Image Caption: Enhancing GenAI with Training Data-Part 2

Image captioning, a fascinating field in artificial intelligence, combines computer vision and natural language processing to generate textual descriptions of images. Through the use of advanced deep learning techniques, this technology has made significant strides in recent years, providing accurate and meaningful captions that can enhance our understanding of visual content.

The key to developing robust image captioning models lies in the training data. A large and diverse dataset of images paired with corresponding captions is essential for teaching the AI system to associate visual features with textual descriptions. The training data must cover a wide range of subjects, contexts, and styles to ensure the model's versatility and ability to generate accurate captions across various domains.

Generative AI, a subset of artificial intelligence that focuses on generating new content, plays a crucial role in image captioning. By leveraging generative models such as recurrent neural networks (RNNs) or transformer-based architectures like the GPT-3, the AI system can learn the intricate relationship between visual input and textual output. These models capture the semantic meaning and context of the image, enabling them to generate coherent and contextually relevant captions.

Training a generative AI model for image captioning involves a multi-step process. Initially, the model is trained on a large-scale dataset containing paired images and captions, using techniques like supervised learning. During this training phase, the model learns to map visual features to relevant textual descriptions, developing an understanding of the semantics and structure of captions.

Nexdata Image Caption Training Datasets

Image caption data of human face

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

Race distribution: Asian, Caucasian, black race, brown race

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

Diversity of content: including wearing masks, anti-spoofing samples, different facial expressions, wearing glasses, wearing headphones, and multiple facial poses

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: race, gender, age, camera angle, lighting, diversity content, and other facial feature information

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

Image caption data of gestures

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

Race distribution: Asian

Age distribution: mainly young and middle-aged

Gender distribution: male, female

Collection environment: including indoor scenes and outdoor scenes

Collection diversity: different age groups, different collection environments, different seasons, different various collection angles and different gestures

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: hand movements, gestures, image shooting angles, subject gender, age, hand accessories, and other hand features

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

Image caption data of OCR in natural scenes

Data size: 2,800 pictures, 2,800 descriptions, 200 images per language

Language distribution:

Asian languages: Korean, Indonesian, Malay, Vietnamese, Thai, Chinese, Japanese

European languages: French, German, Italian, Portuguese, Russian, Spanish, English

Collection environment: including store plaques, stop signs, posters, road signs, prompts and other scenes

Collection diversity: including 14 languages, various natural scenes, and multiple shooting angles

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

Collection equipment: mobile phone, camera

Description language: English

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

Description content: text arrangement, text content, color, text location, material, related picture icons and other related features

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