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Found 5 papers, 1 papers with code
Img2Loc: Revisiting Image Geolocalization using Multi-modality Foundation Models and Image-based Retrieval-Augmented Generation
Geolocating precise locations from images presents a challenging problem in computer vision and information retrieval. Traditional methods typically employ either classification, which dividing the Earth surface into grid cells and classifying images accordingly, or retrieval, which identifying locations by matching images with a database of image-location pairs.
On the Promises and Challenges of Multimodal Foundation Models for Geographical, Environmental, Agricultural, and Urban Planning Applications
The advent of large language models (LLMs) has heightened interest in their potential for multimodal applications that integrate language and vision.
XAI meets Biology: A Comprehensive Review of Explainable AI in Bioinformatics Applications
Artificial intelligence (AI), particularly machine learning and deep learning models, has significantly impacted bioinformatics research by offering powerful tools for analyzing complex biological data.
BadSAM: Exploring Security Vulnerabilities of SAM via Backdoor Attacks
Recently, the Segment Anything Model (SAM) has gained significant attention as an image segmentation foundation model due to its strong performance on various downstream tasks.
Text2Seg: Remote Sensing Image Semantic Segmentation via Text-Guided Visual Foundation Models
We developed a pipeline that leverages multiple FMs to facilitate remote sensing image semantic segmentation tasks guided by text prompt, which we denote as Text2Seg.
Instance Segmentation
Segmentation Of Remote Sensing Imagery
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