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Image Caption: Enhancing GenAI with Training Data-Part 1

From:Nexdata Date:2024-04-01

Generative AI has witnessed remarkable advancements in recent years, particularly in the domain of image captioning. This technology combines the power of computer vision and natural language processing to automatically generate descriptions or captions for images. Image captioning holds great potential in various applications, from assisting visually impaired individuals to improving image search and retrieval systems. However, the quality and accuracy of the generated captions heavily rely on the training data used to train the model.

Generative AI algorithms for image captioning leverage large-scale datasets, such as COCO (Common Objects in Context) or Flickr30k, which contain thousands of images with manually generated captions. These datasets cover a wide range of subjects and provide the necessary context for the model to learn the relationships between visual content and textual descriptions. The training process involves optimizing the model's parameters to minimize the difference between the generated captions and the ground truth captions in the training data.

To enhance the performance of image captioning models, researchers are constantly exploring methods to improve the quality and diversity of the training data. One approach is to incorporate additional sources of data, such as user-generated captions from social media platforms or specialized domain-specific datasets. By including diverse and real-world captions, models can better capture the nuances of language and produce more accurate and contextually relevant descriptions.

Another area of focus is data augmentation techniques, which involve creating variations of existing images and captions. This approach helps expose the model to different perspectives and variations of the same scene, enabling it to generate captions that are more robust and adaptable. Techniques like random cropping, flipping, and rotation can be applied to images, while textual augmentation methods like paraphrasing or word substitutions can be used for captions.

Nexdata Image Caption Training Datasets

Image caption data of human body in CCTV scenes

Data size: 2,000 images, 2,000 descriptions

Race distribution: Asian

Gender distribution: male, female

Age distribution: under 18 years old, 18~45 years old, 46~60 years old, over 60 years old

Collection environment: including indoor scenes and outdoor scenes

Collection diversity: different age groups, different collection environments, and different seasons

Data format: image format is .jpg, text format is .txt

Description language: English

Text length: most of them are 30~60 words, usually 3-5 sentences

Description content: gender, age, clothing, hairstyle, body orientation, posture, and other descriptions of human attributes

Accuracy rate: the proportion of correctly labeled images is not less than 97%

Image caption data of diverse scenes

Data size: 2,000 images, 2,000 descriptions

Collection environment: including natural scenes, urban street scenes, shopping mall scenes, exhibitions,family environment, displays and other scenes

Acquisition equipment: various brands of cameras

Collection diversity: multiple scenes, multiple time periods, multiple shooting angles

Data format: image format is .jpg, text format is .txt

Description language: English

Text length: most of them are 30~60 words, usually 3-5 sentences

Description content: the main scene in the image, usually including foreground and foreground detail description, background and background detail description

Accuracy rate: the proportion of correctly labeled images is not less than 97%

Image caption data of vehicles

Data siza: 2,000 images, 2,000 descriptions

Models: car, SUV, MPV, truck, coach

Time distribution: day, night

Collection environment: outdoor road

Collection equipment: surveillance camera

Collection angle: overlooking

Collection diversity: different models, different times

Data format: image format is .jpg, text format is .txt

Description language: English

Text length: most of them are 30~60 words, usually 3-5 sentences

Description: model, color, vehicle orientation, time, location or scene, and other vehicle attributes

Accuracy rate: the proportion of correctly labeled images is not less than 97%

Image & Video caption data of human action

Data size: 1,000 images, 1,000 videos, 2,000 descriptions

Race distribution: Caucasian, black

Gender distribution: male, female

Age distribution: from teenagers to old age, mainly young and middle-aged

Collection environment: including indoor scenes and outdoor scenes

Collection diversity: different age groups, different collection environments, different seasons, various shooting angles, and various human behaviors

Data format: image format is .jpg, video format is .mp4, text format is .txt

Description language: English

Text length: most of them are 30~60 words, usually 3-5 sentences

Descriptive content: gender, age, clothing, behavior, body movements and other salient information

Accuracy rate: the proportion of correct labeling is not less than 97%

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