Getting to the Point: Why Pointing Improves LVLMs
Getting to the Point: Why Pointing Improves LVLMs explores This research explores how explicit object pointing in Large Vision-Language Models improves zero-shot counting accuracy and generalization by encoding spatial information.. Commercial viability score: 4/10 in Large Vision-Language Models.
BUILDER'S SANDBOX
Build This Paper
Use an AI coding agent to implement this research.
OpenAI CodexAI Agent
Lightweight coding agent in your terminal.
Claude CodeAI Agent
Agentic coding tool for terminal workflows.
AntiGravity IDEScaffolding
AI agent mindset installer and workflow scaffolder.
CursorIDE
AI-first code editor built on VS Code.
VS CodeIDE
Free, open-source editor by Microsoft.
Recommended Stack
Startup Essentials
Estimated $10K - $14K over 6-10 weeks.
See exactly what it costs to build this -- with 3 comparable funded startups.
7-day free trial. Cancel anytime.
Discover the researchers behind this paper and find similar experts.
7-day free trial. Cancel anytime.
Related Papers
- Builds On ThisThe Dual Mechanisms of Spatial Reasoning in Vision-Language ModelsViability: 3.0
- Prior WorkSpatial Reasoning is Not a Free Lunch: A Controlled Study on LLaVAViability: 4.0
- Prior WorkEfficient Inference of Large Vision Language ModelsViability: 4.0
- Higher ViabilityCounting Circuits: Mechanistic Interpretability of Visual Reasoning in Large Vision-Language ModelsViability: 7.0
- Higher ViabilityMolmoPoint: Better Pointing for VLMs with Grounding TokensViability: 7.0
Related Resources
- How are researchers addressing the bias present in large vision-language models?(question)
- What are the computational overheads associated with training and deploying large vision-language models?(question)
- What are the challenges of real-world navigation tasks for existing large vision-language models?(question)