Image captioning is the task of generating a natural language description (caption) for an image. It’s a multimodal problem that combines computer vision and natural language processing. Typical modern approaches use a CNN (like ResNet or VGG) to encode the image into feature representations, then feed that into an RNN or Transformer-based decoder that generates a sentence word by word (often trained with pairs of images and ground truth captions). The model learns to associate visual concepts with language. For instance, given an image of a dog playing with a ball, a caption might be “A dog is playing fetch with a blue ball in a park.” Challenges include correctly identifying objects, their attributes and relations, and producing coherent, grammatically correct sentences. Evaluation metrics like BLEU, METEOR, or CIDEr compare generated captions with human-written captions. Image captioning has applications in accessibility (describing images to visually impaired users) and content management.