Pixels to Prose: Understanding the art of Image Captioning

arXiv:2408.15714 · cs.CV, cs.LG · Submitted 2024-08-28 · Read on arXiv

cs.CV, cs.LG

Submitted: 2024-08-28

Updated: 2024-08-28

Terminology

• Transformer — 85× in this paper · explained in 2 episodes
• Attention Mechanism — 42× in this paper · explained in 2 episodes
• Alignment — 19× in this paper · explained in 1 episode
• Embeddings — 18× in this paper · explained in 2 episodes
• Accuracy — 13× in this paper · explained in 1 episode
• Reinforcement Learning — 10× in this paper · explained in 4 episodes
• Activation Function — 7× in this paper · explained in 1 episode
• Conference on Computer Vision and Pattern Recognition — 7× in this paper
• Contrastive Learning — 7× in this paper · explained in 5 episodes
• Feature Extraction — 7× in this paper · explained in 2 episodes
• Knowledge Graph — 7× in this paper · explained in 7 episodes
• Convolutional Neural Network — 6× in this paper
• Perplexity — 6× in this paper · explained in 3 episodes
• Self-Supervised Learning — 6× in this paper · explained in 7 episodes
• Abstraction — 5× in this paper · explained in 1 episode
• Consistency — 5× in this paper · explained in 3 episodes
• Latent Space — 5× in this paper · explained in 7 episodes
• Benchmark — 4× in this paper · explained in 2 episodes
• Brevity Penalty — 4× in this paper
• Cosine Similarity — 4× in this paper · explained in 3 episodes
• Covariate Shift — 4× in this paper · explained in 2 episodes
• Recurrent Neural Network — 4× in this paper
• Region Proposal Network — 4× in this paper
• Transformer Architecture — 4× in this paper · explained in 2 episodes
• Visual Question Answering — 4× in this paper
• CVF International Conference on Computer Vision — 3× in this paper
• Downstream Tasks — 3× in this paper · explained in 2 episodes
• Dual-Level Collaborative Transformer — 3× in this paper
• Fine-Tuning — 3× in this paper · explained in 5 episodes
• Foundation Model — 3× in this paper · explained in 3 episodes
• Latent Representation — 3× in this paper · explained in 2 episodes
• Masked Language Modeling — 3× in this paper
• Representation Learning — 3× in this paper · explained in 2 episodes
• Abstract Scene Graph — 2× in this paper
• Adaptability — 2× in this paper · explained in 1 episode
• Adaptation — 2× in this paper · explained in 1 episode
• Adversarial Training — 2× in this paper · explained in 1 episode
• Computational overhead — 2× in this paper · explained in 2 episodes
• Contextual Understanding — 2× in this paper · explained in 2 episodes
• Contrastive Language-Image Pre-Training — 2× in this paper
• Federated Learning — 2× in this paper · explained in 14 episodes
• Feed-Forward Network — 2× in this paper
• Foundation Models — 2× in this paper · explained in 7 episodes
• Grounding — 2× in this paper · explained in 3 episodes
• Knowledge graphs — 2× in this paper · explained in 2 episodes
• Latent Representations — 2× in this paper · explained in 3 episodes
• Loss Function — 2× in this paper · explained in 2 episodes
• Regularization — 2× in this paper · explained in 3 episodes
• Semantic Alignment — 2× in this paper · explained in 3 episodes
• Tensor Product Scene-Graph-Triplet Representation — 2× in this paper
• Trace — 2× in this paper · explained in 2 episodes
• Variational Bayes — 2× in this paper · explained in 2 episodes
• Vector Representations — 2× in this paper · explained in 2 episodes
• Video-Language Model — 2× in this paper
• Visual Geometry Group — 2× in this paper
• Adam — 1× in this paper · explained in 2 episodes
• Clustering — 1× in this paper · explained in 2 episodes
• Core — 1× in this paper · explained in 2 episodes
• Domain Shift — 1× in this paper · explained in 4 episodes
• Few-Shot Learning — 1× in this paper · explained in 4 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• Gradient Flow — 1× in this paper · explained in 2 episodes
• Image-Text Matching — 1× in this paper
• Interpretability — 1× in this paper · explained in 15 episodes
• Late Fusion — 1× in this paper · explained in 2 episodes
• Masked Image Modeling — 1× in this paper
• Multimodal Fusion — 1× in this paper · explained in 3 episodes
• Objective Function — 1× in this paper · explained in 2 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Segmentation Masks — 1× in this paper · explained in 2 episodes
• Semantic Gap — 1× in this paper · explained in 2 episodes
• Semantic Propositional Image Caption Evaluation — 1× in this paper
• Semi-supervised Learning — 1× in this paper · explained in 4 episodes
• Transfer Learning — 1× in this paper · explained in 10 episodes
• Transformer Models — 1× in this paper · explained in 2 episodes
• Unified Framework — 1× in this paper · explained in 5 episodes
• Variational Autoencoder (VAE) — 1× in this paper · explained in 3 episodes
• Zero-Shot Generalization — 1× in this paper · explained in 5 episodes

Sources

• Deep Captioning with Multimodal Recurrent Neural Networks (m-RNN)
• TPsgtR: Neural-Symbolic Tensor Product Scene-Graph-Triplet Representation for Image Captioning

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