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 721–730 of 1723 papers

TitleStatusHype
SIMS: Simulating Stylized Human-Scene Interactions with Retrieval-Augmented Script Generation—0
Quantifying the synthetic and real domain gap in aerial scene understanding—0
On-chip Hyperspectral Image Segmentation with Fully Convolutional Networks for Scene Understanding in Autonomous Driving—0
InstanceGaussian: Appearance-Semantic Joint Gaussian Representation for 3D Instance-Level Perception—0
SceneTAP: Scene-Coherent Typographic Adversarial Planner against Vision-Language Models in Real-World Environments—0
LoCATe-GAT: Modeling Multi-Scale Local Context and Action Relationships for Zero-Shot Action RecognitionCode0
Reconstructing Animals and the Wild—0
Grid-augmented vision: A simple yet effective approach for enhanced spatial understanding in multi-modal agentsCode0
Box for Mask and Mask for Box: weak losses for multi-task partially supervised learningCode0
HSI-Drive v2.0: More Data for New Challenges in Scene Understanding for Autonomous Driving—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