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Rui Shi

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4 papers

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JBHI Journal 2025 Journal Article

HRProtoKD: A hierarchical and relational prototype based knowledge distillation framework for few-shot cancer molecular subtyping

Fei Guo
Rui Shi
Jia Zhou
Junlin Xu
Hui Cui
Ping Xuan
Xikang Feng
Leyi Wei

Accurate molecular subtyping of cancers is critical for drug discovery and disease treatment but re-mains challenging due to the scarcity of labeled data and the intrinsic heterogeneity of cancer biology. While current methods address this issue via few-shot learning (FSL), they often overlook the hierarchical relation-ships among cancer subtypes and the transfer of relational knowledge between models, both of which are crucial for improving generalization under limited supervision. In this work, we propose HRProtoKD, a novel hierarchical and relational prototype-based knowledge distillation framework designed for few-shot cancer subtype classification. HRProtoKD first employs hierarchical inter-class prototype clustering to capture the underlying class relationships. A prototype-wise contrastive loss is then introduced to enhance intra-class compactness and interclass separability. Furthermore, relational prototype knowledge distillation is applied to transfer structural knowledge from a teacher model to a student model. In addition, we construct three benchmark datasets for few-shot cancer molecular subtyping. Extensive experiments conducted on these datasets demonstrate that HRProtoKD consistently outperforms state-of-the-art meta-learning baselines under both 1-shot and 5-shot learning settings, thereby advancing precision oncology and supporting data-driven approaches for drug discovery and therapeutic development.

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AAAI Conference 2025 Conference Paper

InstantSticker: Realistic Decal Blending via Disentangled Object Reconstruction

Yi Zhang
Xiaoyang Huang
Yishun Dou
Yue Shi
Rui Shi
Ye Chen
Bingbing Ni
Wenjun Zhang

We present InstantSticker, a disentangled reconstruction pipeline based on Image-Based Lighting (IBL), which focuses on highly realistic decal blending, simulates stickers attached to the reconstructed surface, and allows for instant editing and real-time rendering. To achieve stereoscopic impression of the decal, we introduce shadow factor into IBL, which can be adaptively optimized during training. This allows the shadow brightness of surfaces to be accurately decomposed rather than baked into the diffuse color, ensuring that the edited texture exhibits authentic shading. To address the issues of warping and blurriness in previous methods, we apply As-Rigid-As-Possible (ARAP) parameterization to pre-unfold a specified area of the mesh and use the local UV mapping combined with a neural texture map to enhance the ability to express high-frequency details in that area. For instant editing, we utilize the Disney BRDF model, explicitly defining material colors with 3-channel diffuse albedo. This enables instant replacement of albedo RGB values during the editing process, avoiding the prolonged optimization required in previous approaches. In our experiment, we introduce the Ratio Variance Warping (RVW) metric to evaluate the local geometric warping of the decal area. Extensive experimental results demonstrate that our method surpasses previous decal blending methods in terms of editing quality, editing speed and rendering speed, achieving the state-of-the-art.

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AAAI Conference 2025 Conference Paper

Neural Block Compression: Variable Bitrates Feature Blocks for Texture Representation

Rui Shi
Yishun Dou
Zhong Zheng
Xiangzhong Fang
Wenjun Zhang
Bingbing Ni

The imperative for compression of material textures emerges from the critical demand for high-quality rendering, which necessitates sophisticated textures that, in turn, require substantial storage and memory resources. Thus, low-bitrate compression is crucial, especially in modern games demanding higher texture resolutions. Concurrent methodologies in texture compression predominantly employ a block-based paradigm based on color space, which inevitably leads to representational redundancies and a limited compression scope, particularly at lower bitrates. In the context of mobile devices, bandwidth during texture loading and runtime memory are major bottlenecks, making existing compression algorithms inadequate for high-resolution textures. To mitigate these limitations, we propose a novel multi-resolution texture compression scheme, Neural Block Compression (NBC), developed within the neural feature domain. Our encoding scheme is constructed on a hierarchy of multi-resolution neural feature blocks, and the key ingredient is the variable bitrates quantization scheme. It allocates higher bitrates to higher feature mip-levels and lower bitrates to lower feature mip-levels, thereby extending the concept of block compression from color domain into neural feature domain. Extensive experiments demonstrate the superior texture compression quality achieved by the proposed scheme, especially at low bitrates.

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EAAI Journal 2024 Journal Article

Lightweight network based features fusion for steel rolling ambient sound classification

Rui Shi
Fei Zhang
YanJiao Li

With the intelligent development of industrial production, sound monitoring technology has been widely used to monitor the operation status of mechanical facilities, and this progress has gradually become a research hotspot in the steel manufacturing industry. However, the complex composition of sound sources and the high computational requirements of most models limit their applicability to industrial scenarios. This work presents a targeted approach for remote monitoring of rolling sound. They are using visual features of audio signals to design classification models on the self-harvested dataset for more efficient adaptation to complex production sites. An in-depth analysis of the actual sound reveals that it is characterized by high similarity, complexity, and partial synchronization. To optimize interclass and intraclass data engineering few-sample, multi-feature fusion, and data augmentation methods were combined to characterize the details fully. In addition, by leveraging the deep small convolutions of VGGNet and the randomness of stochastic pooling, local features are effectively extracted. Finally, global average pooling with final softmax layer stacking is used to classify the rolling signals to reduce the number of parameters, avoid overfitting, and perform a global analysis of the features. Experimental results on the rolling sound dataset show that the method proposed achieves an accuracy of 91. 26%, with 92. 66%, and 95. 88% on the ESC10 and MIMII datasets, respectively. These results confirm that the method can be widely applied to sound classification for multi-category rolling processes, showing good performance and scalability.

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