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Spatial Reasoning

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Showing 326–350 of 453 papers

TitleStatusHype
Whiteboard-of-Thought: Thinking Step-by-Step Across Modalities—0
GSR-BENCH: A Benchmark for Grounded Spatial Reasoning Evaluation via Multimodal LLMs—0
Neuro-symbolic Training for Reasoning over Spatial LanguageCode0
WildVision: Evaluating Vision-Language Models in the Wild with Human Preferences—0
RoboPoint: A Vision-Language Model for Spatial Affordance Prediction for Robotics—0
SpaRC and SpaRP: Spatial Reasoning Characterization and Path Generation for Understanding Spatial Reasoning Capability of Large Language ModelsCode0
Quantifying Geospatial in the Common Crawl Corpus—0
SpatialRGPT: Grounded Spatial Reasoning in Vision Language Models—0
Reframing Spatial Reasoning Evaluation in Language Models: A Real-World Simulation Benchmark for Qualitative ReasoningCode0
Can Large Language Models Create New Knowledge for Spatial Reasoning Tasks?—0
Generating Human Motion in 3D Scenes from Text Descriptions—0
RoboHop: Segment-based Topological Map Representation for Open-World Visual Navigation—0
Beyond Human Vision: The Role of Large Vision Language Models in Microscope Image Analysis—0
Re-Thinking Inverse Graphics With Large Language Models—0
Learning to Localize Objects Improves Spatial Reasoning in Visual-LLMsCode0
HAMMR: HierArchical MultiModal React agents for generic VQA—0
Challenges Faced by Large Language Models in Solving Multi-Agent Flocking—0
Grounding Spatial Relations in Text-Only Language ModelsCode0
SpatialPIN: Enhancing Spatial Reasoning Capabilities of Vision-Language Models through Prompting and Interacting 3D Priors—0
JSTR: Joint Spatio-Temporal Reasoning for Event-based Moving Object Detection—0
DivCon: Divide and Conquer for Progressive Text-to-Image Generation—0
Contrastive Region Guidance: Improving Grounding in Vision-Language Models without Training—0
A Surprising Failure? Multimodal LLMs and the NLVR Challenge—0
DriveVLM: The Convergence of Autonomous Driving and Large Vision-Language Models—0
PIVOT: Iterative Visual Prompting Elicits Actionable Knowledge for VLMs—0
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