Information Router for Mitigating Modality Dominance in Vision-Language Models

1. 1
   Diagnose Modality Dominance

   Identify instances where your VLM's predictions are disproportionately influenced by a single input modality (visual or linguistic), leading to biased or inaccurate results.
2. 2
   Analyze Current Information Flow

   Examine your existing VLM architecture to understand how visual and linguistic features are processed, combined, and weighted before the final prediction. Pinpoint potential bottlenecks or fixed fusion points.
3. 3
   Design a Dynamic Router Component

   Propose an architectural component, an 'Information Router,' positioned to dynamically control and balance information flow from different modalities. Consider mechanisms beyond simple attention.
4. 4
   Implement Routing Logic

   Develop the internal logic for the router. This could involve adaptive gating, learned weighting, or context-aware fusion mechanisms that adjust modality contributions based on the input data itself. Integrate this into your VLM's forward pass.
5. 5
   Evaluate Robustness and Accuracy

   Train and test the VLM with the integrated Information Router. Use diverse datasets and evaluation metrics to measure improvements in overall accuracy, robustness against modality biases, and generalization across tasks.

import torch
import torch.nn as nn

class InformationRouter(nn.Module):
    def __init__(self, visual_dim, text_dim, output_dim):
        super().__init__()
        # Example: Simple gating mechanism based on combined features
        self.gate_weights = nn.Linear(visual_dim + text_dim, 2) # Output 2 weights (visual, text)
        self.fusion_layer = nn.Linear(visual_dim + text_dim, output_dim)

    def forward(self, visual_features, text_features):
        # Concatenate features to inform routing decision
        combined_features = torch.cat((visual_features, text_features), dim=-1)

        # Learn dynamic weights for each modality
        routing_scores = torch.softmax(self.gate_weights(combined_features), dim=-1)
        visual_weight, text_weight = routing_scores.chunk(2, dim=-1)

        # Apply weights to original features
        routed_visual = visual_features * visual_weight
        routed_text = text_features * text_weight

        # Fuse the routed features
        fused_output = self.fusion_layer(torch.cat((routed_visual, routed_text), dim=-1))
        return fused_output

# Example Usage (conceptual VLM integration):
# Assuming visual_features and text_features are outputs from respective encoders
# visual_input_tensor = torch.randn(1, 768) 
# text_input_tensor = torch.randn(1, 768)   

# router_module = InformationRouter(visual_dim=768, text_dim=768, output_dim=1024)
# final_fused_output = router_module(visual_input_tensor, text_input_tensor)
# print(f"Output shape: {final_fused_output.shape}") # Expected: torch.Size([1, 1024])

• Paperarxiv.org