mlsanityData to 7 Oct 2026

OPT: Omni-Perception Pre-Trainer for Cross-Modal Understanding and Generation

arXiv 2107.00249 ↗First posted 1 Jul 2021cs.CVcited by 20

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Abstract (arXiv)

In this paper, we propose an Omni-perception Pre-Trainer (OPT) for cross-modal understanding and generation, by jointly modeling visual, text and audio resources. OPT is constructed in an encoder-decoder framework, including three single-modal encoders to generate token-based embeddings for each modality, a cross-modal encoder to encode the correlations among the three modalities, and two cross-modal decoders to generate text and image respectively. For the OPT's pre-training, we design a multi-task pretext learning scheme to model multi-modal resources from three different data granularities, ie, token-, modality-, and sample-level modeling, through which OPT learns to align and translate among different modalities. The pre-training task is carried out on a large amount of image-text-audio triplets from Open Images. Experimental results show that OPT can learn strong image-text-audio multi-modal representations and achieve promising results on a variety of cross-modal understanding and generation tasks.

Key facts. Cited by 20 papers in the map (data through 7 Oct 2026). Builds on 44 of its 50 references that are in the map. First posted on arXiv 1 Jul 2021.

Builds on

44 of 50 references.

References · 44

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Citing papers per year: 2022: 6; 2023: 10; 2024: 3; 2025: 1.

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  1. 2022MERLOT Reserve: Neural Script Knowledge through Vision and Language and Soundcited by 184
  2. 2022VLP: A Survey on Vision-Language Pre-trainingcited by 159
  3. 2023Large-scale Multi-Modal Pre-trained Models: A Comprehensive Surveycited by 153
  4. 2023At Which Training Stage Does Code Data Help LLMs Reasoning?cited by 82
  5. 2024Detecting Multimedia Generated by Large AI Models: A Surveycited by 73
  6. 2023Self-Supervised Multimodal Learning: A Surveycited by 58
  7. 2022Learning in Audio-visual Context: A Review, Analysis, and New Perspectivecited by 44
  8. 2023LLMDet: A Third Party Large Language Models Generated Text Detection Toolcited by 44
  9. 2023OmniVec: Learning robust representations with cross modal sharingcited by 44
  10. 2023SNIP: Bridging Mathematical Symbolic and Numeric Realms with Unified Pre-trainingcited by 41
  11. 2022VATLM: Visual-Audio-Text Pre-Training with Unified Masked Prediction for Speech Representation Learningcited by 34
  12. 2023EVE: Efficient Vision-Language Pre-training with Masked Prediction and Modality-Aware MoEcited by 24
8 more
  1. 2023Accommodating Audio Modality in CLIP for Multimodal Processingcited by 18
  2. 2025OmniVec2 -- A Novel Transformer based Network for Large Scale Multimodal and Multitask Learningcited by 13
  3. 2023LANISTR: Multimodal Learning from Structured and Unstructured Datacited by 10
  4. 2024Pedestrian Attribute Recognition: A New Benchmark Dataset and A Large Language Model Augmented Frameworkcited by 10
  5. 2022Leveraging Acoustic Contextual Representation by Audio-textual Cross-modal Learning for Conversational ASRcited by 7
  6. 2024Navigating the Landscape of Large Language Models: A Comprehensive Review and Analysis of Paradigms and Fine-Tuning Strategiescited by 5
  7. 2022HiVLP: Hierarchical Vision-Language Pre-Training for Fast Image-Text Retrievalcited by 4
  8. 2023Conversational Speech Recognition by Learning Audio-textual Cross-modal Contextual Representationcited by 4