FarSLIP: Discovering Effective CLIP Adaptation for Fine-Grained Remote Sensing Understanding
☆16Nov 20, 2025Updated 11 months ago
Alternatives and similar repositories for FarSLIP
Users that are interested in FarSLIP are comparing it to the libraries listed below. We may earn a commission when you buy through links labeled 'Ad' on this page.
Sorting:
- Repository of CFM: Language-aligned Concept Foundation Model for Vision☆29Updated this week
- One model is enough: Toward multiclass weakly supervised remote sensing image semantic segmentation☆29Jul 26, 2023Updated 3 years ago
- [CVPR 2026 Highlight] ReAG: Reasoning-Augmented Generation for Knowledge-based Visual Question Answering☆32Jul 13, 2026Updated 3 months ago
- A Tool to visualize NN training☆15Feb 21, 2026Updated 8 months ago
- Code for "Task-Aware Clustering for Prompting Vision-Language Models" in CVPR 2025.☆14Jun 12, 2025Updated last year
- Deploy open-source AI quickly and easily - Special Bonus Offer • AdRunpod Hub is built for open source. One-click deployment and autoscaling endpoints without provisioning your own infrastructure.
- Lab: Dataset-agnostic equity returns prediction pipeline with time-series cross-validation☆16Feb 17, 2026Updated 8 months ago
- ☆12Mar 13, 2024Updated 2 years ago
- AI-powered image editor with LangGraph agent technology. Edit photos using natural language commands through an intelligent conversationa…☆19Jun 13, 2025Updated last year
- Official repo for paper "Static Segmentation by Tracking: A Label-Efficient Approach to Fine-Grained Segmentation". Ideal for specimen im…☆15Sep 20, 2026Updated last month
- ☆18Nov 15, 2024Updated last year
- This repository is for the Computer Vision Nano-degree Program from Udacity.☆11Aug 30, 2024Updated 2 years ago
- ☆17Jun 15, 2026Updated 4 months ago
- Cross Visual Prompt Tuning [ICCV 2025]☆14Aug 3, 2025Updated last year
- ☆20Sep 21, 2026Updated 3 weeks ago
- Proton VPN Special Offer - Get 70% off • AdSpecial partner offer. Trusted by over 100 million users worldwide. Tested, Approved and Recommended by Experts.
- Code of ["Spectral Prompt Tuning: Unveiling Unseen Classes for Zero-Shot Semantic Segmentation"]