Cohere For AI - Guest Speaker: Jay Gala, Generative AI Engineer 

Description of the talk: In this presentation, we will delve into the challenges and solutions surrounding cloud segmentation in remote sensing, focusing on the innovative approach introduced in the paper "Adaptive-Labeling for Enhancing Remote Sensing Cloud Understanding." We will begin by outlining the problem statement, highlighting the limitations of traditional cloud segmentation methods, particularly their struggle with inaccurate labeling in training data, which leads to suboptimal model performance.
Next, we will briefly touch upon traditional techniques used in cloud segmentation, acknowledging their reliance on pre-existing knowledge and the distinction between Earth's surface and clouds, as well as their vulnerability to intricate backgrounds and the subjective nature of threshold selection.

Following this, we will discuss the challenges these traditional techniques face, emphasizing their inability to effectively distinguish clouds in the presence of atmospheric aerosols, dust, smoke, and hazy pixels, and their dependence on the availability of accurate mask annotations, which are often invalid.

Finally, we will introduce Cloud Adaptive-Labeling (CAL), a groundbreaking method that addresses these challenges by iteratively improving the quality of training data annotations through a trainable pixel intensity threshold. This dynamic thresholding strategy adapts to changes in training loss, enhancing the model's ability to accurately distinguish clouds from other elements in remote sensing imagery. Through extensive experiments, CAL has demonstrated significant improvements in model performance, surpassing existing techniques and establishing new state-of-the-art results.

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