Stacked Deconvolutional Network for Semantic Segmentation Introduction Semantic segmentation is a critical task in the field of computer vision, aiming to classify each pixel in an image and predict its category. However, existing Fully Convolutional Networks (FCNs) have limitations in handling spatial resolution, often leading to problems such as ...
FP-Age: Face Parsing Attention Mechanism for Facial Age Estimation in the Wild Research Background Age estimation on facial images is a significant computer vision task with extensive applications in forensics, security, health welfare, and social media. However, due to diverse factors such as head pose, facial expressions, and occlusions, the perf...
TGFuse: A Transformer and Generative Adversarial Network-Based Method for Infrared and Visible Image Fusion Background Introduction With the development of imaging devices and analysis methods, multimodal visual data is rapidly emerging, with many practical applications. In these applications, image fusion plays a significant role in helping the hu...
Unsupervised Temporal Consistency Learning for Consistent Video Object Removal Background and Motivation In the fields of video editing and restoration, Video Object Removal is an essential task with the goal of erasing target objects throughout an entire video, filling the gaps with plausible content. Existing solutions are mainly divided into two...
CLASH: A Gait Recognition Framework Based on Complementary Learning and Neural Architecture Search Research Background Gait recognition is a biometric technology that identifies individuals based on their walking patterns. This technology has widespread applications in security screening, video retrieval, and identity recognition due to its ability...
Transparent Deep Image Aesthetic Assessment Based on Tag Content Descriptions Academic Background With the proliferation of social media platforms such as Instagram and Flickr, there is an increasing demand for Image Aesthetics Assessment (IAA) models. These models can help social network service providers optimize image ranking or recommendation r...
Solving Few-Shot Classification Problems with Balanced Feature Alignment and Uniformity Background and Motivation The goal of Few-Shot Learning (FSL) is to correctly recognize new samples with only a few examples from new classes. Existing few-shot learning methods mainly learn transferable knowledge from base classes by maximizing the information ...
Negative Deterministic Information-Based Multiple Instance Learning for Weakly Supervised Object Detection and Segmentation Background Introduction In the past decade, significant progress has been made in the field of computer vision, particularly in object detection and semantic segmentation. However, most of the designed algorithms and models he...
Information-aware Transformer Unfolding Network for Hyperspectral and Multispectral Image Fusion Background Introduction Hyperspectral images (HSIs) play a crucial role in remote sensing applications such as material identification, image classification, target detection, and environmental monitoring, due to their spectral information across multip...
A Framework for Solving Graph Optimization Problems Based on Graph Neural Networks Background and Research Motivation In solving Constraint Satisfaction Problems (CSPs) and Combinatorial Optimization Problems (COPs), a common method is the combination of backtracking and branch heuristics. Although branch heuristics designed for specific problems a...