Measuring the Diversity of Automatic Image Descriptions
By Emiel van Miltenburg, Desmond Elliott, Piek T. J. M. Vossen. 2018
49 citations recorded by the source corpus.
Subfields: Multimodal Learning, Evaluation & Benchmarks
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
- Giuseppe Fiameni
- Rita Cucchiara
- Lorenzo Baraldi
- Marcella Cornia
- Matteo Stefanini
- Silvia Cascianelli
- Emiel van Miltenburg
- Piek T. J. M. Vossen
In Evaluation & Benchmarks
- Sebastian Gehrmann
- Thibault Sellam
- Elizabeth Clark
- Shashi Narayan
- Ankur P. Parikh
- Anastasia Shimorina
- Emiel van Miltenburg
- Tatsunori B. Hashimoto
Citations in the corpus
- The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics (2021, methodology): “To account for this aspect, we compute multiple diversity metrics, starting with those proposed for the analysis of the results of the E2E NLG challenge (Dusek et al., 2020) and by van Miltenburg et al. (2018).”
- Talking about other people: an endless range of possibilities (2018, background): “Earlier work has looked at stereotyping behavior, reporting bias, and the use of negations in image descriptions (van Miltenburg, 2016; Misra et al., 2016; van Miltenburg et al., 2016, 2017), and recently Van Miltenburg et al. (2018) provided an overview of measures to quantify diversity.”
- Automatic Construction of Evaluation Suites for Natural Language Generation Datasets (2021, background): “judgements, and BERTSCORE for all non-English text; (3) Diversity: we additionally measure the vocabulary size of all outputs, the average output length (in words), the MSTTR [59], which measures the word-level diversity in the text, and the local recall [60], which identifies which fraction of the ”
- From Show to Tell: A Survey on Deep Learning-Based Image Captioning (2021, background): “The overall performance of a captioning system can be evaluated in terms of corpus-level diversity or, when the system can output multiple captions for the same image, single image-level diversity (termed as global diversity and local diversity, respectively, in [155]).”
- From Show to Tell: A Survey on Image Captioning (2021, background): “The overall performance of a captioning system can be evaluated in terms of corpus-level diversity or, when the system can output multiple captions for the same image, single image-level diversity (termed as global diversity and local diversity , respectively, in [139]).”