There has been a long-standing quest for a unified audio-visual-text model to
enable various multimodal understanding tasks, which mimics the listening,
seeing and reading process of human beings. Humans tends to represent knowledge
using two separate systems: one for representing verbal (textual) information
and one for representing non-verbal (visual and auditory) information. These
two systems can operate independently but can also interact with each other.
Motivated by this understanding of human cognition, in this paper, we introduce
CoAVT — a novel cognition-inspired Correlated Audio-Visual-Text pre-training
model to connect the three modalities. It contains a joint audio-visual encoder
that learns to encode audio-visual synchronization information together with
the audio and visual content for non-verbal information, and a text encoder to
handle textual input for verbal information. To bridge the gap between
modalities, CoAVT employs a query encoder, which contains a set of learnable
query embeddings, and extracts the most informative audiovisual features of the
corresponding text. Additionally, to leverage the correspondences between audio
and vision with language respectively, we also establish the audio-text and
visual-text bi-modal alignments upon the foundational audiovisual-text
tri-modal alignment to enhance the multimodal representation learning. Finally,
we jointly optimize CoAVT model with three multimodal objectives: contrastive
loss, matching loss and language modeling loss. Extensive experiments show that
CoAVT can learn strong multimodal correlations and be generalized to various
downstream tasks. CoAVT establishes new state-of-the-art performance on
text-video retrieval task on AudioCaps for both zero-shot and fine-tuning
settings, audio-visual event classification and audio-visual retrieval tasks on
AudioSet and VGGSound.
Expert Commentary: A Novel Approach to Multimodal Understanding
As a commentator in the field of multimedia information systems and related technologies, I find the concept of a unified audio-visual-text model for multimodal understanding tasks to be both intriguing and promising. The idea of mimicking the human listening, seeing, and reading process to enable machines to understand and interpret different modes of information is a significant step toward achieving more sophisticated artificial intelligence systems.
One key aspect highlighted in the article is the recognition that humans naturally represent knowledge using separate systems for verbal and non-verbal information. This recognition aligns well with the multi-disciplinary nature of the concepts discussed, as it draws upon cognitive science, human perception, and linguistics to inform the design of the model.
The proposed CoAVT (Correlated Audio-Visual-Text) model presents a novel approach to connect the three modalities: audio, visual, and text. By incorporating a joint audio-visual encoder that learns to encode audio-visual synchronization information along with the content, and a separate text encoder to handle textual input, CoAVT strives to bridge the gap between modalities and create a comprehensive representation of multimodal data.
One interesting feature of CoAVT is the use of a query encoder, which utilizes learnable query embeddings to extract informative audiovisual features from corresponding text. This approach emphasizes the importance of aligning audio, vision, and language in order to improve multimodal representation learning.
The article mentions that CoAVT is optimized through three multimodal objectives: contrastive loss, matching loss, and language modeling loss. These objectives provide a comprehensive training framework that aims to capture the correlations between different modalities and enhance the model’s ability to perform various downstream tasks.
In the experiments conducted, CoAVT demonstrated strong performance on different tasks, such as text-video retrieval, audio-visual event classification, and audio-visual retrieval. The achievement of state-of-the-art performance in these tasks indicates the potential of the proposed model in advancing the field of multimedia information systems and related technologies.
Overall, the CoAVT model presents a promising step toward achieving a unified audio-visual-text approach to multimodal understanding. Its emphasis on leveraging the interactions between different modalities and incorporating a comprehensive training framework showcases the multi-disciplinary nature of this research. With further development and refinement, CoAVT has the potential to significantly contribute to the fields of animations, artificial reality, augmented reality, and virtual realities by enabling more sophisticated and nuanced interpretations of multimodal data.