Unified Video-Language Pre-training with Synchronized Audio
- URL: http://arxiv.org/abs/2405.07202v1
- Date: Sun, 12 May 2024 07:59:46 GMT
- Title: Unified Video-Language Pre-training with Synchronized Audio
- Authors: Shentong Mo, Haofan Wang, Huaxia Li, Xu Tang,
- Abstract summary: We propose an enhanced framework for Video-Language pre-training with Synchronized Audio.
Our framework learns tri-modal representations in a unified self-supervised transformer.
Our model pre-trained on only 0.9M data achieves improving results against state-of-the-art baselines.
- Score: 21.607860535968356
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Video-language pre-training is a typical and challenging problem that aims at learning visual and textual representations from large-scale data in a self-supervised way. Existing pre-training approaches either captured the correspondence of image-text pairs or utilized temporal ordering of frames. However, they do not explicitly explore the natural synchronization between audio and the other two modalities. In this work, we propose an enhanced framework for Video-Language pre-training with Synchronized Audio, termed as VLSA, that can learn tri-modal representations in a unified self-supervised transformer. Specifically, our VLSA jointly aggregates embeddings of local patches and global tokens for video, text, and audio. Furthermore, we utilize local-patch masked modeling to learn modality-aware features, and leverage global audio matching to capture audio-guided features for video and text. We conduct extensive experiments on retrieval across text, video, and audio. Our simple model pre-trained on only 0.9M data achieves improving results against state-of-the-art baselines. In addition, qualitative visualizations vividly showcase the superiority of our VLSA in learning discriminative visual-textual representations.
Related papers
- Audio-visual Generalized Zero-shot Learning the Easy Way [20.60905505473906]
We introduce EZ-AVGZL, which aligns audio-visual embeddings with transformed text representations.
We conduct extensive experiments on VGGSound-GZSL, UCF-GZSL, and ActivityNet-GZSL benchmarks.
arXiv Detail & Related papers (2024-07-18T01:57:16Z) - Improving Audio-Visual Speech Recognition by Lip-Subword Correlation
Based Visual Pre-training and Cross-Modal Fusion Encoder [58.523884148942166]
We propose two novel techniques to improve audio-visual speech recognition (AVSR) under a pre-training and fine-tuning training framework.
First, we explore the correlation between lip shapes and syllable-level subword units in Mandarin to establish good frame-level syllable boundaries from lip shapes.
Next, we propose an audio-guided cross-modal fusion encoder (CMFE) neural network to utilize main training parameters for multiple cross-modal attention layers.
arXiv Detail & Related papers (2023-08-14T08:19:24Z) - Exploring the Role of Audio in Video Captioning [59.679122191706426]
We present an audio-visual framework, which aims to fully exploit the potential of the audio modality for captioning.
We propose new local-global fusion mechanisms to improve information exchange across audio and video.
arXiv Detail & Related papers (2023-06-21T20:54:52Z) - CLIPSonic: Text-to-Audio Synthesis with Unlabeled Videos and Pretrained
Language-Vision Models [50.42886595228255]
We propose to learn the desired text-audio correspondence by leveraging the visual modality as a bridge.
We train a conditional diffusion model to generate the audio track of a video, given a video frame encoded by a pretrained contrastive language-image pretraining model.
arXiv Detail & Related papers (2023-06-16T05:42:01Z) - Language-Guided Audio-Visual Source Separation via Trimodal Consistency [64.0580750128049]
A key challenge in this task is learning to associate the linguistic description of a sound-emitting object to its visual features and the corresponding components of the audio waveform.
We adapt off-the-shelf vision-language foundation models to provide pseudo-target supervision via two novel loss functions.
We demonstrate the effectiveness of our self-supervised approach on three audio-visual separation datasets.
arXiv Detail & Related papers (2023-03-28T22:45:40Z) - VATLM: Visual-Audio-Text Pre-Training with Unified Masked Prediction for
Speech Representation Learning [119.49605266839053]
We propose a unified cross-modal representation learning framework VATLM (Visual-Audio-Text Language Model)
The proposed VATLM employs a unified backbone network to model the modality-independent information.
In order to integrate these three modalities into one shared semantic space, VATLM is optimized with a masked prediction task of unified tokens.
arXiv Detail & Related papers (2022-11-21T09:10:10Z) - Audio-visual Generalised Zero-shot Learning with Cross-modal Attention
and Language [38.02396786726476]
We propose to learn multi-modal representations from audio-visual data using cross-modal attention.
In our generalised audio-visual zero-shot learning setting, we include all the training classes in the test-time search space.
Due to the lack of a unified benchmark in this domain, we introduce a (generalised) zero-shot learning benchmark on three audio-visual datasets.
arXiv Detail & Related papers (2022-03-07T18:52:13Z) - AVLnet: Learning Audio-Visual Language Representations from
Instructional Videos [69.56522471911396]
We introduce the Audio-Video Language Network (AVLnet), a self-supervised network that learns a shared audio-visual embedding space directly from raw video inputs.
We train AVLnet on HowTo100M, a large corpus of publicly available instructional videos, and evaluate on image retrieval and video retrieval tasks.
Our code, data, and trained models will be released at avlnet.csail.mit.edu.
arXiv Detail & Related papers (2020-06-16T14:38:03Z)
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.