4 accepted papers
We investigate the question of whether transformers are effective for long-term time series forecasting. They suffer from temporal information loss, even though they use positional encoding to embed order information. This is due to the inherent permutation-invariant nature of attention nets. Mamba…
Change detection has long been used for various tasks. With advancements in robotic systems and computer vision, change detection techniques can be further explored for diverse applications. Current state-of-the-art methods primarily use either satellite images or street-level images to detect chang
We have seen recent advances in the fields of Machine Learning (ML), Deep Learning (DL), and Artificial intelligence (AI) that the models are becoming increasingly complex and large in terms of architecture and parameter size. These complex ML/DL models have beaten the state of the art in most field…
Vision transformers have gained significant attention and achieved state-of-the-art performance in various computer vision tasks, including image classification, instance segmentation, and object detection. However, challenges remain in addressing attention complexity and effectively capturing fine-…