SOTAVerified

Scene Understanding

Scene understanding involves interpreting the visual information of a scene, including objects, their spatial relationships, and the overall layout. It goes beyond simple object recognition by considering the context and how objects relate to each other and the environment.

Papers

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Showing 1271–1280 of 1723 papers

TitleStatusHype
Unsupervised Image Segmentation by Mutual Information Maximization and Adversarial Regularization—0
An Analysis of State-of-the-Art Models for Situated Interactive MultiModal Conversations (SIMMC)—0
IMENet: Joint 3D Semantic Scene Completion and 2D Semantic Segmentation through Iterative Mutual Enhancement—0
False Negative Reduction in Video Instance Segmentation using Uncertainty EstimatesCode0
SDOF-Tracker: Fast and Accurate Multiple Human Tracking by Skipped-Detection and Optical-FlowCode0
OffRoadTranSeg: Semi-Supervised Segmentation using Transformers on OffRoad environments—0
iReason: Multimodal Commonsense Reasoning using Videos and Natural Language with Interpretability—0
OpenRooms: An Open Framework for Photorealistic Indoor Scene Datasets—0
Feature-Level Collaboration: Joint Unsupervised Learning of Optical Flow, Stereo Depth and Camera Motion—0
Towards urban scenes understanding through polarization cues—0
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All datasetsSemantic Scene Understanding Challenge (passive actuation & ground-truth localisation)ADE20K valSemantic Scene Understanding Challenge (active actuation & ground-truth localisation)

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ACRV BaselineOMQ0.44—Unverified
2Team VGAI (TCS Research)OMQ0.37—Unverified
3Demo_semantic_SLAMOMQ0.11—Unverified
#ModelMetricClaimedVerifiedStatus
1CPN(ResNet-101)Mean IoU46.3—Unverified
#ModelMetricClaimedVerifiedStatus
1ACRV BaselineOMQ0.35—Unverified