Unsupervised Extraction of Video Highlights Via Robust Recurrent Auto-encoders (1510.01442v1)

Abstract: With the growing popularity of short-form video sharing platforms such as \em{Instagram} and \em{Vine}, there has been an increasing need for techniques that automatically extract highlights from video. Whereas prior works have approached this problem with heuristic rules or supervised learning, we present an unsupervised learning approach that takes advantage of the abundance of user-edited videos on social media websites such as YouTube. Based on the idea that the most significant sub-events within a video class are commonly present among edited videos while less interesting ones appear less frequently, we identify the significant sub-events via a robust recurrent auto-encoder trained on a collection of user-edited videos queried for each particular class of interest. The auto-encoder is trained using a proposed shrinking exponential loss function that makes it robust to noise in the web-crawled training data, and is configured with bidirectional long short term memory (LSTM)~\cite{LSTM:97} cells to better model the temporal structure of highlight segments. Different from supervised techniques, our method can infer highlights using only a set of downloaded edited videos, without also needing their pre-edited counterparts which are rarely available online. Extensive experiments indicate the promise of our proposed solution in this challenging unsupervised settin

Unsupervised Extraction of Video Highlights Via Robust Recurrent Auto-encoders

The paper "Unsupervised Extraction of Video Highlights Via Robust Recurrent Auto-encoders" introduces an innovative approach to automatically extract highlights from videos without the need for supervision. The increasing prevalence of short-form video platforms such as Instagram and Vine has created demands for cohesive, automated highlight compilation from lengthy video inputs. Traditional methods rely heavily on heuristic approaches or supervised learning, requiring pre- and post-edited video pairs, which are challenging to source in large quantities from user-generated content. This paper proposes an unsupervised method that leverages recurrent auto-encoders to discern highlights solely from edited video content accessible on platforms like YouTube.

Methodology

The authors propose a robust recurrent auto-encoder, enhanced by a shrinking exponential loss function, as the cornerstone of the model. This mechanism is specifically designed to mitigate the impact of noisy web-crawled training data, predominantly composed of edited videos contributed by numerous users. The recurrent auto-encoder utilizes bidirectional LSTM cells to effectively model temporal dependencies inherent in video highlights, capturing not only immediate local sequence patterns but also broader contextual relationships among video snippets.

The process commences with temporal segmentation, wherein videos are divided into snippets ranging from 48 to 96 frames. Feature extraction is conducted using advanced C3D networks, providing computationally efficient 3D spatial-temporal representation compared to traditional methodologies like dense trajectory features. Through mean pooling, these features are distilled into the network input, enabling the auto-encoder to reconstruct them with high fidelity during training. Importantly, the shrinking exponential loss progressively reduces the influence of outliers, facilitating convergence by initially using a higher loss gradient to accelerate parameter calibration before diminishing it to focus on more clustered highlight data.

Results

The experimental evaluation underscored the proficiency of the proposed unsupervised approach, demonstrating competitive performance relative to modified supervised methods despite operating solely on edited video data. The results manifested notable improvements in mean average precision (mAP) across several domains like freeride, parkour, and surfing. The comparison between the recurrent auto-encoder (RRAE) and conventional unsupervised methods such as PCA and OCSVM yielded favorable outcomes, underscoring its superior capability in handling temporal video features for highlight detection.

Implications and Future Perspectives

The implications of adopting such a technique extend both practically and theoretically within the realms of AI-driven video processing. Practically, it offers a scalable and efficient solution for content creators and video editors, mitigating the labor-intensive process of manual highlight extraction. Theoretically, it provides a robust framework for further exploration into unsupervised video learning models, potentially stimulating advancements in automated video summarization, content curation, and even in domains beyond video analysis, such as anomaly detection in sequential data streams.

The introduction of shrinking exponential loss as a novel training strategy could inspire additional research into loss function design, promoting adaptive mechanisms in learning models. Moreover, with the growing prominence of sequential data capturing, assessing and enhancing the bi-directional LSTM integration within diverse auto-encoder applications remains a promising area for research dynamic.

Conclusively, this paper contributes significantly to the field by enabling large-scale, unsupervised extraction of meaningful video content, empowering AI systems to efficiently navigate and process the copious amounts of user-generated digital footage inherent in modern media landscapes.