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Jiande Sun

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6 papers

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IJCAI Conference 2025 Conference Paper

Multi-Hierarchical Fine-Grained Feature Mapping Driven by Feature Contributions for Molecular Odor Prediction

Hongxin Xie
Jiande Sun
Fanfu Xue
Zifei Han
Shanshan Feng
Qi Chen

Molecular odor prediction involves using a molecule's structure to estimate its odor. While accurate prediction remains challenging, AI models can suggest potential odors. Existing methods, however, often rely on basic descriptors or handcrafted fingerprints, which lack expressive power and hinder effective learning. Furthermore, these methods suffer from severe class imbalance, limiting the training effectiveness of AI models. To address these challenges, we propose a Feature Contribution-driven Hierarchical Multi-Feature Mapping Network (HMFNet). Specifically, we introduce a fine-grained, Local Multi-Hierarchy Feature Extraction module (LMFE) that performs deep feature extraction at the atomic level, capturing detailed features crucial for odor prediction. To enhance the extraction of discriminative atomic features, we integrate a Harmonic Modulated Feature Mapping (HMFM). This module dynamically learns feature importance and frequency modulation, improving the model's capability to capture relevant patterns. Additionally, a Global Multi-Hierarchy Feature Extraction module (GMFE) is designed to learn global features from the molecular graph topology, enabling the model to fully leverage global information and enhance its discriminative power for odor prediction. To further mitigate the issue of class imbalance, we propose a Chemically-Informed Loss (CIL). Experimental results demonstrate that our approach significantly improves performance across various deep learning models, highlighting its potential to advance molecular structure representation and accelerate the development of AI-driven technologies.

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IJCAI Conference 2025 Conference Paper

Multimodal Inverse Attention Network with Intrinsic Discriminant Feature Exploitation for Fake News Detection

Tianlin Zhang
En Yu
Yi Shao
Jiande Sun

Multimodal fake news detection has garnered significant attention due to its profound implications for social security. While existing approaches have contributed to understanding cross-modal consistency, they often fail to leverage modal-specific representations and explicit discrepant features. To address these limitations, we propose a Multimodal Inverse Attention Network (MIAN), a novel framework that explores intrinsic discriminative features based on news content to advance fake news detection. Specifically, MIAN introduces a hierarchical learning module that captures diverse intra-modal relationships through local-to-global and local-to-local interactions, thereby generating enhanced unimodal representations to improve the identification of fake news at the intra-modal level. Additionally, a cross-modal interaction module employs a co-attention mechanism to establish and model dependencies between the refined unimodal representations, facilitating seamless semantic integration across modalities. To explicitly extract inconsistency features, we propose an inverse attention mechanism that effectively highlights the conflicting patterns and semantic deviations introduced by fake news in both intra- and inter-modality. Extensive experiments on benchmark datasets demonstrate that MIAN significantly outperforms state-of-the-art methods, underscoring its pivotal contribution to advancing social security through enhanced multimodal fake news detection.

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IJCAI Conference 2025 Conference Paper

PerfSeer: An Efficient and Accurate Deep Learning Models Performance Predictor

Xinlong Zhao
Jiande Sun
Jia Zhang
Tong Liu
Ke Liu

Predicting the performance of deep learning (DL) models, such as execution time and resource utilization, is crucial for Neural Architecture Search (NAS), DL cluster schedulers, and other technologies that advance deep learning. The representation of a model is the foundation for its performance prediction. However, existing methods cannot comprehensively represent diverse model configurations, resulting in unsatisfactory accuracy. To address this, we represent a model as a graph that includes the topology, along with node, edge, and global features, all of which are crucial for effectively capturing the performance of the model. Based on this representation, we propose PerfSeer, a novel predictor that uses a Graph Neural Network (GNN)-based performance prediction model, SeerNet. SeerNet fully leverages the topology and various features, while incorporating optimizations such as Synergistic Max-Mean aggregation (SynMM) and Global-Node Perspective Boost (GNPB) to more effectively capture the critical performance information, enabling it to predict the performance of models accurately. Furthermore, SeerNet can be extended to SeerNet-Multi by using Project Conflicting Gradients (PCGrad), enabling efficient simultaneous prediction of multiple performance metrics without significantly affecting accuracy. We constructed a dataset containing performance metrics for 53k+ model configurations, including execution time, memory usage, and Streaming Multiprocessor (SM) utilization during both training and inference. The evaluation results show that PerfSeer outperforms nn-Meter, Brp-NAS, and DIPPM.

