AI & ML interests
LLM, ComputerVision, ML Architectures
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3 days ago
VividFlow: AI Image Enhancement & Video Generation š¬šØ
Bring your images to life with cinematic motion AND create stunning AI backgrounds! VividFlow combines professional-grade video generation with intelligent background replacement in one streamlined platform.
š Dual Creative Powers
Transform any static image into high-quality dynamic videos with smooth, natural motion ranging from 0.5 to 5 seconds. Choose from curated motion templates across 8 categories designed for portraits, products, landscapes, and artistic content. Create photorealistic backgrounds by selecting from 24 professionally crafted scene presets spanning studios, natural environments, urban settings, and artistic atmospheres...etc.
ā” Optimized Performance
Video generation currently completes in 4-5 minutes with active optimization underway to dramatically reduce processing time. Background replacement finishes in 30-40 seconds after initial loading. The independent dual-tab design ensures smooth workflow without performance conflicts.
šÆ Complete Creative Control
Achieve perfectly consistent results with seed-based reproducibility and adjustable duration for video generation. Background creation offers flexible composition modes, precision edge softening for challenging subjects, and instant mask preview for quality verification.
š Continuous Innovation
Ongoing optimization targets significantly faster video generation through advanced model preparation. Future enhancements include expanded template libraries, batch processing capabilities, and industry-specific presets shaped by community feedback.
š Try it now: https://huggingface.co/spaces/DawnC/VividFlow
Support development with a ā¤ļø ā your engagement shapes future priorities!
#AI #ImageToVideo #BackgroundGeneration #VideoGeneration
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23 days ago
PawMatchAI ā Smarter, Safer, and More Thoughtful Recommendations šāØ
š¾ Recommendation system update ā deeper reasoning, safer decisions
Over the past weeks, user feedback led me to rethink how PawMatchAI handles description-based breed recommendations. Instead of only matching surface-level preferences, the system now implements a multi-dimensional semantic reasoning architecture that emphasizes real-life compatibility and risk awareness.
Key technical improvements:
- SBERT-powered semantic understanding with dynamic weight allocation across six constraint dimensions (space, activity, noise, grooming, experience, family)
- Hierarchical constraint management distinguishing critical safety constraints from flexible preferences, with progressive relaxation when needed
-Multi-head scoring system combining semantic matching (15%), lifestyle compatibility (70%), constraint adherence (10%), and confidence calibration (5%)
-Intelligent risk filtering that applies graduated penalties (-10% to -40%) for genuine incompatibilities while preserving user choice
The goal: š Not just dogs that sound good on paper, but breeds people will actually thrive with long-term.
What's improved?
- šÆ Clearer separation of must-have safety constraints versus flexible preferences
- š§ Bidirectional semantic matching evaluating compatibility from both user and breed perspectives
- š Context-aware prioritization where critical factors (safety, space, noise) automatically receive higher weighting
What's next?
- š Expanding behavioral and temperament analysis dimensions
- š¾ Extension to additional species with transfer learning
- š± Mobile-optimized deployment for easier access
- š§© Enhanced explainability showing why specific breeds are recommended
š Try PawMatchAI: https://huggingface.co/spaces/DawnC/PawMatchAI
#AIProduct #SBERT #RecommendationSystems #DeepLearning #MachineLearning #NLP
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