mlsanityData to 7 Oct 2026

ZR-2021VG: Zero-Resource Speech Challenge, Visually-Grounded Language Modelling track, 2021 edition

arXiv 2107.06546 ↗First posted 14 Jul 2021cs.CL cs.LG cs.SDcited by 6

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Abstract (arXiv)

We present the visually-grounded language modelling track that was introduced in the Zero-Resource Speech challenge, 2021 edition, 2nd round. We motivate the new track and discuss participation rules in detail. We also present the two baseline systems that were developed for this track.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 24 of its 39 references that are in the map. First posted on arXiv 14 Jul 2021.

Builds on

24 of 39 references.

References · 24

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2018Representation Learning with Contrastive Predictive Codingcited by 7,662
  3. 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,016
  4. 2014Deep Visual-Semantic Alignments for Generating Image Descriptionscited by 966
  5. 2019HowTo100M: Learning a Text-Video Embedding by Watching Hundred Million Narrated Video Clipscited by 946
  6. 2017Towards Automatic Learning of Procedures from Web Instructional Videoscited by 616
  7. 2019An Unsupervised Autoregressive Model for Speech Representation Learningcited by 185
  8. 2018How2: A Large-scale Dataset for Multimodal Language Understandingcited by 137
  9. 2020The Zero Resource Speech Benchmark 2021: Metrics and baselines for unsupervised spoken language modelingcited by 100
  10. 2015Deep Multimodal Semantic Embeddings for Speech and Imagescited by 56
  11. 2020Text-Free Image-to-Speech Synthesis Using Learned Segmental Unitscited by 50
  12. 2017The Zero Resource Speech Challenge 2017cited by 45
12 more
  1. 2019Learning Hierarchical Discrete Linguistic Units from Visually-Grounded Speechcited by 44
  2. 2019The Zero Resource Speech Challenge 2019: TTS without Tcited by 38
  3. 2017Representations of language in a model of visually grounded speech signalcited by 37
  4. 2021Visually grounded models of spoken language: A survey of datasets, architectures and evaluation techniquescited by 32
  5. 2017Encoding of phonology in a recurrent neural model of grounded speechcited by 27
  6. 2019Language learning using Speech to Image retrievalcited by 21
  7. 2017Semantic speech retrieval with a visually grounded model of untranscribed speechcited by 20
  8. 2019Models of Visually Grounded Speech Signal Pay Attention To Nouns: a Bilingual Experiment on English and Japanesecited by 13
  9. 2019Word Recognition, Competition, and Activation in a Model of Visually Grounded Speechcited by 13
  10. 2018AVA-Speech: A Densely Labeled Dataset of Speech Activity in Moviescited by 10
  11. 2018Symbolic inductive bias for visually grounded learning of spoken languagecited by 10
  12. 2020Textual Supervision for Visually Grounded Spoken Language Understandingcited by 9

Built on by

Cited by year

Citing papers per year: 2021: 1; 2022: 3; 2023: 0; 2024: 2.

6 papers.

  1. 2022Self-supervised language learning from raw audio: Lessons from the Zero Resource Speech Challengecited by 34
  2. 2022Self-Supervised Representation Learning for Speech Using Visual Grounding and Masked Language Modelingcited by 25
  3. 2021Fast-Slow Transformer for Visually Grounding Speechcited by 23
  4. 2022Evaluating context-invariance in unsupervised speech representationscited by 11
  5. 2024Learning Semantic Information from Raw Audio Signal Using Both Contextual and Phonetic Representationscited by 0
  6. 2024Multimodal Input Aids a Bayesian Model of Phonetic Learningcited by 0