Joint-Centric Dual Contrastive Alignment with Structure-Preserving and Information-Balanced Regularization

HILBERT learns robust document-level audio-text representations for low-resource data. It uses joint-centric dual contrastive alignment and regularization to create balanced embeddings, enabling effective multimodal AI with limited datasets.

advanced1-2 days6 steps

The play

Define Low-Resource Multimodal Task
Identify a specific AI problem that requires document-level understanding from combined audio and text data, especially where labeled datasets are scarce.
Select Frozen Pre-trained Encoders
Choose and integrate pre-trained, frozen models (e.g., a Transformer for text, a specialized CNN or Transformer for audio) to extract initial features from each modality. Freezing these improves efficiency.
Design Cross-Attentive Fusion Layer
Implement a cross-attentive architecture to effectively combine and exchange information between the extracted audio and text features, creating a unified multimodal representation.
Implement Dual Contrastive Alignment
Apply a joint-centric dual contrastive loss function to align the learned audio and text embeddings in a shared latent space, ensuring robust and discriminative representation learning.
Add Structure-Preserving Regularization
Incorporate regularization terms that maintain the inherent structure of each modality's embeddings while balancing information flow, enhancing embedding quality and training stability.
Train with Limited Dataset
Fine-tune only the new cross-attentive fusion layer and projection heads on your specific, low-resource multimodal dataset using the combined contrastive and regularization losses.

Starter code

import torch
import torch.nn.functional as F

class ContrastiveLoss(torch.nn.Module):
    def __init__(self, temperature=0.07):
        super().__init__()
        self.temperature = temperature

    def forward(self, embedding_a, embedding_b):
        # Normalize embeddings for cosine similarity
        embedding_a = F.normalize(embedding_a, dim=1)
        embedding_b = F.normalize(embedding_b, dim=1)

        # Compute similarity matrix (logits)
        logits_ab = torch.matmul(embedding_a, embedding_b.T) / self.temperature
        logits_ba = torch.matmul(embedding_b, embedding_a.T) / self.temperature

        # Create labels for positive pairs (diagonal)
        labels = torch.arange(len(embedding_a), device=embedding_a.device)

        # Compute cross-entropy loss for both directions
        loss_a = F.cross_entropy(logits_ab, labels)
        loss_b = F.cross_entropy(logits_ba, labels)

        return (loss_a + loss_b) / 2

# Example Usage:
# Assuming you have audio_embeddings and text_embeddings from your encoders
# batch_size = 64
# embedding_dim = 768 # e.g., from a BERT-like model
# audio_embeddings = torch.randn(batch_size, embedding_dim)
# text_embeddings = torch.randn(batch_size, embedding_dim)

# contrastive_loss_fn = ContrastiveLoss(temperature=0.1)
# loss = contrastive_loss_fn(audio_embeddings, text_embeddings)
# print(f"Computed Contrastive Loss: {loss.item()}")

Source

Paperarxiv.org