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Jie Hou

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4 papers

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AAAI Conference 2026 Conference Paper

Gradient-Protected Value Decomposition for Cooperative Multi-Agent Reinforcement Learning

Jie Hou
Haowen Dou
Lujuan Dang
Liangjun Chen
Chenyang Ge

In recent years, deep multi-agent reinforcement learning (MARL) has demonstrated remarkable potential in solving complex cooperative tasks by enabling decentralized yet efficient coordination among agents. However, during decentralized training, agent policy updates induced by different joint action samples may conflict, leading to gradient interference that hinders convergence and the emergence of coordinated behavior. In this paper, we analyze and empirically validate the phenomenon of gradient interference. To address this, we then propose Gradient-Protected Value Decomposition (GPVD), a novel MARL framework that explicitly protects the gradient signals of optimal collaborative actions by suppressing the impact of interfering actions. GPVD employs a dynamic gradient protection mechanism that identifies optimal collaborative joint actions and reweights the loss to attenuate gradients from non-collaborative interfering actions. To effectively identify high-value collaborative actions, we apply SimHash-based state grouping to discover consistent collaboration patterns across similar states. Furthermore, a count-based intrinsic reward is incorporated to encourage exploration and improve the coverage of potentially optimal joint actions. Experiments on challenging multi-agent benchmarks demonstrate that GPVD achieves faster convergence, stronger coordination, and greater training stability compared to state-of-the-art value decomposition methods.

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EAAI Journal 2026 Journal Article

Progressive deep feature learning network based on fault-aware deformable convolution and its application in railway defect visual inspection

Jiahao Shen
Sheng Xiang
Penghua Li
Jie Hou
Yongfu Li
Yi Qin

Visual inspection has become popular for railway track fault identification. However, the current vision models still have limitations in identifying defects in complex backgrounds due to the insufficient integration of local fault features with global context information. Facing the gap, Progressive Deep Feature Learning Network Based on Fault-Aware Deformable Convolution (PFDCNet) is proposed, which integrates a Residual Network-50 (ResNet-50) branch for capturing local details and an Enhanced Vision Transformer (EVT) branch for global context modeling. In the proposed EVT branch, the Feature Selection (FS) module based on self-attention filters out background noise and generates discriminative multi-scale features through feature map reconstruction, enabling alignment with the ResNet-50 branch. Meanwhile, for defect localization, a Cross-Attention (CA) is introduced in EVT to correlate the classification (CLS) token with ResNet-50 feature maps, locating the key fault-related areas and compensating for the information loss caused by the feature selection. Finally, a Feature Fusion Pyramid Network (FFPN) is proposed to fuse the aligned features from two branches and utilize CA-derived fault regions to guide deformable convolutions through an offset loss, which minimizes the distance between the sampling points and predicted fault centers. Extensive experiments on real railway defect datasets and other three public datasets demonstrate the outperformance of PFDCNet over several state-of-the-art models for practical railway fault monitoring. Our code is presented at https: //github. com/sjh551/PFDCNet.

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AAAI Conference 2025 Conference Paper

FashionTailor: Controllable Clothing Editing for Human Images with Appearance Preserving

Jie Hou
Jianghong Ma
Xiangyu Mu
Haijun Zhang
Zhao Zhang

The garment structure serves as a crucial medium for expressing the designer's creative vision and showcasing the distinctive character of clothing items. Effective editing of garment structure in fashion images allows for an advanced preview of the design, accelerating the process of garment customization to meet individualized requirements. Although large-scale diffusion models have demonstrated impressive image generation and editing capabilities, no efforts have been made to exploit their potential in part-level editing of images. Unlike previous research, we define a clothing structure editing (CSE) task aimed at accurately editing the local structure of human-centered clothing images through simple instruction-based prompts while maintaining the consistency of clothing appearance. Specifically, this paper develops a new controllable triple-flow framework for structure editing named FashionTailor. An additional network called ClothingNet is proposed to extract the clothing details to address the rigid constraints of the original garment structure. Then, we propose a semantic-refined module to extract the semantic understanding of the source image and adaptively focus on the part to be edited. We also design a cross-blend attention mechanism to integrate fine-grained clothing features to guarantee precise alignment between appearance and target structure features. In addition, a garment structure dataset called StructureFashion has been collated, wherein each item of clothing is represented by multiple photos with diverse structure characteristics, containing over six million pairs. Finally, our method supports editing the structure of multiple parts on a garment simultaneously. Extensive experiments validate the effectiveness of our method for editing part-level human images in StructureFashion dataset and real-scenarios.

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IJCAI Conference 2018 Conference Paper

A Brand-level Ranking System with the Customized Attention-GRU Model

Yu Zhu
Junxiong Zhu
Jie Hou
Yongliang Li
Beidou Wang
Ziyu Guan
Deng Cai

In e-commerce websites like Taobao, brand is playing a more important role in influencing users' decision of click/purchase, partly because users are now attaching more importance to the quality of products and brand is an indicator of quality. However, existing ranking systems are not specifically designed to satisfy this kind of demand. Some design tricks may partially alleviate this problem, but still cannot provide satisfactory results or may create additional interaction cost. In this paper, we design the first brand-level ranking system to address this problem. The key challenge of this system is how to sufficiently exploit users' rich behavior in e-commerce websites to rank the brands. In our solution, we firstly conduct the feature engineering specifically tailored for the personalized brand ranking problem and then rank the brands by an adapted Attention-GRU model containing three important modifications. Note that our proposed modifications can also apply to many other machine learning models on various tasks. We conduct a series of experiments to evaluate the effectiveness of our proposed ranking model and test the response to the brand-level ranking system from real users on a large-scale e-commerce platform, i. e. Taobao.

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