24. Advanced Multimodal Project

This chapter integrates previous concepts into a complete advanced multimodal project. The example implements a real-time video + audio + text multimodal system for activity recognition.

System architecture combines temporal video features, spectral audio features, and transcription text features through a transformer fusion layer. The project demonstrates best practices for data handling, model architecture, training, optimization, and deployment.

# Complete multimodal activity recognition system
class ActivityRecognitionSystem:
    def __init__(self, config):
        self.video_encoder = VideoTransformer(**config['video'])
        self.audio_encoder = AudioCNN(**config['audio'])
        self.text_encoder = TextTransformer(**config['text'])
        
        # Cross-modal attention
        self.cross_attention = CrossModalAttention(
            hidden_dim=config['fusion']['hidden_dim'],
            num_heads=config['fusion']['num_heads']
        )
        
        self.classifier = nn.Linear(
            config['fusion']['hidden_dim'] * 3,
            config['num_activities']
        )
        
        self.postprocess = ActivitySmoothing(window_size=5)
    
    def forward(self, batch):
        video_out = self.video_encoder(batch['frames'])
        audio_out = self.audio_encoder(batch['spectrograms'])
        text_out = self.text_encoder(batch['transcript_tokens'])
        
        # Cross-modal fusion
        fused = self.cross_attention(video_out, audio_out, text_out)
        
        logits = self.classifier(fused)
        return self.postprocess(logits)

# Inference optimization
@torch.no_grad()
@torch.cuda.amp.autocast()
def optimized_forward(self, batch):
    # Fused operations, no gradient tracking
    return self(batch)

Training strategy uses curriculum learning, starting with single-modality tasks and progressively introducing cross-modal objectives. This approach stabilizes training by establishing strong individual modality representations before learning to integrate them.

Evaluation validates across multiple dimensions: per-modality accuracy, cross-modal agreement, temporal consistency, and inference latency. The final system must meet accuracy thresholds while operating within real-time constraints.

Local verification checkpoint

Run the smallest example from this chapter in a local workspace and record the package version, runtime, data path, and observed output. If the result depends on model size, vector count, CPU/GPU backend, or available memory, note that constraint beside the exercise so the lesson remains reproducible.

Local verification checkpoint

Run the smallest example from this chapter in a local workspace and record the package version, runtime, data path, and observed output. If the result depends on model size, vector count, CPU/GPU backend, or available memory, note that constraint beside the exercise so the lesson remains reproducible.