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Related Experiment Video

Updated: Oct 22, 2025

Eye Tracking During Visually Situated Language Comprehension: Flexibility and Limitations in Uncovering Visual Context Effects
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Video captioning with stacked attention and semantic hard pull.

Md Mushfiqur Rahman1, Thasin Abedin2, Khondokar S S Prottoy2

  • 1Department of Computer Science and Engineering, Islamic University of Technology, Gazipur, Bangladesh.

Peerj. Computer Science
|August 26, 2021
PubMed
Summary

This study introduces a novel Semantically Sensible Video Captioning (SSVC) model that enhances video description generation using stacked attention and spatial hard pull. The new Semantic Sensibility (SS) score improves evaluation beyond traditional metrics.

Keywords:
LSTMSequence to sequenceSpatial Hard PullStacked attentionVideo captioning

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Video captioning bridges Natural Language Processing and Computer Vision.
  • Generating semantically accurate video descriptions is complex.
  • Existing models often use sequential/recurrent layers for encoding and decoding.

Purpose of the Study:

  • To propose a novel video captioning architecture, Semantically Sensible Video Captioning (SSVC).
  • To improve the context generation mechanism in video captioning models.
  • To introduce a more effective evaluation metric for video captioning.

Main Methods:

  • Developed the SSVC architecture with "stacked attention" and "spatial hard pull" for context generation.
  • Employed BLEU score for quantitative analysis.
  • Proposed a human evaluation metric, the Semantic Sensibility (SS) scoring metric, for qualitative analysis.

Main Results:

  • The SSVC model, incorporating stacked attention and spatial hard pull, demonstrated improved performance.
  • The proposed SS scoring metric effectively addresses limitations of existing automated metrics.
  • The novelties enhance the performance of state-of-the-art video captioning architectures.

Conclusions:

  • The SSVC architecture offers a significant advancement in video captioning.
  • The "stacked attention" and "spatial hard pull" mechanisms effectively improve context generation.
  • The Semantic Sensibility (SS) scoring metric provides a valuable tool for evaluating video captioning quality.