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Junjiang Wu

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AAAI Conference 2025 Conference Paper

Incomplete Multi-View Multi-Label Classification via Diffusion-Guided Redundancy Removal

Shilong Ou
Zhe Xue
Lixiong Qin
Yawen Li
Meiyu Liang
Junjiang Wu
Xuyun Zhang
Amin Beheshti

Incomplete multi-view multi-label classification aims to accurately predict labels for each sample in the face of some missing views. Due to its widespread presence in real-world scenarios, it has become an extensively researched topic. In addition to the challenges brought by missing views, it also encounters issues caused by redundant views, whose inclusion fails to make a positive contribution to performance. In this paper, we make the first attempt to take advantage of diffusion models to address the missing view problem and design a strategy to identify and remove redundant views. Specifically, we train a diffusion model conditioned on the pseudo-labels to recover information of missing views. The learned diffusion model can carry data distribution knowledge in training split to the data. Regarding redundant identification strategy, it is designed by considering both the additional information of views and the classification difficulty level of samples, thereby adaptively identifying and removing redundant views. We conduct extensive experiments on five datasets, and the proposed method achieves favorable performance against several state-of-the-art methods on the multi-view multi-label classification task.

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EAAI Journal 2025 Journal Article

Multi-stream feature aggregation network with multi-scale supervision for single image dehazing

Junjiang Wu
Haibo Tao
Kai Xiao
Jun Chu
Lu Leng

Single image dehazing is a challenge, as it requires to eliminate the haze while preserving image quality. Most existing models use encoder–decoder or single-scale structures. Owing to flaws of architectural design, these models fail to capture spatial details and semantic contexts in a complementary manner, resulting in inaccurately detailed recovery and suboptimal dehazing performance. To address these issues, we propose a novel framework known as the Multi-stream Feature Aggregation Network (MSFANet), which leverages and excavates the information from different scales of input images and fuses different levels of features. In addition, we design a Self-adjustable Complementary Features Selection Module (SCFSM) that efficiently selects and aggregates the features with different scales from multiple streams, enabling the full exchange of information between different levels of features within the network. Moreover, during the training stage, we introduce a Fast Fourier Transform (FFT) loss combined with pixel loss to simultaneously supervise the reconstruction process in the spatial and frequency domains with a multi-scale supervision strategy. Extensive experiments on homogeneous and nonhomogeneous datasets show that the proposed MSFANet achieves state-of-the-art dehazing performance. Specifically, our method dramatically boosts the Peak Signal-to-Noise Ratio (PSNR) metric to 42. 47 dB on the commonly used Synthetic Objective Testing Set (SOTS) indoor dataset.

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