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 1651–1675 of 1723 papers

TitleStatusHype
SDOF-Tracker: Fast and Accurate Multiple Human Tracking by Skipped-Detection and Optical-FlowCode0
Exploring Scene Affinity for Semi-Supervised LiDAR Semantic SegmentationCode0
CLAIR-A: Leveraging Large Language Models to Judge Audio CaptionsCode0
Learning Rigidity in Dynamic Scenes with a Moving Camera for 3D Motion Field EstimationCode0
Category-level Neural Field for Reconstruction of Partially Observed Objects in Indoor EnvironmentCode0
Target-Aware Spatio-Temporal Reasoning via Answering Questions in Dynamics Audio-Visual ScenariosCode0
Aerial Scene Understanding in The Wild: Multi-Scene Recognition via Prototype-based Memory NetworksCode0
Task-Aware Asynchronous Multi-Task Model with Class Incremental Contrastive Learning for Surgical Scene UnderstandingCode0
Evaluating Compositional Scene Understanding in Multimodal Generative ModelsCode0
VTQA: Visual Text Question Answering via Entity Alignment and Cross-Media ReasoningCode0
ERFNet: Efficient Residual Factorized ConvNet for Real-time Semantic SegmentationCode0
ASI-Seg: Audio-Driven Surgical Instrument Segmentation with Surgeon Intention UnderstandingCode0
SeGAN: Segmenting and Generating the InvisibleCode0
Artificial Color Constancy via GoogLeNet with Angular Loss FunctionCode0
Adaptive Visual Scene Understanding: Incremental Scene Graph GenerationCode0
Temporally Consistent Horizon LinesCode0
CARL-D: A vision benchmark suite and large scale dataset for vehicle detection and scene segmentationCode0
Tensor Comprehensions: Framework-Agnostic High-Performance Machine Learning AbstractionsCode0
Efficient ConvNet for Real-time Semantic SegmentationCode0
Bridging Stereo Matching and Optical Flow via Spatiotemporal CorrespondenceCode0
Segmenting the FutureCode0
Learning Regional Purity for Instance Segmentation on 3D Point CloudsCode0
SeG-SR: Integrating Semantic Knowledge into Remote Sensing Image Super-Resolution via Vision-Language ModelCode0
Learning Panoptic Segmentation from Instance ContoursCode0
Box for Mask and Mask for Box: weak losses for multi-task partially supervised learningCode0
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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