In the field of image annotation, complex concepts such as metamorphic-based frameworks, human-related cost, and computing cost can make it challenging to understand the underlying processes. In this article, we aim to simplify these complex ideas by using everyday language and engaging analogies.
Metamorphic-Based Frameworks: A Key to Image Annotation
Imagine a magical tool that allows you to transform an image into different variations, each with unique attributes. This is what metamorphic-based frameworks do in image annotation. By automatically generating variants of the original image, these frameworks make it easier for annotators to identify specific elements and their relationships. The magic lies in the algorithms that create these transformations, making the annotation process more efficient and accurate.
Human-Related Cost: A Factor in Image Annotation Budgets
When it comes to image annotation, compensating annotators is crucial. Imagine working tirelessly to label images, only to receive a small payment for your efforts. This is where human-related cost comes into play, as it covers the average rate of 5 USD per hour paid to annotators. It may seem like a small amount, but it adds up when considering the overall project budget and duration.
Computing Cost: A Pay-as-You-Go Model for Proprietary Models
Now imagine having access to powerful computing models that can automate image annotation tasks. However, these models come with a cost, as you are charged on a pay-as-you-go basis. This means that the cost of computing depends on how often you use it. For open-source models deployed locally, this cost is eliminated, making image annotation more affordable and efficient.
Evaluation Process: Assessing LMMs’ Performance in Image Annotation
Imagine evaluating the performance of Large Language Models (LMMs) in image annotation. This process involves three stages: response generation, answer extraction, and score calculation. Firstly, the LMM generates an answer text based on the input query. Then, an answer extractor based on ChatGLM3 is used to identify the correct answer. Finally, the accuracy of the extracted answer is measured against the ground truth.
In conclusion, image annotation involves complex concepts such as metamorphic-based frameworks, human-related cost, and computing cost. By using everyday language and engaging analogies, we demystify these ideas and make them easier to comprehend. The evaluation process for LMMs in image annotation also follows a clear structure, ensuring accurate assessment of their performance.
