shot scale into five classes: ECS (Extreme close-up shot), CS (close-up shot), MS (medium shot), FS (full shot), LS (long shot)
shot movement into four classes: Static, Motion, Pull, Push

Movienet dataset is used for finetuning the model for 5 epochs. v1_split_trailer.json provides the training, validation and test data splits.

Evaluation

Model achieves:

shot scale accuracy of 88.32% and macro-f1 of 88.57%
shot movement accuracy of 91.45% and macro-f1 of 80.8%

Class-wise accuracies:

shot scale: ECS - 90.92%, CS - 83.2%, MS - 85.0%, FS - 89.71%, LS - 94.55%
shot movement: Static - 94.6%, Motion - 87.7%, Pull - 57.5%, Push - 66.82%

Model Definition

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)

gullalc/videomae-base-finetuned-kinetics-movieshots-multitask

VideoMAE finetuned for shot scale and movement classification

Evaluation

Model Definition

gullalc/
videomae-base-finetuned-kinetics-movieshots-multitask