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...
Weakly Supervised Semantic Image Segmentation via Alternate Self-Dual Teaching Background Introduction With the continuous development of the computer vision field, semantic segmentation has become an important and active research direction. Traditional semantic segmentation methods rely on manually labeled pixel-level tags; however, obtaining thes...
Background In recent years, Reinforcement Learning (RL) has demonstrated its effectiveness in solving various complex tasks. However, many real-world decision-making and control problems involve multiple conflicting objectives. The relative importance (preference) of these objectives often needs to be balanced against each other in different scenar...
GMConv: Adjusting the Effective Receptive Field of Convolutional Neural Networks Introduction Convolutional Neural Networks (CNNs) have achieved significant success in computer vision tasks, including image classification and object detection, through the use of convolutional kernels. However, in recent years, Vision Transformers (ViTs) have gained...