Add MM-VID: Novel Cross-Attention Approach for Multimodal Video Under…

multimodal/mm-vid-multimodal-cross-attention.md

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+# MM-VID: Advancing Video Understanding through Multi-Modal Cross-Attention
+
+## Overview
+A groundbreaking approach to multimodal video understanding that achieves state-of-the-art performance while reducing computational requirements by 30%. The model's novel cross-attention mechanism efficiently processes visual, audio, and textual data streams simultaneously.
+
+## Technical Details
+- Architecture: Multi-stream transformer with cross-modal attention
+- Input Modalities: Video, Audio, Text
+- Key Innovation: Adaptive cross-attention mechanism
+- Performance: 30% reduction in computational resources with improved accuracy
+
+## Code Example
+```python
+class MMVidCrossAttention(nn.Module):
+    def __init__(self, dim, num_heads):
+        super().__init__()
+        self.num_heads = num_heads
+        self.qkv = nn.Linear(dim, dim * 3)
+
+    def forward(self, x):
+        B, N, C = x.shape
+        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads)
+        # ... rest of implementation