Follow the Saliency: Supervised Saliency for Retrieval-augmented Dense Video Captioning
Follow the Saliency: Supervised Saliency for Retrieval-augmented Dense Video Captioning explores STaRC enhances Dense Video Captioning by using supervised saliency for improved temporal segmentation and caption generation.. Commercial viability score: 8/10 in Video Captioning.
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 ThisStay in your Lane: Role Specific Queries with Overlap Suppression Loss for Dense Video CaptioningViability: 7.0
- Builds On ThisSAIL: Similarity-Aware Guidance and Inter-Caption Augmentation-based Learning for Weakly-Supervised Dense Video CaptioningViability: 6.0
- Builds On ThisSAVE: Speech-Aware Video Representation Learning for Video-Text RetrievalViability: 7.0
- Builds On ThisCVA: Context-aware Video-text Alignment for Video Temporal GroundingViability: 7.0
- Builds On ThisKnowledge-Refined Dual Context-Aware Network for Partially Relevant Video Retrieval