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EAAI Journal 2023 Journal Article

AFcIHNet: Attention feature-constrained network for single image information hiding

Xingwang Jia
Huamei Xin
Lingchen Gu
Hao Wang
Jiande Sun
Wenbo Wan

Single image information hiding aims to obtain a stegano image by hiding a secret image within a cover image. Recently, deep neural network (DNN) based information hiding methods have been advanced extensively. However, it is difficult for them to preserve the spatial information at different scales on each channel feature map in the stegano image, largely because of a lack of effective features and feature fusion. In this paper, a bi-channel attention feature-constrained pixel-shuffle information hiding network (AFcIHNet) is proposed to efficiently enhance the detailed spatial information in the stegano image. First, the invertible neural network (INN) is employed for the information hiding task, which is adaptively constrained by the proposed bi-channel attention module (Bi-AM). The capability of the Bi-AM module is further enhanced by the introduction of a novel attention loss function. In addition, to take full advantage of the attention based feature fusion mechanism that combines the global and local contexts of the features, we used the Squeeze and Concat (SPC) module with adaptive branching factors, the model can hierarchically approach the input feature map from local to global. Extensive experiments on the DIV2K and COCO datasets show that the proposed method can produce competitive stegano images when compared with some state-of-the-art methods. Finally, we can get a secret image that is closer to the original image. Peak signal-to-noise ratio (PSNR), structural similarity (SSIM) and average pixel difference (APD) were used to evaluate the experimental effects of cover and stegano images as well as secret and recovered secret images. The experimental results show that the obtained stegano and cover images are more indistinguishable, and the recovered secret image is closer to the original image. Overall, the overall performance is improved by nearly 0. 8% compared to the existing baseline model.

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IJCAI Conference 2022 Conference Paper

Robust Single Image Dehazing Based on Consistent and Contrast-Assisted Reconstruction

De Cheng
Yan Li
Dingwen Zhang
Nannan Wang
Xinbo Gao
Jiande Sun

Single image dehazing as a fundamental low-level vision task, is essential for the development of robust intelligent surveillance system. In this paper, we make an early effort to consider dehazing robustness under variational haze density, which is a realistic while under-studied problem in the research filed of singe image dehazing. To properly address this problem, we propose a novel density-variational learning framework to improve the robustness of the image dehzing model assisted by a variety of negative hazy images, to better deal with various complex hazy scenarios. Specifically, the dehazing network is optimized under the consistency-regularized framework with the proposed Contrast-Assisted Reconstruction Loss (CARL). The CARL can fully exploit the negative information to facilitate the traditional positive-orient dehazing objective function, by squeezing the dehazed image to its clean target from different directions. Meanwhile, the consistency regularization keeps consistent outputs given multi-level hazy images, thus improving the model robustness. Extensive experimental results on two synthetic and three real-world datasets demonstrate that our method significantly surpasses the state-of-the-art approaches.

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AAAI Conference 2020 Conference Paper

Multi-Feature Discrete Collaborative Filtering for Fast Cold-Start Recommendation

Yang Xu
Lei Zhu
Zhiyong Cheng
Jingjing Li
Jiande Sun

Hashing is an effective technique to address the largescale recommendation problem, due to its high computation and storage efﬁciency on calculating the user preferences on items. However, existing hashing-based recommendation methods still suffer from two important problems: 1) Their recommendation process mainly relies on the user-item interactions and single speciﬁc content feature. When the interaction history or the content feature is unavailable (the cold-start problem), their performance will be seriously deteriorated. 2) Existing methods learn the hash codes with relaxed optimization or adopt discrete coordinate descent to directly solve binary hash codes, which results in signiﬁcant quantization loss or consumes considerable computation time. In this paper, we propose a fast cold-start recommendation method, called Multi-Feature Discrete Collaborative Filtering (MFDCF), to solve these problems. Speciﬁcally, a lowrank self-weighted multi-feature fusion module is designed to adaptively project the multiple content features into binary yet informative hash codes by fully exploiting their complementarity. Additionally, we develop a fast discrete optimization algorithm to directly compute the binary hash codes with simple operations. Experiments on two public recommendation datasets demonstrate that MFDCF outperforms the stateof-the-arts on various aspects.

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