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Parameter-Efficient Transfer Learning for Remote Sensing Image-Text Retrieval, 2023
[ECCV 2024] Single Image to 3D Textured Mesh in 10 seconds with Convolutional Reconstruction Model.
ALSO: Automotive Lidar Self-supervision by Occupancy estimation
There can be more than Notion and Miro. AFFiNE(pronounced [ə‘fain]) is a next-gen knowledge base that brings planning, sorting and creating all together. Privacy first, open-source, customizable an…
🙃 A delightful community-driven (with 2,400+ contributors) framework for managing your zsh configuration. Includes 300+ optional plugins (rails, git, macOS, hub, docker, homebrew, node, php, python…
[ECCV 2024 Oral] The official implementation of "CAT-SAM: Conditional Tuning for Few-Shot Adaptation of Segment Anything Model".
Grounded SAM 2: Ground and Track Anything in Videos with Grounding DINO, Florence-2 and SAM 2
🎉 PyTorch efficient farthest point sampling (FPS) library.
[CVPR 2024] AirPlanes: Accurate Plane Estimation via 3D-Consistent Embeddings
Diagrams for visualizing neural network architecture (Created with diagrams.net)
Latex code for making neural networks diagrams
🏄 Scalable embedding, reasoning, ranking for images and sentences with CLIP
The implementation of our paper "Point2Building: Reconstructing Buildings from Airborne LiDAR Point Clouds"
Universal Monocular Metric Depth Estimation
[CV4AEC Workshop CVPR 2024] Dataset + Evaluation Metrics
Visualizer for neural network, deep learning and machine learning models
Official Implementation of "DITTO: Dual and Integrated Latent Topologies for Implicit 3D Reconstruction" in CVPR 2024
We present Object Images (Omages): An homage to the classic Geometry Images.
A More Fair and Comprehensive Comparison between KAN and MLP
The repository provides code for running inference with the Meta Segment Anything Model 2 (SAM 2), links for downloading the trained model checkpoints, and example notebooks that show how to use th…
Code for RealmDreamer: Text-Driven 3D Scene Generation with Inpainting and Depth Diffusion [Arxiv 2024]
Official code for "FeatUp: A Model-Agnostic Frameworkfor Features at Any Resolution" ICLR 2024