# Guiding Long-Short Term Memory for Image Caption Generation Source: [Guiding Long-Short Term Memory for Image Caption Generation]() Collection index: [llms.txt]() Data updated: 2025-10-25T20:12:29.814Z Authors: Xu Jia, Efstratios Gavves, Basura Fernando, Tinne Tuytelaars Published: 2015-09-16T15:02:30.000Z ## Abstract In this work we focus on the problem of image caption generation. We propose an extension of the long short term memory (LSTM) model, which we coin gLSTM for short. In particular, we add semantic information extracted from the image as extra input to each unit of the LSTM block, with the aim of guiding the model towards solutions that are more tightly coupled to the image content. Additionally, we explore different length normalization strategies for beam search in order to prevent from favoring short sentences. On various benchmark datasets such as Flickr8K, Flickr30K and MS COCO, we obtain results that are on par with or even outperform the current state-of-the-art. ## 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. - [Image Captioning on MS COCO Karpathy (test)](): Image Captioning; MS COCO Karpathy (test); CIDEr: 0.81. ## Original sources and implementation [Original paper]() [PDF]()