SOTAVerified

Video Segmentation

Papers

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Showing 276–300 of 388 papers

TitleStatusHype
DeepPyramid+: Medical Image Segmentation using Pyramid View Fusion and Deformable Pyramid Reception—0
Deep Spatio-Temporal Random Fields for Efficient Video Segmentation—0
Deep Unfolding-Aided Parameter Tuning for Plug-and-Play-Based Video Snapshot Compressive Imaging—0
DeU-Net: Deformable U-Net for 3D Cardiac MRI Video Segmentation—0
Distortion-Aware Network Pruning and Feature Reuse for Real-time Video Segmentation—0
DNN-Driven Compressive Offloading for Edge-Assisted Semantic Video Segmentation—0
Dynamic Face Video Segmentation via Reinforcement Learning—0
Dynamic Video Segmentation Network—0
Efficient Heterogeneous Video Segmentation at the Edge—0
Efficient MRF Energy Propagation for Video Segmentation via Bilateral Filters—0
Efficient Portrait Matte Creation With Layer Diffusion and Connectivity Priors—0
Efficient Video Segmentation Models with Per-frame Inference—0
Efficient Video Segmentation Using Parametric Graph Partitioning—0
Efficient Video Semantic Segmentation with Labels Propagation and Refinement—0
EISeg: An Efficient Interactive Segmentation Tool based on PaddlePaddle—0
End to End Video Segmentation for Driving : Lane Detection For Autonomous Car—0
EntitySAM: Segment Everything in Video—0
Every Frame Counts: Joint Learning of Video Segmentation and Optical Flow—0
Eye Tracking Assisted Extraction of Attentionally Important Objects From Videos—0
Real-Time Segmentation Networks should be Latency Aware—0
Fast Action Proposals for Human Action Detection and Search—0
FlowCut: Unsupervised Video Instance Segmentation via Temporal Mask Matching—0
FOMTrace: Interactive Video Segmentation By Image Graphs and Fuzzy Object Models—0
FoodMem: Near Real-time and Precise Food Video Segmentation—0
Fully Automated 2D and 3D Convolutional Neural Networks Pipeline for Video Segmentation and Myocardial Infarction Detection in Echocardiography—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1GDHFAccuracy86.86—Unverified