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natural-language-processing

Image-guided Story Ending Generation

3260 papers • 126 benchmarks • 313 datasets

Image-guided Story Ending Generation (IgSEG) aims to generate a story ending for a given multi-sentence story plot and an ending-related image.

(Image credit: Papersgraph)

Benchmarks

These leaderboards are used to track progress in image-guided-story-ending-generation

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Dataset

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LSMDC-E

Libraries

Use these libraries to find image-guided-story-ending-generation models and implementations

sooftware/Attention-Implementation

2 papers 479

Datasets

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LSMDC-E

Subtasks

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Most implemented papers

Attention is All you Need

Noam Shazeer, Ashish Vaswani, Lukasz Kaiser, Jakob Uszkoreit, Niki Parmar, I. Polosukhin, Llion Jones, Aidan N. Gomez•Sun Jun 11 2017

A new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely is proposed, which generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data.

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Content

Introduction Benchmarks Datasets Subtasks Libraries Papers

sh951011/Attention-Implementation

2 papers 479

sooftware/nlp-attentions

2 papers 479

Paper Graph

Effective Approaches to Attention-based Neural Machine Translation

Christopher D. Manning, Thang Luong, Hieu Pham•Sun Aug 16 2015

A global approach which always attends to all source words and a local one that only looks at a subset of source words at a time are examined, demonstrating the effectiveness of both approaches on the WMT translation tasks between English and German in both directions.

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Paper Graph

MMT: Image-guided Story Ending Generation with Multimodal Memory Transformer

Dizhan Xue, Shengsheng Qian, Quan Fang, Changsheng Xu•Sun Oct 09 2022

This work proposes Multimodal Memory Transformer (MMT), an end-to-end framework that models and fuses both contextual and visual information to effectively capture the multimodal dependency for IgSEG.

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Paper Graph

A Survey on Interpretable Cross-modal Reasoning

Dizhan Xue, Shengsheng Qian, Changsheng Xu, Zuyi Zhou•Mon Sep 04 2023

This survey delves into the realm of interpretable cross-modal reasoning (I-CMR), where the objective is not only to achieve high predictive performance but also to provide human-understandable explanations for the results.

5 0

Paper Graph

Story Ending Generation with Incremental Encoding and Commonsense Knowledge

Minlie Huang, Jian Guan, Yansen Wang•Wed Aug 29 2018

A novel model for story ending generation that adopts an incremental encoding scheme to represent context clues which are spanning in the story context and can generate more reasonable story endings than state-of-the-art baselines1.

165 0

Paper Graph

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Image-guided Story Ending Generation | State-of-the-Art