Jin Han

JBHI Journal 2026 Journal Article

LRCOMF: A Learn-Review-Challenge Online Meta Learning Framework for EEG Emotion Recognition With Unlabeled Online Samples

• Yiyuan Chen
• Jin Han
• Jingtao Lv
• Xiaowei Qin
• Xiaodong Xu

Emotion recognition based on Electroencephalogram (EEG) is of great importance for cognitive psychology, disease therapy, etc. However, most mainstream recognition models are trained using batch learning, which fails to adapt to the dynamic and non-stationary nature of real-time EEG streams. In contrast, online learning can adjust model parameters continuously, but it typically relies on labeled data and a fully trained initial model, which are unavailable in practical scenarios. To tackle this challenge, we propose a novel learn-review-challenge online meta-learning framework (LRCOMF) for unlabeled online EEG learning. This framework incorporates a meta updating module through a multi-task cache and a customized sampling strategy to improve the model’s generalization during online learning. A sample judgement module is implemented based on a prototype weight being designed to estimate the confidence of predicted labels during the “learn-review” step. Additionally, a challenge module using a clustering quality metric determines whether low-confidence samples can be reconsidered during the “challenge” phase. The validation employed the DEAP and DREAMER datasets. In comparison to the initial baseline condition, the recognition model exhibited substantial enhancements 3. 59%/3. 87%/1. 98% (p $< $ 0. 001) for arousal, valence, dominance labels in the DEAP dataset. Substantial increases of 3. 42%/3. 63%/2. 93% (p $< $ 0. 001) were also noted in DREAMER’s evaluation of arousal/valence/dominance labels. This research is essential for tackling unlabeled online learning in authentic EEG contexts and enhancing the advancement of EEG practical applications.

AAAI Conference 2026 Conference Paper

Seeing Through the Rain: Resolving High-Frequency Conflicts in Deraining and Super-Resolution via Diffusion Guidance

• Wenjie Li
• Jinglei Shi
• Jin Han
• Heng Guo
• Zhanyu Ma

Clean images are crucial for visual tasks such as small object detection, especially at high resolutions. However, real-world images are often degraded by adverse weather, and weather restoration methods may sacrifice high-frequency details critical for analyzing small objects. A natural solution is to apply super-resolution (SR) after weather removal to recover both clarity and fine structures. However, simply cascading restoration and SR struggle to bridge their inherent conflict: removal aims to remove high-frequency weather-induced noise, while SR aims to hallucinate high-frequency textures from existing details, leading to inconsistent restoration contents. In this paper, we take deraining as a case study and propose DHGM, a Diffusion-based High-frequency Guided Model for generating clean and high-resolution images. DHGM integrates pre-trained diffusion priors with high-pass filters to simultaneously remove rain artifacts and enhance structural details. Extensive experiments demonstrate that DHGM achieves superior performance over existing methods, with lower costs.

EAAI Journal 2026 Journal Article

Translating regulatory clauses into executable codes for building design checking via large language model driven function matching and composing

• Zhe Zheng
• Jin Han
• Ke-Yin Chen
• Xin-Yu Cao
• Xin-Zheng Lu
• Jia-Rui Lin

AAAI Conference 2025 Conference Paper

DrugHash: Hashing Based Contrastive Learning for Virtual Screening

• Jin Han
• Yun Hong
• Wu-Jun Li

Virtual screening (VS) is a critical step in computer-aided drug discovery, aiming to identify molecules that bind to a specific target protein. Traditional VS methods, such as docking, are often too time-consuming to efficiently screen large-scale molecular databases. Recent advances in deep learning have demonstrated that learning vector representations for both proteins and molecules using contrastive learning can outperform traditional docking methods. However, considering that the target databases often contain billions of molecules, real-valued vector representations adopted by existing methods can still incur large memory and time cost in VS. To address this problem, we propose DrugHash, a hashing-based contrastive learning method for VS. DrugHash formulates VS as a retrieval task that leverages binary hash codes for efficient retrieval. In particular, DrugHash designs a simple yet effective hashing strategy to enable end-to-end learning of binary hash codes for both proteins and molecules, which can dramatically reduce the memory and time cost with higher accuracy compared with existing methods. Experimental results show that DrugHash can outperform existing methods to achieve state-of-the-art accuracy, with at least a 32 times reduction in memory cost and a 4.6 times improvement in speed.

