Sitemap
A list of all the posts and pages found on the site. For you robots out there, there is an XML version available for digesting as well.
Pages
Posts
Future Blog Post
Published:
This post will show up by default. To disable scheduling of future posts, edit config.yml and set future: false.
Blog Post number 4
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 3
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 2
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 1
Published:
This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
portfolio
Portfolio item number 1
Short description of portfolio item number 1
Portfolio item number 2
Short description of portfolio item number 2
publications
MSFA: Multi-stage feature aggregation network for multi-label image recognition
Published in IET Image Processing, 2024
Multi-label image recognition (MLR) is a significant branch of image classification that aims to assign multiple categorical labels to each input. Previous research has focused on enhancing the learning of category-related regional features. However, the potential impact of multi-scale distributions in intra- and inter-category targets on MLR tends to be neglected. Besides, semantic consistency for categories is restricted to be considered on single-scale features, resulting in suboptimal feature extraction. To address the limitations of above, a Multi-stage Feature Aggregation (MSFA) network is proposed. In MSFA, a novel local feature extraction method is suggested to progressively extract category-related high-resolution local features in both spatial and channel dimensions. Subsequently, local and global features are fused without additional up- and down-sampling to enrich the scale diversity of the features while incorporating refined class-specific information. Furthermore, a hierarchical prediction scheme for MLR is proposed, which generates classification confidence corresponding to different scales under hierarchical loss supervision. Consequently, the final output of the network comes from the joint prediction by the classifiers on multi-scale features, ensuring a stronger feature extraction capability. The extensive experiments have been carried on VOC and MS-COCO datasets, and the superiority of MSFA over existing mainstream methods has been verified.
Recommended citation: Jiale Chen, Feng Xu, Tao Zeng, Xin Li, Shangjing Chen, Jie Yu. MSFA: Multi-stage feature aggregation network for multi-label image recognition. IET Image Processing, 2024.
Download Paper
Knowledge-driven prototype refinement for few-shot fine-grained recognition
Published in Knowledge-Based Systems, 2025
Advancements in deep learning have made image classification rival human performance with sufficient data and supervision. However, in domains with limited visual samples and high labeling costs, enabling AI systems to learn efficiently from few examples is challenging. This challenge is compounded in fine-grained categories, where subtle differences and scarce samples hinder robust representation extraction. To address this, we propose the Knowledge-Driven Prototype Refinement (KDPR) framework, which enhances few-shot fine-grained recognition by integrating prior knowledge from text. KDPR simulates human focus on discriminative foreground regions to extract refined views, forming a dual-branch learning framework alongside original images. It also constructs an unsupervised adjacency graph among visual instances and uses graph neural networks to improve category representation robustness. Additionally, a knowledge transfer-based image recognizer integrates prior text embeddings with global semantics directly into visual recognition, providing extra semantic guidance. To optimize knowledge-to-vision mapping, an auxiliary spatial prototype calibration aligns prototype representations across multiple spaces. Extensive experiments on three fine-grained datasets and two popular backbones demonstrate the effectiveness and state-of-the-art performance of our approach, especially in 1-shot learning. The source code is available at: https://github.com/HHU-JialeChen/KDPRNet.
Recommended citation: Jiale Chen, Feng Xu, Xin Lyu, Tao Zeng, Xin Li, and Shangjing Chen. Knowledge-driven prototype refinement for few-shot fine-grained recognition. Knowledge-Based Systems, 318:113535, 2025.
Download Paper
Self-Supervised Contrastive Learning for Multi-Label Images
Published in arXiv, 2025
Self-supervised learning (SSL) has demonstrated its effectiveness in learning representations through comparison methods that align with human intuition. However, mainstream SSL methods heavily rely on high body datasets with single label, such as ImageNet, resulting in intolerable pre-training overhead. Besides, more general multi-label images are frequently overlooked in SSL, despite their potential for richer semantic information and broader applicability in downstream scenarios. Therefore, we tailor the mainstream SSL approach to guarantee excellent representation learning capabilities using fewer multi-label images. Firstly, we propose a block-wise augmentation module aimed at extracting additional potential positive view pairs from multi-label images. Subsequently, an image-aware contrastive loss is devised to establish connections between these views, thereby facilitating the extraction of semantically consistent representations. Comprehensive linear fine-tuning and transfer learning validate the competitiveness of our approach despite challenging sample quality and quantity. Code is available on https://github.com/HHU-JialeChen/SSL.git.
