OmniDataComposer: A Unified Data Structure for Multimodal Data Fusion and Infinite Data Generation

• URL: http://arxiv.org/abs/2308.04126v2
• Date: Thu, 17 Aug 2023 09:25:22 GMT
• Title: OmniDataComposer: A Unified Data Structure for Multimodal Data Fusion and Infinite Data Generation
• Authors: Dongyang Yu and Shihao Wang and Yuan Fang and Wangpeng An
• Abstract summary: OmniDataComposer is an innovative approach for multimodal data fusion and unlimited data generation. It is capable of identifying over 6400 categories of objects, substantially broadening the spectrum of visual information. It amalgamates diverse modalities, promoting reciprocal enhancement among modalities and facilitating cross-modal data correction.
• Score: 8.149870655785955
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper presents OmniDataComposer, an innovative approach for multimodal data fusion and unlimited data generation with an intent to refine and uncomplicate interplay among diverse data modalities. Coming to the core breakthrough, it introduces a cohesive data structure proficient in processing and merging multimodal data inputs, which include video, audio, and text. Our crafted algorithm leverages advancements across multiple operations such as video/image caption extraction, dense caption extraction, Automatic Speech Recognition (ASR), Optical Character Recognition (OCR), Recognize Anything Model(RAM), and object tracking. OmniDataComposer is capable of identifying over 6400 categories of objects, substantially broadening the spectrum of visual information. It amalgamates these diverse modalities, promoting reciprocal enhancement among modalities and facilitating cross-modal data correction. \textbf{The final output metamorphoses each video input into an elaborate sequential document}, virtually transmuting videos into thorough narratives, making them easier to be processed by large language models. Future prospects include optimizing datasets for each modality to encourage unlimited data generation. This robust base will offer priceless insights to models like ChatGPT, enabling them to create higher quality datasets for video captioning and easing question-answering tasks based on video content. OmniDataComposer inaugurates a new stage in multimodal learning, imparting enormous potential for augmenting AI's understanding and generation of complex, real-world data.

Related papers

• mmE5: Improving Multimodal Multilingual Embeddings via High-quality Synthetic Data [71.352883755806]
  Multimodal embedding models have gained significant attention for their ability to map data from different modalities, such as text and images, into a unified representation space. However, the limited labeled multimodal data often hinders embedding performance. Recent approaches have leveraged data synthesis to address this problem, yet the quality of synthetic data remains a critical bottleneck.
  arXiv  Detail & Related papers  (2025-02-12T15:03:33Z)
• Ola: Pushing the Frontiers of Omni-Modal Language Model with Progressive Modality Alignment [88.72389428177942]
  Ola is an omni-modal language model that achieves competitive performance across image, video, and audio understanding. We aim to make Ola a fully open omni-modal understanding solution to advance future research in this emerging field.
  arXiv  Detail & Related papers  (2025-02-06T18:59:55Z)
• MM-LDM: Multi-Modal Latent Diffusion Model for Sounding Video Generation [14.28357169715152]
  We introduce a novel multi-modal latent diffusion model (MM-LDM) for the task. We first unify the representation of audio and video data by converting them into a single or a couple of images. Then, we introduce a hierarchical multi-modal autoencoder that constructs a low-level perceptual latent space for each modality and a shared high-level semantic feature space.
  arXiv  Detail & Related papers  (2024-10-02T14:32:24Z)
• OmniBench: Towards The Future of Universal Omni-Language Models [63.16606414452612]
  We introduce OmniBench, a novel benchmark designed to rigorously evaluate models' ability to recognize, interpret, and reason across visual, acoustic, and textual inputs simultaneously. Our main findings reveal that most OLMs exhibit critical limitations in instruction-following and reasoning capabilities within tri-modal contexts. To address this gap, we curate an instruction tuning dataset of 84.5K training samples, OmniInstruct, for training OLMs to adapt to multimodal contexts.
  arXiv  Detail & Related papers  (2024-09-23T17:59:05Z)
• OneEncoder: A Lightweight Framework for Progressive Alignment of Modalities [0.08192907805418585]
  Cross-modal alignment learning integrates information from different modalities like text, image, audio and video to create unified models. Current techniques rely on large modality-specific encoders, necessitating fine-tuning or training from scratch on vast aligned datasets. OneEncoder is a lightweight framework that progressively represents and aligns four modalities.
  arXiv  Detail & Related papers  (2024-09-17T10:38:46Z)
• Learning Multimodal Data Augmentation in Feature Space [65.54623807628536]
  LeMDA is an easy-to-use method that automatically learns to jointly augment multimodal data in feature space. We show that LeMDA can profoundly improve the performance of multimodal deep learning architectures.
  arXiv  Detail & Related papers  (2022-12-29T20:39:36Z)
• Everything at Once -- Multi-modal Fusion Transformer for Video Retrieval [36.50847375135979]
  Multi-modal learning from video data has seen increased attention recently as it allows to train semantically meaningful embeddings without human annotation. We present a multi-modal, modality fusion transformer approach that learns to exchange information between multiple modalities, such as video, audio, and text, and integrate them into a joined multi-modal representation.
  arXiv  Detail & Related papers  (2021-12-08T18:14:57Z)
• VX2TEXT: End-to-End Learning of Video-Based Text Generation From Multimodal Inputs [103.99315770490163]
  We present a framework for text generation from multimodal inputs consisting of video plus text, speech, or audio. Experiments demonstrate that our approach based on a single architecture outperforms the state-of-the-art on three video-based text-generation tasks.
  arXiv  Detail & Related papers  (2021-01-28T15:22:36Z)
• Automatic Curation of Large-Scale Datasets for Audio-Visual Representation Learning [62.47593143542552]
  We describe a subset optimization approach for automatic dataset curation. We demonstrate that our approach finds videos with high audio-visual correspondence and show that self-supervised models trained on our data, despite being automatically constructed, achieve similar downstream performances to existing video datasets with similar scales.
  arXiv  Detail & Related papers  (2021-01-26T14:27:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.