What is instance Recognition?

Part I: Sparse features for matching object instances.

What is instance level?

Instance Level Recognition (ILR), is a visual recognition task to recognize a specific instance of an object not just object class. For example, as shown in the above image, painting is an object class, and “Mona Lisa” by Leonardo Da Vinci is an instance of that painting.

What is fine grained classification?

Fine-grained categorization, as a sub-field of object recognition, aims to distinguish subordinate categories within entry level categories. Examples include recognizing species of birds such as “northern cardinal” or “indigo bunting”; flowers such as “tulip” or “cherry blossom”.

What is instance level image Retrieval?

Instance-level image retrieval is the task of searching in a large database for images that match an object in a query image. Instance recognition is a challenging task that aims to vi- sually recognize an object instance. This is distinct from category-level recognition that identifies only the object class.

What is instance retrieval?

Image Instance Retrieval is the problem of retrieving images from a database representing the same object or scene as the one depicted in a query image.

What is object instance segmentation?

Instance segmentation is a computer vision task for detecting and localizing an object in an image. Instance segmentation is a natural sequence of semantic segmentation, and it is also one of the biggest challenges compared to other segmentation techniques.

What is the difference between coarse and fine-grained?

Precision and ambiguity Coarse-grained materials or systems have fewer, larger discrete components than fine-grained materials or systems. A coarse-grained description of a system regards large subcomponents. A fine-grained description regards smaller components of which the larger ones are composed.

What is instance recognition in artificial intelligence?

Instance-level recognition (ILR) is the computer vision task of recognizing a specific instance of an object, rather than simply the category to which it belongs.

What is the difference between semantic segmentation and instance segmentation?

Semantic segmentation associates every pixel of an image with a class label such as a person, flower, car and so on. It treats multiple objects of the same class as a single entity. In contrast, instance segmentation treats multiple objects of the same class as distinct individual instances.

How does instance segmentation work?

Instance segmentation contains 2 major parts: Object Detection (which contains classification as well) and semantic segmentation. In other words, it just runs object detection firstly, then uses a semantic segmentation model inside every rectangle (which are called bounding boxes).

What are the differences between object detection and segmentation?

Segmentation models provide the exact outline of the object within an image. That is, pixel by pixel details are provided for a given object, as opposed to Classification models, where the model identifies what is in an image, and Detection models, which places a bounding box around specific objects.