From Show to Tell: A Survey on Image Captioning

By Matteo Stefanini, Marcella Cornia, Lorenzo Baraldi, Silvia Cascianelli, Giuseppe Fiameni, Rita Cucchiara. 2021

58 citations recorded by the source corpus.

Subfields: Multimodal Learning, Natural Language Generation

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Multimodal Learning

1. Giuseppe Fiameni
2. Rita Cucchiara
3. Lorenzo Baraldi
4. Marcella Cornia
5. Ron Mokady
6. Amir Hertz
7. Matteo Stefanini
8. Radu Soricut

In Natural Language Generation

1. Rita Cucchiara
2. Lorenzo Baraldi
3. Marcella Cornia
4. Giuseppe Fiameni
5. Ron Mokady
6. Amir Hertz
7. Radu Soricut
8. Matteo Stefanini

Citations in the corpus

• ClipCap: CLIP Prefix for Image Captioning (2021, methodology): “Commonly, image captioning [34] models first encode the input pixels as feature vectors, which are then used to produce the final sequence of words.”
• Translation between Molecules and Natural Language (2022, unknown): “Image captioning has been studied extensively (Pan et al., 2004; Lu et al., 2018; Hossain et al., 2019; Stefanini et al., 2021).”
• Universal Captioner: Long-Tail Vision-and-Language Model Training through Content-Style Separation (2021, background): “Generating textual descriptions from visual inputs is a fundamental step towards the automatic modeling of connections between the visual and textual modalities [33, 53, 68].”