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Found 8 papers, 4 papers with code
Bridging Remote Sensors with Multisensor Geospatial Foundation Models
A key discovery of our research is that representations derived from natural images are not always compatible with the distinct characteristics of geospatial remote sensors, underscoring the limitations of existing representations in this field.
PipeRAG: Fast Retrieval-Augmented Generation via Algorithm-System Co-design
Retrieval-augmented generation (RAG) can enhance the generation quality of large language models (LLMs) by incorporating external token databases.
CaMML: Context-Aware Multimodal Learner for Large Models
In this work, we introduce Context-Aware MultiModal Learner (CaMML), for tuning large multimodal models (LMMs).
Ranked #47 on
Visual Question Answering
on MM-Vet
Lightweight In-Context Tuning for Multimodal Unified Models
In-context learning (ICL) involves reasoning from given contextual examples.
PreDiff: Precipitation Nowcasting with Latent Diffusion Models
We conduct empirical studies on two datasets: N-body MNIST, a synthetic dataset with chaotic behavior, and SEVIR, a real-world precipitation nowcasting dataset.
Wrapped Cauchy Distributed Angular Softmax for Long-Tailed Visual Recognition
The class-wise distribution of angular representation becomes a sum of these kernels.
Towards Geospatial Foundation Models via Continual Pretraining
Geospatial technologies are becoming increasingly essential in our world for a wide range of applications, including agriculture, urban planning, and disaster response.
Defocus Deblur Microscopy via Head-to-Tail Cross-scale Fusion
Microscopy imaging is vital in biology research and diagnosis.
