Dense Encoder

Dense encoders are neural network architectures designed to efficiently learn rich, compact representations from input data, primarily focusing on tasks like information retrieval and image segmentation. Current research emphasizes improving their generalization capabilities across diverse datasets, exploring training techniques like parameter-efficient methods and in-batch negatives, and developing novel decoder architectures for enhanced feature aggregation and hierarchical information processing. These advancements are driving improvements in various applications, including medical image analysis (e.g., polyp and optic disc segmentation), dialogue systems, and time-series forecasting, by enabling more accurate and efficient models.