StructSAM: Structure- and Spectrum-Preserving Token Merging for Segment Anything Models
StructSAM: Structure- and Spectrum-Preserving Token Merging for Segment Anything Models explores StructSAM accelerates Segment Anything Model (SAM) inference by intelligently merging tokens, offering a speedup with minimal accuracy loss, making it ideal for real-time segmentation applications.. Commercial viability score: 7/10 in Image Segmentation.
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 $9K - $13K 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 ThisSAM3-LiteText: An Anatomical Study of the SAM3 Text Encoder for Efficient Vision-Language SegmentationViability: 6.0
- Builds On ThisEye image segmentation using visual and concept prompts with Segment Anything Model 3 (SAM3)Viability: 4.0
- Builds On ThisBalancing Saliency and Coverage: Semantic Prominence-Aware Budgeting for Visual Token Compression in VLMsViability: 3.0
- Prior WorkET-SAM: Efficient Point Prompt Prediction in SAM for Unified Scene Text Detection and Layout AnalysisViability: 7.0
- Prior Work