest point detection methods have received increasing attention and are widely used in computer vision tasks such as image retrieval and 3D reconstruction. In this work, second…
Color Image Edge Detection using Multi-scale and Multi-directional Gabor filter
Yunhong Li, Yuandong Bi, Weichuan Zhang +2
In this paper, a color edge detection method is proposed where the multi-scale Gabor filter are used to obtain edges from input color images. The main advantage of the proposed met…
NDPNet: A novel non-linear data projection network for few-shot fine-grained image classification
Weichuan Zhang, Xuefang Liu, Zhe Xue +2
Metric-based few-shot fine-grained image classification (FSFGIC) aims to learn a transferable feature embedding network by estimating the similarities between query images and supp…
Image Feature Information Extraction for Interest Point Detection: A Review
Junfeng Jing, Tian Gao, Weichuan Zhang +2
Interest point detection is one of the most fundamental and critical problems in computer vision and image processing. In this paper, we carry out a comprehensive review on image f…