Visual Question Answering and Image Captioning blend computer vision with natural language processing. These tasks enable AI to understand and describe images, answering questions about visual content and generating descriptive captions.

Models for these tasks use multimodal architectures, combining CNNs for image processing with RNNs or Transformers for text. Evaluation metrics assess accuracy and fluency, while addressing challenges like subjectivity and dataset bias.

Visual Question Answering and Image Captioning

Tasks in visual question answering

• Visual Question Answering (VQA) combines computer vision and NLP to answer questions about images in natural language
• VQA takes image and question as input, outputs answer based on image content
• Requires understanding both visual and textual information (What color is the car? Blue)
• Applications include assisting visually impaired users, image retrieval systems

Models for VQA and captioning

• Multimodal architectures integrate CNNs for images and RNNs/Transformers for text
• VQA models use image encoder, question encoder, fusion module, answer decoder
• Captioning models employ image encoder, caption decoder, attention mechanism
• Training involves end-to-end approaches, transfer learning, curriculum learning
• Popular architectures: Show, Attend and Tell; Bottom-Up and Top-Down Attention

Image caption generation techniques

• Encoder-decoder architecture uses CNN encoder and RNN/Transformer decoder
• Attention mechanisms focus on relevant image regions during caption generation
• Caption generation extracts features, initializes state, generates words sequentially
• Beam search or sampling techniques produce final caption from decoder outputs
• Training objectives include maximum likelihood and reinforcement learning
• Transformer-based models (CLIP, ViT) show promising results in recent research

Evaluation of VQA models

• Datasets: VQA Dataset, Visual Genome, CLEVR provide diverse question types
• Metrics: Accuracy, WUPS measure answer correctness and similarity
• VQA Score balances human consensus and model predictions
• Human evaluation assesses relevance, fluency, and consistency with image
• Challenges include subjectivity, multiple correct answers, balancing diversity/accuracy
• Bias and fairness concerns in datasets and model outputs require careful consideration