Clockwork Convnets for Video Semantic Segmentation (1608.03609v1)

Abstract: Recent years have seen tremendous progress in still-image segmentation; however the na\"ive application of these state-of-the-art algorithms to every video frame requires considerable computation and ignores the temporal continuity inherent in video. We propose a video recognition framework that relies on two key observations: 1) while pixels may change rapidly from frame to frame, the semantic content of a scene evolves more slowly, and 2) execution can be viewed as an aspect of architecture, yielding purpose-fit computation schedules for networks. We define a novel family of "clockwork" convnets driven by fixed or adaptive clock signals that schedule the processing of different layers at different update rates according to their semantic stability. We design a pipeline schedule to reduce latency for real-time recognition and a fixed-rate schedule to reduce overall computation. Finally, we extend clockwork scheduling to adaptive video processing by incorporating data-driven clocks that can be tuned on unlabeled video. The accuracy and efficiency of clockwork convnets are evaluated on the Youtube-Objects, NYUD, and Cityscapes video datasets.

• The paper demonstrates that exploiting semantic stability in video allows for reduced computation by updating deeper layers less frequently.
• It introduces a novel approach using fixed and adaptive scheduling to match network update rates with semantic changes in video frames.
• Empirical results on datasets like Cityscapes validate a favorable trade-off between efficiency and segmentation accuracy.

Overview of "Clockwork Convnets for Video Semantic Segmentation"

The paper "Clockwork Convnets for Video Semantic Segmentation" addresses the computational challenges inherent in the direct application of still-image semantic segmentation techniques to video processing. The authors posit that, by exploiting the temporal continuity present in video, semantic content changes more gradually than pixel-level changes. Thus, they introduce a novel framework called "clockwork convolutional networks" (convnets) which effectively utilizes spatiotemporal consistency to optimize the execution of video segmentation tasks.

Key Contributions

The research centers on two main observations: temporal redundancy in video data and application adaptability of execution schedules as an architectural feature. Based on these observations, the authors propose an approach that:

1. Exploits Semantic Stability: Recognizes that while pixels change quickly between frames, the semantics of a scene evolve more slowly, allowing insights from prior frames to inform the current frame's processing.
2. Dynamic Execution Schedules: Introduces "clockwork" convnets that execute at varying update rates across different layers, driven by either fixed or adaptive clocks tuned to semantic stability.

These methods aim to reduce both latency and overall computational demands in video processing by selectively varying the update schedule of network layers according to their feature "velocity," defined as the rate of semantic content change.

Methodology

The authors present a systematic approach to decomposing network stages and scheduling them to optimize video semantic segmentation. Initially, they evaluate fixed schedules defining multiple pipeline stages executing at different rates. For instance, deeper layers, which encapsulate stable semantic features, are updated less frequently than more volatile shallow layers. This strategy leverages already-computed information, drastically reducing redundant calculations.

Moreover, clockwork execution is extended to adaptive scheduling, where decisions to update layers are based on data-driven clocks sensitive to scene semantics. These adaptive schedules dynamically vary the extent of computation based on detected scene changes—potentially on the fly—improving practical efficiency without sacrificing accuracy.

Empirical Evaluation

The efficacy of clockwork convnets is demonstrated across several challenging datasets, including YouTube-Objects, NYUD, and Cityscapes, which reflect diverse video segmentation scenarios. The authors showcase how their method achieves a favorable trade-off between computational efficiency and semantic segmentation accuracy. Notably, key results highlighted show a reduction in computational demands without substantial degradation in network performance.

Vital experiments on fixed and adaptive scheduling approaches empirically validate their hypothesis regarding deep feature stability, underpin notion of hierarchical execution tailored to video content dynamics.

Implications and Future Directions

Undeniably, the introduction of clockwork convnets presents significant implications for real-time video processing applications, such as autonomous driving, surveillance systems, and robotics. This paper provides the groundwork for further exploration into how execution mechanisms can be integrated into neural architectures to support efficient, context-aware video processing.

Looking forward, the development of more sophisticated adaptive scheduling strategies, possibly leveraging reinforcement learning or other advanced machine learning techniques, could drive improvements in online decision-making processes for these networks. Moreover, the integration of more advanced spatiotemporal handling capabilities, potentially via connections with recurrent neural architectures or attention mechanisms, indicates promising avenues for future research aimed at achieving even higher degrees of efficiency and accuracy in video semantic segmentation.

In summary, this work lays the foundation for broader explorations into temporally adaptive neural network architectures, aligning processing methodologies with intrinsic data properties, and setting a precedent for future research to optimize neural networks for video applications.