Recommended citation: Jiale Chen. Self-Supervised Contrastive Learning for Multi-Label Images. arXiv, 2025.
Download Paper
Multi-view aggregation and multi-relation alignment for few-shot fine-grained recognition
Published in Expert Systems with Applications, 2025
Few-shot fine-grained recognition (FS-FGR) aims to recognize nuanced categories with a limited number of labeled samples that were not encountered during training. Previous work has made significant progress by enhancing the learning of foreground refined regions and the alignment of consistent semantics. However, the detrimental impact of insufficient background diversity on constructing representative category prototypes has been overlooked. Meanwhile, the alignment of semantically consistent features has been hampered by the reliance on singular metrics, resulting in suboptimal feature extraction. To address the limitations above, a novel framework with multi-view aggregation and multi-relation alignment (MVRA) is proposed. In this framework, we strive to refine category prototypes by generating and consolidating multiple views from limited learnable samples. Specifically, we generate foreground-refined views, pinpointing discriminative regions, and background-obfuscated views, broadening the landscape of background diversity. Further, without relying on the entire prior, a global label assignment module is designed to automatically assign reliable labels to the query set samples. Finally, armed with these credible labels, the multi-relation alignment module harnesses the enriched views and their semantic congruencies, facilitating robust feature extraction. The effectiveness and outstanding performance of MVRA are evaluated through extensive experiments conducted on three fine-grained benchmark datasets.
Recommended citation: Jiale Chen, Feng Xu, Xin Lyu, Tao Zeng, Xin Li, and Shangjing Chen. Multi-view aggregation and multi-relation alignment for few-shot fine-grained recognition. Expert Systems with Applications, 283:127764, 2025.
Download Paper
Knowledge-Guided Distribution Alignment for Cross-Domain Few-Shot Learning
Published in Knowledge-Based Systems, 2025
Cross-domain few-shot learning (CD-FSL) aims to recognize novel categories with minimal labeled samples in target domains that differ from source domains. However, difficulty in obtaining valid domain bias guidance leads to negative transfer challenges because the target domain samples are unknown during source domain training. Inspired by human reliance on prior knowledge when adapting to new domains, we propose a knowledge-guided distribution alignment network (KDANet). In contrast to earlier CD-FSL approaches that primarily focus on visual alignment alone, KDANet incorporates textual priors in both the training and adaptation stages, thereby enhancing domain transferability. Specifically, KDANet integrates textual priors as knowledge guidance for visual representation learning during pretraining in the source domain with sufficient samples to establish an initial learning foundation. To tackle the scarcity of target domain samples, available samples and construct pseudo-episodes are expanded through critical region detection. Leveraging the pretrained model and pseudo-episodes, a two-stage progressive finetuning method is employed to refine feature extraction and calibrate prototypes for target domain tasks, with prior knowledge guiding the learning process continuously. Moreover, adaptive distribution alignment is proposed throughout cross-domain training and finetuning to suppress distribution bias interference by utilizing multi-source domain alignment and triplet supervision. Quantitative and qualitative experiments demonstrate the superior performance of proposed method, particularly in challenging 1-shot tasks. Under the CD-FSL benchmark, proposed method achieves an average accuracy improvement of 3% across all target domains, outperforming state-of-the-art methods. Code is available in https://github.com/HHU-JialeChen/KDANet.git
Recommended citation: Jiale Chen, Feng Xu, Xin Lyu, Tao Zeng, Xin Li, and Shangjing Chen. Knowledge-Guided Distribution Alignment for Cross-Domain Few-Shot Learning. Knowledge-Based Systems, 318:114316, 2025.
Download Paper
talks
Talk 1 on Relevant Topic in Your Field
Published:
This is a description of your talk, which is a markdown file that can be all markdown-ified like any other post. Yay markdown!
Conference Proceeding talk 3 on Relevant Topic in Your Field
Published:
This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
This is a description of a teaching experience. You can use markdown like any other post.
Teaching experience 2
Workshop, University 1, Department, 2015
This is a description of a teaching experience. You can use markdown like any other post.