Microsoft COCO Captions: Data Collection and Evaluation Server (1504.00325v2)

Abstract: In this paper we describe the Microsoft COCO Caption dataset and evaluation server. When completed, the dataset will contain over one and a half million captions describing over 330,000 images. For the training and validation images, five independent human generated captions will be provided. To ensure consistency in evaluation of automatic caption generation algorithms, an evaluation server is used. The evaluation server receives candidate captions and scores them using several popular metrics, including BLEU, METEOR, ROUGE and CIDEr. Instructions for using the evaluation server are provided.

• The paper introduces a dataset with over 330,000 images and multiple human-generated captions, including a subset with 40 references, to benchmark captioning models.
• The paper employs an evaluation server that uses BLEU, METEOR, ROUGE, and CIDEr metrics to ensure consistent and reliable performance comparisons.
• The paper analyzes human captioning variability and outlines future directions to enhance both dataset quality and evaluation methodologies.

Microsoft COCO Captions: Data Collection and Evaluation Server

The paper "Microsoft COCO Captions: Data Collection and Evaluation Server" authored by Xinlei Chen et al. presents a comprehensive dataset and an evaluation mechanism aimed at standardizing the evaluation of image caption generation algorithms. The Microsoft COCO (Common Objects in COntext) Captions dataset, in conjunction with the evaluation server, addresses the challenge of automatic image captioning, a prominent task intersecting computer vision, natural language processing, and machine learning.

Data Collection

The dataset builds upon the Microsoft COCO dataset, focusing on collecting captions for over 330,000 images. The collection process is meticulously designed to capture the complexity and intention behind each image. For the training and validation datasets, five independent human-generated captions are provided for each image. In the testing phase, two different subsets are created: MS COCO c5 and MS COCO c40. MS COCO c5 includes five reference captions per image, while MS COCO c40 includes 40 reference captions for a subset of 5,000 images in the test set. The expanded number of captions in MS COCO c40 aims to improve the correlation of automated evaluations with human judgments.

Data collection was executed using Amazon's Mechanical Turk (AMT), where participants were guided through a specific set of instructions to ensure consistency and quality in the generated captions. The instructions were crafted to avoid common pitfalls such as over-describing minor details or conjecturing beyond the visible content.

Evaluation Server

A critical contribution of this paper is the establishment of an evaluation server that ensures consistent and reliable evaluation of image captioning algorithms. This server employs multiple well-established metrics including BLEU, METEOR, ROUGE, and CIDEr to score candidate captions. The approach mitigates discrepancies that arise from variations in metric implementations and provides a standardized framework for comparison across different models.

Evaluation Metrics

1. BLEU: This metric focuses on the precision of n-grams (up to 4-grams in this context), with a brevity penalty to discourage short captions. Despite being widely used, BLEU tends to favor short sentences and often fails in sentence-level evaluations.
2. ROUGE: Specifically designed for text summarization, ROUGE computes recall-based scores for n-grams and Longest Common Subsequence (LCS), along with skip bi-grams. ROUGE-L, which uses LCS, and ROUGE-S, which uses skip bi-grams, are particularly noted.
3. METEOR: This metric aligns words in candidate and reference sentences to minimize chunks of contiguous tokens. It incorporates precision, recall, and a penalty based on alignment chunks, balancing between precision and recall more effectively than BLEU.
4. CIDEr: Developed to evaluate image descriptions, CIDEr utilizes TF-IDF weighting for n-grams and achieves higher correlation with human judgments. CIDEr-D further refines this metric by incorporating length-based penalties to prevent gaming the metrics.

Human Performance Analysis

The paper includes an examination of human agreement in captioning tasks to benchmark the performance of automated systems. It highlights the variability in human-generated captions and the complex equivalency in different expressions of the same visual content. An analysis of human precision and recall in word prediction tasks provides further insights, illustrating how specific terms (e.g., nouns vs. adjectives) differ in predictability and usage consistency among human subjects.

Implications and Future Directions

The Microsoft COCO Captions dataset and its evaluation server have significant implications for the research community. By providing a large, diverse, and well-annotated dataset along with a standardized evaluation mechanism, researchers can reliably compare the performance of various captioning models. This consistency is critical for meaningful advances in the field, as it mitigates the risk of overfitting to particular datasets or metrics.

Future developments may include expanding the dataset, improving human evaluation processes, and further refining automated evaluation metrics through human judgment experiments. Such efforts aim to ensure that progress in image captioning translates to genuine improvements in understanding and generating natural language descriptions of visual content.

In conclusion, this paper lays an essential foundation for progress in image captioning, providing robust resources and tools for the research community to build upon.