NeurIPS Conference 2025 Conference Paper

PocketSR: The Super-Resolution Expert in Your Pocket Mobiles

• Haoze Sun
• Linfeng Jiang
• Fan Li
• Renjing Pei
• Zhixin Wang
• Yong Guo
• Jiaqi Xu
• Haoyu Chen

Real-world image super-resolution (RealSR) aims to enhance the visual quality of in-the-wild images, such as those captured by mobile phones. While existing methods leveraging large generative models demonstrate impressive results, the high computational cost and latency make them impractical for edge deployment. In this paper, we introduce PocketSR, an ultra-lightweight, single-step model that brings generative modeling capabilities to RealSR while maintaining high fidelity. To achieve this, we design LiteED, a highly efficient alternative to the original computationally intensive VAE in SD, reducing parameters by 97. 5\% while preserving high-quality encoding and decoding. Additionally, we propose online annealing pruning for the U-Net, which progressively shifts generative priors from heavy modules to lightweight counterparts, ensuring effective knowledge transfer and further optimizing efficiency. To mitigate the loss of prior knowledge during pruning, we incorporate a multi-layer feature distillation loss. Through an in-depth analysis of each design component, we provide valuable insights for future research. PocketSR, with a model size of 146M parameters, processes 4K images in just 0. 8 seconds, achieving a remarkable speedup over previous methods. Notably, it delivers performance on par with state-of-the-art single-step and even multi-step RealSR models, making it a highly practical solution for edge-device applications.

TIST Journal 2024 Journal Article

RANGO: A Novel Deep Learning Approach to Detect Drones Disguising from Video Surveillance Systems

• Jin Han
• Yun-Feng Ren
• Alessandro Brighente
• Mauro Conti

Video surveillance systems provide means to detect the presence of potentially malicious drones in the surroundings of critical infrastructures. In particular, these systems collect images and feed them to a deep-learning classifier able to detect the presence of a drone in the input image. However, current classifiers are not efficient in identifying drones that disguise themselves with the image background, e.g., hiding in front of a tree. Furthermore, video-based detection systems heavily rely on the image’s brightness, where darkness imposes significant challenges in detecting drones. Both these phenomena increase the possibilities for attackers to get close to critical infrastructures without being spotted and hence be able to gather sensitive information or cause physical damages, possibly leading to safety threats. In this article, we propose RANGO, a drone detection arithmetic able to detect drones in challenging images where the target is difficult to distinguish from the background. RANGO is based on a deep learning architecture that exploits a Preconditioning Operation (PREP) that highlights the target by the difference between the target gradient and the background gradient. The idea is to highlight features that will be useful for classification. After PREP, RANGO uses multiple convolution kernels to make the final decision on the presence of the drone. We test RANGO on a drone image dataset composed of multiple already-existing datasets to which we add samples of birds and planes. We then compare RANGO with multiple currently existing approaches to show its superiority. When tested on images with disguising drones, RANGO attains an increase of 6.6% mean Average Precision (mAP) compared to YOLOv5 solution. When tested on the conventional dataset, RANGO improves the mAP by approximately 2.2%, thus confirming its effectiveness also in the general scenario.

AAAI Conference 2020 Conference Paper

Reborn Filters: Pruning Convolutional Neural Networks with Limited Data

• Yehui Tang
• Shan You
• Chang Xu
• Jin Han
• Chen Qian
• Boxin Shi
• Chao Xu
• Changshui Zhang

Channel pruning is effective in compressing the pretrained CNNs for their deployment on low-end edge devices. Most existing methods independently prune some of the original channels and need the complete original dataset to fix the performance drop after pruning. However, due to commercial protection or data privacy, users may only have access to a tiny portion of training examples, which could be insufficient for the performance recovery. In this paper, for pruning with limited data, we propose to use all original filters to directly develop new compact filters, named reborn filters, so that all useful structure priors in the original filters can be well preserved into the pruned networks, alleviating the performance drop accordingly. During training, reborn filters can be easily implemented via 1 × 1 convolutional layers and then be fused in the inference stage for acceleration. Based on reborn filters, the proposed channel pruning algorithm shows its effectiveness and superiority on extensive experiments.

NeurIPS Conference 2020 Conference Paper

UnModNet: Learning to Unwrap a Modulo Image for High Dynamic Range Imaging

• Chu Zhou
• Hang Zhao
• Jin Han
• Chang Xu
• Chao Xu
• Tiejun Huang
• Boxin Shi

A conventional camera often suffers from over- or under-exposure when recording a real-world scene with a very high dynamic range (HDR). In contrast, a modulo camera with a Markov random field (MRF) based unwrapping algorithm can theoretically accomplish unbounded dynamic range but shows degenerate performances when there are modulus-intensity ambiguity, strong local contrast, and color misalignment. In this paper, we reformulate the modulo image unwrapping problem into a series of binary labeling problems and propose a modulo edge-aware model, named as UnModNet, to iteratively estimate the binary rollover masks of the modulo image for unwrapping. Experimental results show that our approach can generate 12-bit HDR images from 8-bit modulo images reliably, and runs much faster than the previous MRF-based algorithm thanks to the GPU acceleration.