Shot Contrastive Self-Supervised Learning for Scene Boundary Detection (2104.13537v1)

Abstract: Scenes play a crucial role in breaking the storyline of movies and TV episodes into semantically cohesive parts. However, given their complex temporal structure, finding scene boundaries can be a challenging task requiring large amounts of labeled training data. To address this challenge, we present a self-supervised shot contrastive learning approach (ShotCoL) to learn a shot representation that maximizes the similarity between nearby shots compared to randomly selected shots. We show how to apply our learned shot representation for the task of scene boundary detection to offer state-of-the-art performance on the MovieNet dataset while requiring only ~25% of the training labels, using 9x fewer model parameters and offering 7x faster runtime. To assess the effectiveness of ShotCoL on novel applications of scene boundary detection, we take on the problem of finding timestamps in movies and TV episodes where video-ads can be inserted while offering a minimally disruptive viewing experience. To this end, we collected a new dataset called AdCuepoints with 3,975 movies and TV episodes, 2.2 million shots and 19,119 minimally disruptive ad cue-point labels. We present a thorough empirical analysis on this dataset demonstrating the effectiveness of ShotCoL for ad cue-points detection.

• The paper introduces Shot Contrastive Learning (ShotCoL), a novel self-supervised method for scene boundary detection in long-form videos.
• ShotCoL employs a contrastive learning framework where temporally adjacent shots are used as positive pairs to train visual and audio encoders.
• ShotCoL achieves state-of-the-art performance on the MovieNet dataset with 52.83% average precision, using fewer labels than previous methods and demonstrating generalizability.

Shot Contrastive Self-Supervised Learning for Scene Boundary Detection

The paper "Shot Contrastive Self-Supervised Learning for Scene Boundary Detection" presents a novel approach called Shot Contrastive Learning (ShotCoL) aimed at addressing the challenging problem of scene boundary detection within long-form videos such as movies and TV episodes. The authors leverage self-supervised learning methodologies to build effective representations that capture the subtle temporal structures inherent in cinematic narratives, enabling the detection of scene changes with greater accuracy and efficiency than existing methods.

Overview of Methodology

The primary innovation in this work lies in extending the principles of contrastive learning to video scenes by devising a new augmentation strategy tailored to the structural patterns of film production. ShotCoL assumes that temporally adjacent shots typically share consistent elements such as actors and location settings, given their role in conveying a cohesive segment of the story. This hypothesis forms the basis of utilizing nearby shot sequences as positive pairs while treating randomly selected shots as negatives, thus training shot encoders using contrastive loss to distinguish real transitions.

Key components of ShotCoL include:

• Encoder Networks: Separate encoders for visual and audio modalities ensure comprehensive feature extraction. The visual encoder leverages ResNet architectures while the audio function uses mono audio samples processed through specialized neural networks.
• Contrastive Learning Framework: The representation learning employs a query-key mechanism wherein nearby shots are contrasted against one another to maximize the relevance of shot sequences within the same scenes. A queue-based approach, inspired by the momentum contrastive learning schema, offers robust dictionary-based training, updating the encoder models with consistency across batches.
• Application of Shot Representation: With the learned representation, a supervised layer is added wherein scene boundary detection is framed as a binary classification task. Queries are constructed around shot boundaries and evaluated using a context-enriched MLP classifier to validate scene segmentation points.

Numerical Results and Performance

ShotCoL demonstrates state-of-the-art effectiveness on the MovieNet dataset, surpassing prior leading approaches such as LGSS, which relied on larger and multimodal model configurations. Key performance metrics highlight that ShotCoL requires significantly fewer training labels yet achieves higher average precision and recall rates. An impressive finding from the paper shows that ShotCoL achieves a superior average precision of 52.83%, with reduced computational complexity and operational overhead compared to previous systems.

Furthermore, an empirical analysis on a new dataset, AdCuepoints, illustrates the generalizability of ShotCoL in detecting minimally disruptive ad insertion points. With its novel feature encoding, ShotCoL outperformed traditional image and video-based models and demonstrated efficacy under cross-dataset scenarios where model training on one dataset was applied to another.

Implications and Future Directions

From a theoretical standpoint, this research underlines the growing importance of self-supervised learning in video understanding, particularly in the context of semantic segmentation where vast, unlabeled datasets are prevalent. Practically, ShotCoL represents a promising advancement for video editing applications like automated scene annotation and resilient content-moderation mechanisms within streaming services.

Looking forward, efforts to further optimize contrastive learning processes for video data could involve exploring asymmetric data augmentation strategies or integrating additional modalities for richer semantic encoding. Expanding ShotCoL’s focus on diverse domains or varied temporal events could potentially enhance AI’s capability to recognize complex narrative structures within multimedia content. AI researchers might be inspired by ShotCoL’s design to rethink augmentation techniques and pretext tasks in other domains of sequential data evaluation.