Local Feature
Local feature extraction in computer vision and related fields aims to identify and represent salient, localized information within data, such as images or point clouds, enabling tasks like object recognition, image matching, and segmentation. Current research emphasizes improving the robustness and efficiency of local feature extraction, often employing convolutional neural networks (CNNs), transformers, and hybrid architectures like Mamba, to capture both local and global contextual information. These advancements are crucial for enhancing the accuracy and interpretability of machine learning models across diverse applications, including medical image analysis, remote sensing, and robotics.
Papers
Local Feature Selection without Label or Feature Leakage for Interpretable Machine Learning Predictions
Harrie Oosterhuis, Lijun Lyu, Avishek Anand
Learning Global and Local Features of Power Load Series Through Transformer and 2D-CNN: An Image-based Multi-step Forecasting Approach Incorporating Phase Space Reconstruction
Zihan Tang, Tianyao Ji, Wenhu Tang
Local positional graphs and attentive local features for a data and runtime-efficient hierarchical place recognition pipeline
Fangming Yuan, Stefan Schubert, Peter Protzel, Peer Neubert
Shifting Focus: From Global Semantics to Local Prominent Features in Swin-Transformer for Knee Osteoarthritis Severity Assessment
Aymen Sekhri, Marouane Tliba, Mohamed Amine Kerkouri, Yassine Nasser, Aladine Chetouani, Alessandro Bruno, Rachid Jennane