Object Segmentation
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Benchmarks
GRIT
Most implemented
Mask R-CNN
Filtering-out poor-quality images for data preparation
Augmenting Deep Classifiers with Polynomial Neural Networks
DeepCut: Unsupervised Segmentation using Graph Neural Networks Clustering
Papers
Filtering-out poor-quality images for data preparation
Filtering noise is a fundamental part of data preparation that enhances image quality for applications such as object segmentation, detection, and recognition. Various noise reduction techniques are proposed in the liter…
Image Quality AssessmentTraffic Sign RecognitionObject SegmentationAutonomous VehiclesEvidence-Backed Video Question Answering
Current Video Large Language Models (Video LLMs) excel in question answering (QA) but largely operate as black boxes, providing textual answers without verifiable visual grounding. Existing explainability efforts rely on…
Video Question AnsweringObject SegmentationVisual GroundingA Large-Scale Dataset and a New Method for RemoteSensing Traffic Object Segmentation
Remote sensing imagery plays a crucial role in evaluating regional transportation capacity. However, existing segmentation datasets often lack diversity in object categories and scenes, limiting the ability of models to …
Object SegmentationSUMO: Segment and Track Any Motion with Nonlinear State Space Models
Visual Object Tracking (VOT) and Moving Object Segmentation (MOS) are two fundamental tasks in computer vision that involve both spatial and temporal object dynamics. Existing methods rely predominantly on visual cues an…
Visual Object TrackingObject SegmentationDCSNet: Multiscale Feature Aggregation for Small Medical Object Segmentation with Detection-guided Hierarchical Cropping
Small object segmentation in medical imaging is primarily hindered by class imbalance and inherent boundary complexity. Consequently, conventional global networks frequently fail to detect sparse targets or suffer from s…
Object SegmentationLesion SegmentationFrom Reconstruction to Decision: A Post-Encoder Plug-in Adapter for Curvilinear Segmentation
Curvilinear object segmentation, including vessels and cracks, is challenging due to extreme spatial sparsity and topological fragility, where small local errors can cause severe structural disconnections. Meanwhile, mod…
Object Segmentation