Classification combined with localization refers to the process of identifying and classifying objects in an image, and also determining the location of those objects within the image. This is a common task in image recognition and is often used in applications such as object detection in self-driving cars or facial recognition in security systems.

The process typically involves training a machine learning model on a large dataset of images that have been labeled with the objects and their locations. The model is then able to recognize and classify these objects in new images, and also predict their locations within the image. This is typically done using bounding boxes or other types of annotations that define the region of the image where the object is located.

Instance segmentation differs from semantic segmentation in the sense that it gives a unique label to every instance of a particular object in the image. As can be seen in the image above all cars are assigned different colours i.e different labels. With semantic segmentation all of them would have been assigned the same colour.

Semantic segmentation is the process of classifying each pixel belonging to a particular label. It doesn't different across different instances of the same object. For example if there are 2 cats in an image, semantic segmentation gives same label to all the pixels of both cats

Pose estimation refers to the process of determining the position and orientation of an object or a person in a given image or video. This is a common task in computer vision and is often used in applications such as augmented reality, robotics, and virtual reality.

Pose estimation involves analyzing the visual features of an object or a person and determining their pose in 3D space. This requires a detailed understanding of the geometric relationships between different body parts or objects, and the ability to accurately estimate their relative positions and orientations.

Object tracking is a fundamental task in computer vision that involves following the motion of one or more objects over time in a video sequence. The goal of object tracking is to identify and locate objects in each frame of the video, and associate these objects across frames to maintain their identity over time. Object tracking algorithms typically rely on a combination of techniques such as motion estimation, appearance modeling, feature extraction, and machine learning to achieve accurate and robust tracking. Object tracking has many applications in fields such as surveillance, autonomous driving, robotics, and augmented reality, and is an active area of research in computer vision.