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 1341–1350 of 1723 papers

TitleStatusHype
Video-kMaX: A Simple Unified Approach for Online and Near-Online Video Panoptic Segmentation—0
Video Token Sparsification for Efficient Multimodal LLMs in Autonomous Driving—0
Vision-based Automated Bridge Component Recognition Integrated With High-level Scene Understanding—0
Vision-Centric Representation-Efficient Fine-Tuning for Robust Universal Foreground Segmentation—0
Vision-Language Embodiment for Monocular Depth Estimation—0
Vision-Language Models for Autonomous Driving: CLIP-Based Dynamic Scene Understanding—0
Vision-Language Models Struggle to Align Entities across Modalities—0
Vision-Language Pre-training with Object Contrastive Learning for 3D Scene Understanding—0
Visual Affordance and Function Understanding: A Survey—0
Visual Jenga: Discovering Object Dependencies via Counterfactual Inpainting—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