CIDEr: Consensus-based Image Description Evaluation

Teaser Figure Consensus Sentences (bold) captured by the proposed CIDEr evaluation protocol.


Ramakrishna Vedantam,

C. Lawrence Zitnick,

Devi Parikh


Automatically describing an image with a sentence is a long-standing challenge in computer vision and natural language processing. Due to recent progress in object detection, attribute classification, action recognition, etc., there is renewed interest in this area. However, evaluating the quality of descriptions has proven to be challenging. We propose a novel paradigm for evaluating image descriptions that uses human consensus. This paradigm consists of three main parts: a new triplet-based method of collecting human annotations to measure consensus, a new automated metric (CIDEr) that captures consensus, and two new datasets: PASCAL-50S and ABSTRACT-50S that contain 50 sentences describing each image. Our simple metric captures human judgment of consensus better than existing metrics across sentences generated by various sources. We also evaluate five state-of-the-art image description approaches using this new protocol and provide a benchmark for future comparisons. A version of CIDEr named CIDEr-D is available as a part of MS COCO evaluation server to enable systematic evaluation and benchmarking.


Ramakrishna Vedantam, C. Lawrence Zitnick, Devi Parikh

Consensus-based Image Description Evaluation

in proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015

[PDF] [Poster] [Extended Abstract]


author = {Vedantam, Ramakrishna and Lawrence Zitnick, C. and Parikh, Devi},
title = {CIDEr: Consensus-Based Image Description Evaluation},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2015}


Download the PASCAL-50S, ABSTRACT-50S datasets here and the consensus annotations here

NOTE: '.mat' files can be easily loaded in Python using


ArXiv version with supplementary material arXiv


Berkeley Vision and Language Workshop Slides