Image Encoder

Image encoders are fundamental components of many computer vision systems, aiming to transform images into meaningful numerical representations that capture essential visual information. Current research focuses on improving encoder efficiency, robustness across diverse datasets (including synthetic data), and mitigating biases. Prominent approaches utilize vision transformers, convolutional neural networks, and diffusion models, often integrated with other modules like adapters or retrieval branches to enhance performance for tasks such as image segmentation, object detection, and zero-shot learning. These advancements have significant implications for various applications, including image manipulation detection, medical image analysis, and improving the efficiency and fairness of large multimodal models.