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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1201–1225 of 1723 papers

TitleStatusHype
Reconstructing Vechicles from a Single Image: Shape Priors for Road Scene Understanding—0
Recyclable Semi-supervised Method Based on Multi-model Ensemble for Video Scene Parsing—0
Cataract-1K: Cataract Surgery Dataset for Scene Segmentation, Phase Recognition, and Irregularity Detection—0
Reducing Label Dependency for Underwater Scene Understanding: A Survey of Datasets, Techniques and Applications—0
Referring Self-supervised Learning on 3D Point Cloud—0
RefineCap: Concept-Aware Refinement for Image Captioning—0
CASPNet++: Joint Multi-Agent Motion Prediction—0
Case-based Reasoning Augmented Large Language Model Framework for Decision Making in Realistic Safety-Critical Driving Scenarios—0
Cascaded Classification Models: Combining Models for Holistic Scene Understanding—0
Relationship Proposal Networks—0
Relevance-driven Decision Making for Safer and More Efficient Human Robot Collaboration—0
Relevance for Human Robot Collaboration—0
Car Segmentation and Pose Estimation using 3D Object Models—0
Can you text what is happening? Integrating pre-trained language encoders into trajectory prediction models for autonomous driving—0
Can MLLMs Guide Me Home? A Benchmark Study on Fine-Grained Visual Reasoning from Transit Maps—0
REMIPS: Physically Consistent 3D Reconstruction of Multiple Interacting People under Weak Supervision—0
Can LVLMs Obtain a Driver's License? A Benchmark Towards Reliable AGI for Autonomous Driving—0
Can Large Multimodal Models Understand Agricultural Scenes? Benchmarking with AgroMind—0
Can Foundation Models Perform Zero-Shot Task Specification For Robot Manipulation?—0
Residual 3D Scene Flow Learning with Context-Aware Feature Extraction—0
Resource-Efficient Multiview Perception: Integrating Semantic Masking with Masked Autoencoders—0
Vision-Language Pre-training with Object Contrastive Learning for 3D Scene Understanding—0
3D Shape Augmentation with Content-Aware Shape Resizing—0
BridgeNet: Comprehensive and Effective Feature Interactions via Bridge Feature for Multi-task Dense Predictions—0
Rethinking RGB-D Fusion for Semantic Segmentation in Surgical Datasets—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