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Newer models like JAGAN (Joint Attention Generative Adversarial Nets) are introduced to ensure that the generated text maintains a professional "clinical language style". 📊 Key Challenges & Metrics

The identifier refers to the specific article index for a prominent scientific review titled "Deep image captioning: A review of methods, trends and future challenges" , published in the journal Neurocomputing (Volume 546, August 2023). 126287

Traditional training data can lead to hallucinations or biased outputs, particularly in socio-economically diverse content. The study organizes the "deep image captioning" process

The study organizes the "deep image captioning" process by simulating the human experience of describing an image through three specific stages: trends and future challenges"

The review highlights the primary obstacles currently facing researchers in the field:

Metrics like BLEU and ROUGE are used to measure accuracy, but they sometimes struggle to capture the full semantic meaning or clinical relevance of a caption.

The field is shifting toward Multimodal Large Language Models (MLLMs) to provide better reasoning and generative flexibility. Community Perspectives