# Attention Beam: An Image Captioning Approach

Source: [Attention Beam: An Image Captioning Approach](<https://www.sota2.com/research/paper/68fa68bf1e209131cea1e08f>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T22:44:52.991Z

Authors: Anubhav Shrimal, Tanmoy Chakraborty

Published: 2020-11-03T14:57:42.000Z

## Abstract

The aim of image captioning is to generate textual description of a given image. Though seemingly an easy task for humans, it is challenging for machines as it requires the ability to comprehend the image (computer vision) and consequently generate a human-like description for the image (natural language understanding). In recent times, encoder-decoder based architectures have achieved state-of-the-art results for image captioning. Here, we present a heuristic of beam search on top of the encoder-decoder based architecture that gives better quality captions on three benchmark datasets: Flickr8k, Flickr30k and MS COCO.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2011.01753>)

[PDF](<https://arxiv.org/pdf/2011.01753.pdf>)