8 accepted papers
Fine-grained remote sensing datasets often use hierarchical label structures to differentiate objects in a coarse-to-fine manner, with each object annotated across multiple levels. However, embedding this semantic hierarchy into the representation learning space to improve fine-grained detection per
Hierarchical classification (HC) on degraded images presents challenges due to feature corruption, unreliable confidence estimation, and fine-grained misclassification. Existing methods often struggle to balance semantic consistency and adaptive decision paths under low-quality visual conditions. To
Incremental learning is a strategy that continuously incorporates new data to tackle emerging tasks without the need for retraining the model. While effective, it encounters the challenge of catastrophic forgetting. Hierarchical Classification (HC) enhances classification accuracy and efficiency by…
Deep learning (DL)-based hyperspectral image (HSI) denoising has achieved remarkable accomplishments but is still limited by insufficient training data of noisy-clean pairs. This paper proposes a novel approach to enhance the diffusion model (DM)-based HSI denoising by leveraging abundant RGB images…
Hierarchical multi-granularity classification (HMC) assigns hierarchical multi-granularity labels to each object and focuses on encoding the label hierarchy, e.g., ["Albatross", "Laysan Albatross"] from coarse-to-fine levels. However, the definition of what is fine-grained is subjective, and the ima…
Hyperspectral videos (HSVs) have more potential in target tracking than color videos thanks to the material identification capability provided by abundant spectral bands. Due to limited HSVs for training, most current hyperspectral trackers are based on hand-crafted features rather than deeply learn…
Data-driven deep learning (DL)-based methods directly learn the nonlinear mapping between noisy hyperspectral images (HSIs) and corresponding clean ones. However, DLbased methods neglect the prior knowledge of HSIs embodied by physical models. Consequently, they require complex network architectures…
Hyperspectral unmixing is an important tool to learn the material constitution and distribution of a scene. Model-based unmixing methods depend on well-designed iterative optimization algorithms, which is usually time consuming. Learning-based methods perform unmixing in a data-driven manner but hea…