Jinli Suo

Infrared–visible image fusion leverages the complementary strengths of both modalities to enhance visual perception in challenging environments. While image-level fusion has achieved promising results, extending it to video remains challenging due to temporal illumination variations, brightness flickering, and visual inconsistency caused by motion under non-uniform illumination. To address these issues, we propose Seq-IF, a sequential fusion framework that ensures visual consistency and structural fidelity when processing video sequences. The framework comprises a static–dynamic decoupling module for robust foreground–background separation. For the background, fusion is performed by selecting the frame with the highest contrast to ensure clarity and stability. For the dynamic objects, the pixel intensity is adaptively adjusted across consecutive frames by a lightweight MLP-based illumination-consistency fine-tuning module that performs online adaptation and dynamically optimizes brightness in response to scene changes. Later we introduce a spatial–frequency fusion module integrating multi-scale encoder and edge-guided decoder to ensure structural consistency. Extensive experiments demonstrate that the fusion results produced by Seq-IF outperform baseline methods in terms of clarity, detail preservation, and illumination stability, achieving smoother temporal transitions and enhanced perceptual quality. Furthermore, the effectiveness of Seq-IF is validated on downstream tasks, including object detection and optical flow estimation, highlighting its applicability to real-world scenarios.

The explosive growth of medical data poses significant challenges for storage and sharing. Current compression techniques utilizing Implicit Neural Representations (INRs) effectively strike a balance between encoding accuracy and compression ratio, yet they suffer from slow encoding speeds. By contrast, data-driven compressors encode fast but heavily rely on the training data and cannot generalize well. To develop a practical compression tool overcoming all these limitations, we introduce Shape-Texture Decoupled Compression (DeepSTD), which focuses on the data set of the same modality and body parts and proposes decoupling the variations into shape and texture components for separate encoding. Disentangling two components facilitates designing proper encoding strategies suitable for their respective characteristics—swift shape encoding based on INRs and effective data-driven texture encoding. The proposed approach combines the advantages of INR-based and data-driven models, to achieve high fidelity, fast encoding speed, as well as good generalizability. Comprehensive evaluations on large-scale Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) datasets demonstrate superior performance across encoding quality, compression ratio, and speed. Besides, with features like parallel acceleration with multiple Graphics Processing Units (multi-GPU), flexible control of compression ratio, and broad applicability, DeepSTD offers a robust and efficient solution for the pressing demands of modern medical data compression.

Explainability is essential for neural networks that model long time series, yet most existing explainable AI methods only produce point-wise importance scores and fail to capture temporal structures such as trends, cycles, and regime changes. This limitation weakens human interpretability and trust in long-horizon models. To address these issues, we identify four key requirements for interpretable time-series modeling: temporal continuity, pattern-centric explanation, causal disentanglement, and faithfulness to the model's inference process. We propose EXCAP, a unified framework that satisfies all four requirements. EXCAP combines an attention-based segmenter that extracts coherent temporal patterns, a causally structured decoder guided by a pre-trained causal graph, and a latent aggregation mechanism that enforces representation stability. Our theoretical analysis shows that EXCAP provides smooth and stable explanations over time and is robust to perturbations in causal masks. Extensive experiments on classification and forecasting benchmarks demonstrate that EXCAP achieves strong predictive accuracy while generating coherent and causally grounded explanations. These results show that EXCAP offers a principled and scalable approach to interpretable modeling of long time series with relevance to high-stakes domains such as healthcare and finance.

The thermal camera excels at perceiving outdoor environments under low-light conditions, making it ideal for applications such as nighttime autonomous driving and unmanned navigation. However, thermal cameras encounter challenges when capturing signage from objects made of similar materials, which can pose safety risks for accurately understanding semantics in autonomous driving systems. In contrast, the neuromorphic vision camera, also known as an event camera, detects changes in light intensity asynchronously and has proven effective in high-speed, low-light traffic environments. Recognizing the complementary characteristics of these two modalities, this paper proposes UTA-Sign, an unsupervised thermal-event video augmentation for traffic signage in low-illumination environments, targeting elements such as license plates and roadblock indicators. To address the signage blind spots of thermal imaging and the non-uniform sampling of event cameras, we developed a dual-boosting mechanism that fuses thermal frames and event signals for consistent signage representation over time. The proposed method utilizes thermal frames to provide accurate motion cues as temporal references for aligning the uneven event signals. At the same time, event signals contribute subtle signage content to the raw thermal frames, enhancing the overall understanding of the environment. The proposed method is validated on datasets collected from real-world scenarios, demonstrating superior quality in traffic signage sketching and improved detection accuracy at the perceptual level.