Are you interested in classifying shot scales and movements in videos using advanced deep learning techniques? Look no further! In this article, we will guide you through the fine-tuning of the VideoMAE model, specifically designed for classifying shot types and their movements. Buckle up for a learning journey filled with twists and turns!

Understanding the Basics: Shot Type and Movement

Before we dive into the nuts and bolts of VideoMAE, let’s get a clearer picture of the objectives:

• Shot Scale Categories: You will classify shots into five distinct types:
  • ECS (Extreme Close-Up Shot)
  • CS (Close-Up Shot)
  • MS (Medium Shot)
  • FS (Full Shot)
  • LS (Long Shot)
• Shot Movement Categories: You’ll classify shot movements into four types:
  • Static
  • Motion
  • Pull
  • Push

Setting the Stage: The Model Architecture

The architecture of our model can be easily likened to a theater production. Just like a director orchestrates various elements on stage to create a compelling show, our model integrates different components to classify video shots effectively. Each part has its role— the processors analyze input data, while linear classification layers act as the performance reviews, determining how well each shot is executed.

Model Definition Code

Let’s take a look at the core structure of our custom VideoMAE model:

python
from transformers import VideoMAEImageProcessor, VideoMAEModel, VideoMAEConfig, PreTrainedModel

class CustomVideoMAEConfig(VideoMAEConfig):
    def __init__(self, scale_label2id=None, scale_id2label=None, movement_label2id=None, movement_id2label=None, **kwargs):
        super().__init__(**kwargs)
        self.scale_label2id = scale_label2id if scale_label2id is not None else {}
        self.scale_id2label = scale_id2label if scale_id2label is not None else {}
        self.movement_label2id = movement_label2id if movement_label2id is not None else {}
        self.movement_id2label = movement_id2label if movement_id2label is not None else {}

class CustomModel(PreTrainedModel):
    config_class = CustomVideoMAEConfig

    def __init__(self, config, model_name, scale_num_classes, movement_num_classes):
        super().__init__(config)
        self.vmae = VideoMAEModel.from_pretrained(model_name, ignore_mismatched_sizes=True)
        self.fc_norm = nn.LayerNorm(config.hidden_size) if config.use_mean_pooling else None
        self.scale_cf = nn.Linear(config.hidden_size, scale_num_classes)
        self.movement_cf = nn.Linear(config.hidden_size, movement_num_classes)

    def forward(self, pixel_values, scale_labels=None, movement_labels=None):
        vmae_outputs = self.vmae(pixel_values)
        sequence_output = vmae_outputs[0]
        if self.fc_norm is not None:
            sequence_output = self.fc_norm(sequence_output.mean(1))
        else:
            sequence_output = sequence_output[:, 0]

        scale_logits = self.scale_cf(sequence_output)
        movement_logits = self.movement_cf(sequence_output)

        if scale_labels is not None and movement_labels is not None:
            loss = F.cross_entropy(scale_logits, scale_labels) + F.cross_entropy(movement_logits, movement_labels)
            return {'loss': loss, 'scale_logits': scale_logits, 'movement_logits': movement_logits}
        return {'scale_logits': scale_logits, 'movement_logits': movement_logits}

scale_lab2id = {'ECS': 0, 'CS': 1, 'MS': 2, 'FS': 3, 'LS': 4}
scale_id2lab = {v: k for k,v in scale_lab2id.items()}
movement_lab2id = {'Static': 0, 'Motion': 1, 'Pull': 2, 'Push': 3}
movement_id2lab = {v: k for k,v in movement_lab2id.items()}

config = CustomVideoMAEConfig(scale_lab2id, scale_id2lab, movement_lab2id, movement_id2lab)
model = CustomModel(config, model_name, 5, 4)

Fine-Tuning Your Model

Now that we’ve established our model’s structure, it’s time to fine-tune it using the MovieNet dataset. This dataset provides the necessary video classifications, allowing your model to train for 5 epochs effectively.

Evaluating Your Model’s Performance

Your fine-tuned model will give you impressive results:

• Shot scale accuracy: 88.32% with a macro F1 score of 88.57%
• Shot movement accuracy: 91.45% with a macro F1 score of 80.8%

Additionally, the class-wise accuracies for shot scale and movement indicate the model’s strengths and opportunities for improvement.

Troubleshooting Common Issues

Even the best productions can face hiccups. Here are a few troubleshooting ideas to help you navigate through any potential issues:

• Low Accuracy: Ensure that your dataset is balanced. Misclassifications can often stem from an unequal representation of classes.
• Model Training Stalls: Check if your data processing pipeline is optimized; slow data loading may hamper training.
• Integration Errors: Make sure that all library imports and configurations are correct. If any part is overlooked, the model will struggle to function effectively.
• For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

With the VideoMAE model fine-tuned for shot scale and movement classification, you’re well on your way to enhancing your video analysis projects. At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.