SCSampler: Sampling Salient Clips from Video for Efficient Action Recognition (1904.04289v2)

Abstract: While many action recognition datasets consist of collections of brief, trimmed videos each containing a relevant action, videos in the real-world (e.g., on YouTube) exhibit very different properties: they are often several minutes long, where brief relevant clips are often interleaved with segments of extended duration containing little change. Applying densely an action recognition system to every temporal clip within such videos is prohibitively expensive. Furthermore, as we show in our experiments, this results in suboptimal recognition accuracy as informative predictions from relevant clips are outnumbered by meaningless classification outputs over long uninformative sections of the video. In this paper we introduce a lightweight "clip-sampling" model that can efficiently identify the most salient temporal clips within a long video. We demonstrate that the computational cost of action recognition on untrimmed videos can be dramatically reduced by invoking recognition only on these most salient clips. Furthermore, we show that this yields significant gains in recognition accuracy compared to analysis of all clips or randomly/uniformly selected clips. On Sports1M, our clip sampling scheme elevates the accuracy of an already state-of-the-art action classifier by 7% and reduces by more than 15 times its computational cost.

Overview of "SCSampler: Sampling Salient Clips from Video for Efficient Action Recognition"

The paper "SCSampler: Sampling Salient Clips from Video for Efficient Action Recognition" addresses the challenge of action recognition in untrimmed videos, which are common in real-world scenarios such as YouTube. These videos often contain lengthy segments with minimal activity relevant to action recognition. Applying a standard deep convolutional neural network (CNN) based action classifier densely over the entirety of these videos is computationally prohibitive and leads to diminished recognition accuracy due to the prevalence of uninformative clips. The proposed research introduces a streamlined "clip-sampling" model, SCSampler, designed to efficiently identify the most salient clips within a longer video, thus optimizing both the computational resources and the accuracy of action recognition.

Problem Context and the Proposed Solution

Most contemporary action recognition systems operate by applying a deep CNN over short clips with fixed temporal length, aggregating clip-level predictions across the entire video. While this is effective for short, well-trimmed videos, it becomes inefficient for long videos. The aggregation of uninformative clip predictions can reduce overall recognition fidelity.

SCSampler addresses this inefficiency through a lightweight network tasked with sampling only the most salient clips for further analysis by the action classifier. By restricting the computationally intensive classifier to run solely on these selected clips, the authors claim significant improvements in accuracy and dramatic reductions in runtime cost. On Sports1M, an action recognition benchmark, this clip sampling scheme enhances the classifier's accuracy by 7% and reduces its computational expense by more than 15-fold.

Technical Insights and Architectures

The design of SCSampler is predicated on efficiency, entailing the development of two primary variants. One variant operates directly on compressed video formats, eliminating the need for costly decoding processes. The other variant utilizes audio features, which are computationally less intensive than visual features, exploiting the semantic relationship between audio and video elements.

The sampler is independently trained to gauge clip saliency. Two learning objectives are explored: one aligns the sampling with the classifier’s existing bias towards certain patterns (classifier-dependent), while the second is classifier-agnostic, providing more extensive applicability across different models.

Empirical Evaluation and Strong Results

The experimental evaluations established significant performance improvements over various baselines. The authors compare their approach against methods that randomly or uniformly select clips as well as dense evaluation strategies (considering all clips). SCSampler demonstrated superior performance not only in accuracy but also in computational efficiency. For example, when employed with the ir-CSN-152 classifier on Sports1M, SCSampler improved accuracy by 7% over dense evaluation, marking a noteworthy advancement given the computational runtime reduction from 14 days to just under a day across the dataset.

Furthermore, SCSampler showed versatility, providing accuracy gains even on different datasets and classifiers, although the best performance was achieved when the sampler was trained for a specific dataset-classifier pair.

Implications and Future Directions

The research presents practical implications, making it feasible to deploy robust action recognition systems on large-scale, uncurated video collections. The theoretical contribution lies in demonstrating how targeted sampling of temporal segments can meaningfully lift performance metrics while reducing computational burdens, a principle that may be extrapolated to other sequential data challenges in AI.

Looking ahead, this work opens several avenues for further exploration. Future research can investigate more sophisticated aggregation schemes for the selected clips or extend the sampling approach to include inter-clip dependencies and redundancies. A deeper exploration of how this method could be integrated with existing real-time processing applications or adapted for new, unexplored domains in AI video processing could yield breakthroughs both in efficiency and performance.

In summary, the paper provides a robust framework for enhancing action recognition in unstructured video data, with compelling evidence for practical and theoretical advancement in AI and machine learning domains.