Detection Loss

Detection loss, a crucial component in various machine learning models, focuses on quantifying the discrepancy between predicted and actual outputs, driving model improvement. Current research emphasizes developing novel loss functions tailored to specific challenges, such as open-set recognition (handling unseen classes), adverse conditions (e.g., low light, fog), and efficient model training (e.g., quantization, knowledge distillation). These advancements improve the accuracy and robustness of object detection, visual grounding, and other applications, impacting fields ranging from autonomous driving to medical diagnostics.