SMURF: SeMantic and linguistic UndeRstanding Fusion for Caption
Evaluation via Typicality Analysis

SMURF: SeMantic and linguistic UndeRstanding Fusion for Caption Evaluation via Typicality Analysis

2 June 2021

Joshua Forster Feinglass

Yezhou Yang

Papers citing "SMURF: SeMantic and linguistic UndeRstanding Fusion for Caption Evaluation via Typicality Analysis"

7 / 7 papers shown

Title
Polos: Multimodal Metric Learning from Human Feedback for Image Captioning Yuiga Wada Kanta Kaneda Daichi Saito Komei Sugiura 34 24 0 28 Feb 2024
An Examination of the Robustness of Reference-Free Image Captioning Evaluation Metrics Saba Ahmadi Aishwarya Agrawal 30 6 0 24 May 2023
What's in a Caption? Dataset-Specific Linguistic Diversity and Its Effect on Visual Description Models and Metrics David M. Chan Austin Myers Sudheendra Vijayanarasimhan David A. Ross Bryan Seybold John F. Canny 28 6 0 12 May 2022
Tragedy Plus Time: Capturing Unintended Human Activities from Weakly-labeled Videos Arnav Chakravarthy Zhiyuan Fang Yezhou Yang 35 2 0 28 Apr 2022
Injecting Semantic Concepts into End-to-End Image Captioning Zhiyuan Fang Jianfeng Wang Xiaowei Hu Lin Liang Zhe Gan Lijuan Wang Yezhou Yang Zicheng Liu ViT VLM 24 86 0 09 Dec 2021
Transparent Human Evaluation for Image Captioning Jungo Kasai Keisuke Sakaguchi Lavinia Dunagan Jacob Morrison Ronan Le Bras Yejin Choi Noah A. Smith 33 47 0 17 Nov 2021
A Survey on Bias and Fairness in Machine Learning Ninareh Mehrabi Fred Morstatter N. Saxena Kristina Lerman Aram Galstyan SyDa FaML 326 4,223 0 23 Aug 2019