End-to-End Learning of Motion Representation for Video Understanding (1804.00413v1)

Abstract: Despite the recent success of end-to-end learned representations, hand-crafted optical flow features are still widely used in video analysis tasks. To fill this gap, we propose TVNet, a novel end-to-end trainable neural network, to learn optical-flow-like features from data. TVNet subsumes a specific optical flow solver, the TV-L1 method, and is initialized by unfolding its optimization iterations as neural layers. TVNet can therefore be used directly without any extra learning. Moreover, it can be naturally concatenated with other task-specific networks to formulate an end-to-end architecture, thus making our method more efficient than current multi-stage approaches by avoiding the need to pre-compute and store features on disk. Finally, the parameters of the TVNet can be further fine-tuned by end-to-end training. This enables TVNet to learn richer and task-specific patterns beyond exact optical flow. Extensive experiments on two action recognition benchmarks verify the effectiveness of the proposed approach. Our TVNet achieves better accuracies than all compared methods, while being competitive with the fastest counterpart in terms of features extraction time.

• The paper introduces TVNet, a novel architecture that reformulates TV-L1 optical flow into trainable neural network layers for video understanding.
• It achieves competitive action recognition performance on benchmarks like HMDB51 and UCF101 while reducing computational and storage demands.
• TVNet’s modular design paves the way for efficient, real-time video analysis and further exploration of spatiotemporal feature learning in dynamic scenes.

End-to-End Learning of Motion Representation for Video Understanding

The paper introduces TVNet, a novel neural network architecture explicitly designed for learning optical-flow-like features within an end-to-end framework to advance video understanding tasks. Despite advances in convolutional neural networks (CNNs) for image-based tasks, the authors highlight the lack of equivalent strides in video analysis, largely attributed to the unsolved issue of effective spatiotemporal feature learning. Optical flow, a classical method capturing motion between frames, remains a predominant technique due to its effectiveness; yet, it suffers from inefficiencies and a lack of integration with CNN pipelines.

The central innovation of TVNet lies in its explicit integration of the TV-L1 optical flow method into a trainable neural network framework. This is achieved by reformulating and unfolding iterations of the TV-L1 method as neural network layers, with convolutions replacing traditional gradient and divergence computations and bilinear interpolation supplanting traditional warping techniques. Furthermore, key parameters within this structure, such as initial flow fields and convolutional filters, are relaxed to be trainable, thus enabling the network to learn more refined, task-specific motion features over time.

A significant advantage of TVNet is its efficiency. The architecture is designed with a considerably smaller number of iterative layers than traditional TV-L1 implementations, supporting fast computation without needing ground-truth optical flow for pre-training. This leads to TVNet's ability to deliver results comparable or superior to state-of-the-art methods, such as FlowNet2.0, DeepFlow, and DIS-Fast, with reduced computational resources and storage requirements.

Experimental validation on the HMDB51 and UCF101 action recognition benchmarks showcases the competitive performance of TVNet with traditional two-stream methods, thanks to its end-to-end optimization capability which avoids the cumbersome two-stage training pipeline of extracting and then processing optical flow. Importantly, the inclusion of additional flow-specific loss alongside task-specific objectives aids in maintaining optical-flow-like outputs that are meaningful and robust across different tasks, as demonstrated by quantitative improvements over non-trainable flow estimators.

In theoretical terms, TVNet opens new avenues for approaching motion representation by blending traditional optimization-based flow methods and deep learning. Practically, this integration allows for simultaneous advancements in both efficiency and performance in video-processing applications. The transformability of TVNet into trainable models signifies significant implications for deploying real-time video understanding systems, especially where resource constraints make storage and computational efficiency paramount.

Looking forward, this research points towards further exploration into more complex dynamic video analysis tasks, potentially using larger pretrained models or datasets to see if such models can simultaneously learn more comprehensive representations of motion without losing specificity. The modular nature of TVNet also suggests opportunities for it to be expanded with additional capabilities, such as integrating learning mechanisms for complex temporal dependencies or broader multimodal video content beyond pure optical flow. As neural network architectures continue to evolve, methods like TVNet will likely be foundational in the synthesis of handcrafted feature strategies with modern, data-driven learning frameworks.