Papers Feature Correlation
“Feature Correlation” 태그가 달린 논문 124편 · 필터 해제
Adaptive Assignment for Geometry Aware Local Feature Matching
The detector-free feature matching approaches are currently attracting great attention thanks to their excellent performance. However, these methods still struggle at large-scale and viewpoint variations, due to the geom…
Feature CorrelationFeature Overcorrelation in Deep Graph Neural Networks: A New Perspective
Recent years have witnessed remarkable success achieved by graph neural networks (GNNs) in many real-world applications such as recommendation and drug discovery. Despite the success, oversmoothing has been identified as…
Drug DiscoveryFeature CorrelationInterpretability Guarantees with Merlin-Arthur Classifiers
We propose an interactive multi-agent classifier that provides provable interpretability guarantees even for complex agents such as neural networks. These guarantees consist of lower bounds on the mutual information betw…
Feature CorrelationCorrelation Verification for Image Retrieval
Geometric verification is considered a de facto solution for the re-ranking task in image retrieval. In this study, we propose a novel image retrieval re-ranking network named Correlation Verification Networks (CVNet). O…
Feature CorrelationGeometric MatchingImage RetrievalRe-Ranking+1MRI-based Multi-task Decoupling Learning for Alzheimer's Disease Detection and MMSE Score Prediction: A Multi-site Validation
Accurately detecting Alzheimer's disease (AD) and predicting mini-mental state examination (MMSE) score are important tasks in elderly health by magnetic resonance imaging (MRI). Most of the previous methods on these two…
Alzheimer's Disease DetectionFeature CorrelationMulti-Task LearningRepresentation LearningInteractive Multi-Class Tiny-Object Detection
Annotating tens or hundreds of tiny objects in a given image is laborious yet crucial for a multitude of Computer Vision tasks. Such imagery typically contains objects from various categories, yet the multi-class interac…
Feature CorrelationObjectobject-detectionObject DetectionCorrelation-Aware Deep Tracking
Robustness and discrimination power are two fundamental requirements in visual object tracking. In most tracking paradigms, we find that the features extracted by the popular Siamese-like networks cannot fully discrimina…
Feature CorrelationObject TrackingVisual Object TrackingImproved Dual Correlation Reduction Network
Deep graph clustering, which aims to reveal the underlying graph structure and divide the nodes into different clusters without human annotations, is a fundamental yet challenging task. However, we observed that the exis…
ClusteringFeature CorrelationGraph ClusteringGuide Local Feature Matching by Overlap Estimation
Local image feature matching under large appearance, viewpoint, and distance changes is challenging yet important. Conventional methods detect and match tentative local features across the whole images, with heuristic co…
Feature CorrelationReconFormer: Accelerated MRI Reconstruction Using Recurrent Transformer
Accelerating magnetic resonance image (MRI) reconstruction process is a challenging ill-posed inverse problem due to the excessive under-sampling operation in k-space. In this paper, we propose a recurrent transformer mo…
Feature CorrelationMRI ReconstructionChange Detection from Synthetic Aperture Radar Images via Graph-Based Knowledge Supplement Network
Synthetic aperture radar (SAR) image change detection is a vital yet challenging task in the field of remote sensing image analysis. Most previous works adopt a self-supervised method which uses pseudo-labeled samples to…
Change DetectionFeature CorrelationLightweight Salient Object Detection in Optical Remote Sensing Images via Feature Correlation
Salient object detection in optical remote sensing images (ORSI-SOD) has been widely explored for understanding ORSIs. However, previous methods focus mainly on improving the detection accuracy while neglecting the cost …
Feature Correlationobject-detectionObject DetectionSalient Object DetectionEfficient Non-Local Contrastive Attention for Image Super-Resolution
Non-Local Attention (NLA) brings significant improvement for Single Image Super-Resolution (SISR) by leveraging intrinsic feature correlation in natural images. However, NLA gives noisy information large weights and cons…
Contrastive LearningFeature CorrelationImage Super-ResolutionSuper-ResolutionEfficient Single Image Super-Resolution Using Dual Path Connections with Multiple Scale Learning
Deep convolutional neural networks have been demonstrated to be effective for SISR in recent years. On the one hand, residual connections and dense connections have been used widely to ease forward information and backwa…
Feature CorrelationImage Super-ResolutionSuper-ResolutionDeep Graph Clustering via Dual Correlation Reduction
Deep graph clustering, which aims to reveal the underlying graph structure and divide the nodes into different groups, has attracted intensive attention in recent years. However, we observe that, in the process of node e…
ClusteringFeature CorrelationGraph ClusteringNeRD++: Improved 3D-mirror symmetry learning from a single image
Many objects are naturally symmetric, and this symmetry can be exploited to infer unseen 3D properties from a single 2D image. Recently, NeRD is proposed for accurate 3D mirror plane estimation from a single image. Despi…
Feature CorrelationInductive BiasSymmetry DetectionDenseGAP: Graph-Structured Dense Correspondence Learning with Anchor Points
Establishing dense correspondence between two images is a fundamental computer vision problem, which is typically tackled by matching local feature descriptors. However, without global awareness, such local features are …
Feature CorrelationObject Counting: You Only Need to Look at One
This paper aims to tackle the challenging task of one-shot object counting. Given an image containing novel, previously unseen category objects, the goal of the task is to count all instances in the desired category with…
Feature CorrelationObjectObject CountingTransMVSNet: Global Context-aware Multi-view Stereo Network with Transformers
In this paper, we present TransMVSNet, based on our exploration of feature matching in multi-view stereo (MVS). We analogize MVS back to its nature of a feature matching task and therefore propose a powerful Feature Matc…
3D ReconstructionFeature CorrelationHow Do Neural Sequence Models Generalize? Local and Global Context Cues for Out-of-Distribution Prediction
After a neural sequence model encounters an unexpected token, can its behavior be predicted? We show that RNN and transformer language models exhibit structured, consistent generalization in out-of-distribution contexts.…
Feature Correlation