9 accepted papers
Label Distribution Learning (LDL) is a groundbreaking paradigm for addressing the task with label ambiguity. Subjectivity in annotating label description degrees often leads to imbalanced label distribution. Existing approaches either adopt representation alignment or decoupling strategies to solve
Label Distribution Learning (LDL) represents each instance with a label distribution, but these distributions are often incomplete in practice due to high annotation costs and annotators’ cognitive burden. Recent Incomplete Label Distribution Learning (InLDL) methods leverage global and local correl
Hyperspectral super-resolution involves combining low-resolution hyperspectral images with high-resolution multispectral images to produce a high-resolution hyperspectral image. Recently, although many methods for hyperspectral image super-resolution have been proposed, they often fail to fully util…
The goal of fusing hyperspectral images (HSI) and multispectral images (MSI) is to generate high-resolution hyperspectral images for downstream tasks. However, most existing methods overlook the specific requirements of these tasks, leading to a gap between the fusion process and its subsequent appl…
Co-salient object detection (CoSOD) aims to detect common salient objects across multiple related images. However, existing methods often struggle with limited attention coverage, missing some co-salient objects. To address this, we propose a two-stage feature processing module (FPM) comprising comp…
Label Distribution Learning (LDL) has been successfully implemented in numerous practical applications. However, the imbalance in label distributions presents a significant challenge due to the substantial variation in annotation information. To tackle this issue, we introduce Decoupled Imbalance La
Few-shot learning (FSL) has gained increasing attention in hyperspectral image (HSI) classification due to its ability to perform cross-domain classification with minimal labeled samples. However, existing FSL methods overlook the continuity of HSI spectral sequences and fail to utilize the large am…
Multimodal image fusion aims to merge features from different modalities to create a comprehensively representative image. However, existing medical image fusion methods often struggle to handle noise generated during image acquisition, significantly diminishing their impact on visual quality. To ad…
The purpose of infrared and visible image fusion is to extract and combine information from source images to produce results that contain vital and complementary information. Existing fusion rules may not extract the most useful information and cannot effectively retain important information. To sol…