Work

Guiding the Long-Short Term Memory Model for Image Caption Generation

Year: 2015

Type: conference-paper

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 to avoid bias towards short sentences. On various benchmark datasets such as Flickr8K, Flickr30K and MS COCO, we obtain results that are on par with or better than the current state-of-the-art.

Source: IEEE/CVF International Conference on Computer Vision (ICCV)

DOI: 10.1109/iccv.2015.277

Authors: Xu Jia, Efstratios Gavves, Basura Fernando, Tinne Tuytelaars

Institutions: iMinds, KU Leuven, University of Amsterdam, Australian National University

Language: English

Topic: Multimodal Machine Learning Applications

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: iMinds, KU Leuven

OpenAlex ID: W2220981600

FWCI: 17.25

Cites: 42

Cited by: 483

Related to: 9