MTLSI-Net: A Linear Semantic Interaction Network for Parameter-Efficient Multi-Task Dense Prediction
MTLSI-Net: A Linear Semantic Interaction Network for Parameter-Efficient Multi-Task Dense Prediction explores A parameter-efficient network for multi-task dense prediction that captures cross-task interactions with linear complexity, achieving state-of-the-art results.. Commercial viability score: 7/10 in Multi-Task Dense Prediction.
Updated 4 hours ago
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 ThisMutually Causal Semantic Distillation Network for Zero-Shot LearningViability: 4.0
- Builds On ThisMiniCPM-SALA: Hybridizing Sparse and Linear Attention for Efficient Long-Context ModelingViability: 5.0
- Builds On ThisMulti-Task Learning with Additive U-Net for Image Denoising and ClassificationViability: 3.0
- Builds On ThisExplicit Multi-head Attention for Inter-head Interaction in Large Language ModelsViability: 6.0
- Builds On ThisStacked from One: Multi-Scale Self-Injection for Context Window Extension