Are you interested in advancing your video analysis capabilities? In this guide, we will walk you through how to utilize the VideoMAE model fine-tuned for classifying shot scale and movement using Python. Whether you’re a beginner or an experienced programmer, our step-by-step approach will make this complex topic accessible and engaging.

Understanding the Basics

First, let’s unravel what we mean by shot scale and shot movement. Think of a movie scenario where a camera tracks a character. Depending on how close the camera is positioned, we can classify the shot into specific categories:

• Shot Scale: This is categorized into five classes:
  • ECS (Extreme Close-up Shot)
  • CS (Close-up Shot)
  • MS (Medium Shot)
  • FS (Full Shot)
  • LS (Long Shot)
• Shot Movement: This is divided into four types:
  • Static
  • Motion
  • Pull
  • Push

The Model and Code Setup

The VideoMAE model is trained using the Movienet dataset for efficient classification. The model achieves remarkable accuracy rates, with shot scale accuracy reaching 88.32% and shot movement accuracy at 91.45%. Let’s delve into the necessary code structure.

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, scale_logits, movement_logits
        return scale_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)

Breaking Down the Code

Imagine that building the VideoMAE model is akin to setting up an advanced factory for movie-making:

• CustomVideoMAEConfig: Think of this as your factory manager, ensuring all the workers (model parameters) know their tasks. It sets up the labels for the different types of shots and movements.
• CustomModel: This is like the skilled workers operating machines in your factory. It utilizes the main VideoMAE model to process and classify the video shots.
• Forward Method: Here, the workers process each video resource, produce scale and movement outputs, and when prompted, measure their efficiency (loss).

Evaluating the Model

The model’s performance is impressive, with class-wise accuracies for shot scale and shot movement. These performance metrics help you understand how well your model is doing and where improvements could be made.

Troubleshooting Tips

If you’re facing any issues while implementing the model, consider the following troubleshooting steps:

• Check your data splits in v1_split_trailer.json to ensure proper training.
• Ensure you’re using compatible libraries and versions, specifically transformers.
• Review the model architecture if discrepancies arise in expected input/output shapes.

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Conclusion

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.