6 citations · 9 across the 4 of their papers we have counts for
5 papers
AgriCLIP: Adapting CLIP for Agriculture and Livestock via Domain-Specialized Cross-Model Alignment
Umair Nawaz, Muhammad Awais, Hanan Gani +4
Capitalizing on vast amount of image-text data, large-scale vision-language pre-training has demonstrated remarkable zero-shot capabilities and has been utilized in several applica…
CDChat: A Large Multimodal Model for Remote Sensing Change Description
Mubashir Noman, Noor Ahsan, Muzammal Naseer +4
Large multimodal models (LMMs) have shown encouraging performance in the natural image domain using visual instruction tuning. However, these LMMs struggle to describe the content…
Efficient Localized Adaptation of Neural Weather Forecasting: A Case Study in the MENA Region
Muhammad Akhtar Munir, Fahad Shahbaz Khan, Salman Khan
Accurate weather and climate modeling is critical for both scientific advancement and safeguarding communities against environmental risks. Traditional approaches rely heavily on N…
GeoChat: Grounded Large Vision-Language Model for Remote Sensing
Kartik Kuckreja, Muhammad Sohail Danish, Muzammal Naseer +3
Recent advancements in Large Vision-Language Models (VLMs) have shown great promise in natural image domains, allowing users to hold a dialogue about given visual content. However,…
Latent Embedding Feedback and Discriminative Features for Zero-Shot Classification
Sanath Narayan, Akshita Gupta, Fahad Shahbaz Khan +2
Zero-shot learning strives to classify unseen categories for which no data is available during training. In the generalized variant, the test samples can further belong to seen or…