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Found 11 papers, 7 papers with code
TEOChat: A Large Vision-Language Assistant for Temporal Earth Observation Data
Large vision and language assistants have enabled new capabilities for interpreting natural images.
ExPLoRA: Parameter-Efficient Extended Pre-Training to Adapt Vision Transformers under Domain Shifts
An under-explored question of PEFT is in extending the pre-training phase without supervised labels; that is, can we adapt a pre-trained foundation model to a new domain via efficient self-supervised pre-training on this new domain?
SpotNet: An Image Centric, Lidar Anchored Approach To Long Range Perception
In this paper, we propose SpotNet: a fast, single stage, image-centric but LiDAR anchored approach for long range 3D object detection.
Large Language Models are Geographically Biased
Initially, we demonstrate that LLMs are capable of making accurate zero-shot geospatial predictions in the form of ratings that show strong monotonic correlation with ground truth (Spearman's $\rho$ of up to 0. 89).
DiffusionSat: A Generative Foundation Model for Satellite Imagery
Our method outperforms previous state-of-the-art methods for satellite image generation and is the first large-scale generative foundation model for satellite imagery.
GeoLLM: Extracting Geospatial Knowledge from Large Language Models
With GeoLLM, we observe that GPT-3. 5 outperforms Llama 2 and RoBERTa by 19% and 51% respectively, suggesting that the performance of our method scales well with the size of the model and its pretraining dataset.
Denoising Diffusion Bridge Models
However, for many applications such as image editing, the model input comes from a distribution that is not random noise.
Differentiable Weight Masks for Domain Transfer
For instance, given a network that has been trained on a source task, we would like to re-train this network on a similar, yet different, target task while maintaining its performance on the source task.
Invalid Logic, Equivalent Gains: The Bizarreness of Reasoning in Language Model Prompting
We find that the logically \textit{invalid} reasoning prompts do indeed achieve similar performance gains on BBH tasks as logically valid reasoning prompts.
SatMAE: Pre-training Transformers for Temporal and Multi-Spectral Satellite Imagery
Unsupervised pre-training methods for large vision models have shown to enhance performance on downstream supervised tasks.
Activation Regression for Continuous Domain Generalization with Applications to Crop Classification
Geographic variance in satellite imagery impacts the ability of machine learning models to generalise to new regions.
