mlsanityData to 7 Oct 2026

Encoder-Decoder Based Long Short-Term Memory (LSTM) Model for Video Captioning

arXiv 2401.02052 ↗First posted 2 Oct 2023cs.CVcited by 3

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Abstract (arXiv)

This work demonstrates the implementation and use of an encoder-decoder model to perform a many-to-many mapping of video data to text captions. The many-to-many mapping occurs via an input temporal sequence of video frames to an output sequence of words to form a caption sentence. Data preprocessing, model construction, and model training are discussed. Caption correctness is evaluated using 2-gram BLEU scores across the different splits of the dataset. Specific examples of output captions were shown to demonstrate model generality over the video temporal dimension. Predicted captions were shown to generalize over video action, even in instances where the video scene changed dramatically. Model architecture changes are discussed to improve sentence grammar and correctness.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 16 of its 21 references that are in the map. First posted on arXiv 2 Oct 2023.

Builds on

16 of 21 references.

References · 16

  1. 2020Big Self-Supervised Models are Strong Semi-Supervised Learnerscited by 1,375
  2. 2020Prototypical Contrastive Learning of Unsupervised Representationscited by 636
  3. 2020Dense Regression Network for Video Groundingcited by 176
  4. 2015Sequence to Sequence -- Video to Textcited by 157
  5. 2023mPLUG-2: A Modularized Multi-modal Foundation Model Across Text, Image and Videocited by 154
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  7. 2020A Comprehensive Study of Deep Video Action Recognitioncited by 87
  8. 2019Temporally Grounding Language Queries in Videos by Contextual Boundary-aware Predictioncited by 84
  9. 2021Video Corpus Moment Retrieval with Contrastive Learningcited by 60
  10. 2019LoGAN: Latent Graph Co-Attention Network for Weakly-Supervised Video Moment Retrievalcited by 40
  11. 2020Visual Grounding in Video for Unsupervised Word Translationcited by 27
  12. 2021AnaXNet: Anatomy Aware Multi-label Finding Classification in Chest X-raycited by 19
4 more
  1. 2018Learning Unsupervised Visual Grounding Through Semantic Self-Supervisioncited by 10
  2. 2023Adversarial Sampling for Fairness Testing in Deep Neural Networkcited by 5
  3. 2021Decoupled Spatial Temporal Graphs for Generic Visual Groundingcited by 3
  4. 2023Enhancing Border Security and Countering Terrorism Through Computer Vision: a Field of Artificial Intelligencecited by 2

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Citing papers per year: 2024: 2; 2025: 1.

3 papers.

  1. 2024Deep Learning-Based Speech and Vision Synthesis to Improve Phishing Attack Detection through a Multi-layer Adaptive Frameworkcited by 2
  2. 2024An investigation into the performances of the Current state-of-the-art Naive Bayes, Non-Bayesian and Deep Learning Based Classifier for Phishing Detection: A Surveycited by 1
  3. 2025MAD-OOD: A Deep Learning Cluster-Driven Framework for an Out-of-Distribution Malware Detection and Classificationcited by 0