## Object Detection

Object Detection is a core computer vision task where a model is trained to identify and locate multiple objects within an image by drawing bounding boxes around them. In the context of Vision-Language Models (VLMs), object detection is enhanced by the model's ability to not only recognize objects but also describe them in natural language. VLMs can provide additional context by naming objects, detailing attributes (such as color, size, or type), and offering richer descriptions of the scene. This fusion of vision and language supports more detailed and semantically aware detection, where object recognition can be linked to more complex visual understanding tasks.

## Visual Question Answering (VQA)

Visual Question Answering (VQA) merges vision and language by requiring a model to analyze an image and answer questions about its content. VLMs excel in VQA because they jointly understand both the visual components of an image and the linguistic details of the question. This allows the model to perform tasks like answering "How many dogs are there?" or "Is the person in the image wearing glasses?" with high accuracy. VQA is a key task for VLMs, demonstrating their ability to reason about complex visual scenes while considering natural language prompts.

## Object Character Recognition (OCR)

Object Character Recognition (OCR) involves detecting and recognizing text within an image, often from signs, documents, or other real-world scenes. With VLMs, OCR capabilities go beyond simple text extraction. These models understand the context in which the text appears, enabling them to answer questions, perform translations, or incorporate textual information into broader visual tasks. This contextual awareness makes VLMs particularly adept at handling tasks like reading and interpreting text embedded in images, and answering questions like "What does the sign say?" or "Translate the text in the image."
