RED: Reinforced Encoder-Decoder Networks for Action Anticipation (1707.04818v1)

Abstract: Action anticipation aims to detect an action before it happens. Many real world applications in robotics and surveillance are related to this predictive capability. Current methods address this problem by first anticipating visual representations of future frames and then categorizing the anticipated representations to actions. However, anticipation is based on a single past frame's representation, which ignores the history trend. Besides, it can only anticipate a fixed future time. We propose a Reinforced Encoder-Decoder (RED) network for action anticipation. RED takes multiple history representations as input and learns to anticipate a sequence of future representations. One salient aspect of RED is that a reinforcement module is adopted to provide sequence-level supervision; the reward function is designed to encourage the system to make correct predictions as early as possible. We test RED on TVSeries, THUMOS-14 and TV-Human-Interaction datasets for action anticipation and achieve state-of-the-art performance on all datasets.

• The paper introduces a novel RED network that leverages reinforcement learning to provide early and accurate action anticipation.
• It employs an encoder-decoder architecture with LSTM, predicting a sequence of future video representations from historical inputs.
• Experimental evaluations on benchmarks demonstrate that RED outperforms fixed-frame models, enhancing responsiveness in applications like surveillance and robotics.

Reinforced Encoder-Decoder Networks for Action Anticipation

The paper "RED: Reinforced Encoder-Decoder Networks for Action Anticipation" presents a novel approach to the problem of action anticipation, addressing some of the challenges faced by existing methodologies such as the reliance on single-frame predictions and the constraint of anticipating actions only at fixed times in the future. The authors propose a Reinforced Encoder-Decoder (RED) network architecture that advances the field by utilizing multiple historical representations to predict sequences of future video frames and their respective actions.

Overview of the Proposed Architecture

The RED network consists of a few key components:

1. Video Representation Extractor: It processes video chunks, each spanning 6 frames, to obtain features that can be fed into the encoder-decoder framework.
2. Encoder-Decoder Network: This core module uses LSTM networks to encode a sequence of historical video representations and output a predicted sequence of future representations. This design allows the model to capture the temporal dynamics of actions over time, addressing the limitations of single-frame methods.
3. Classification Network: It further refines the anticipated representations from the decoder to produce predictions of action categories.
4. Reinforcement Learning Module: A substantial contribution of this work is the integration of a reinforcement learning component that provides sequence-level feedback to the encoder-decoder architecture. The reward function is crafted to incentivize early and accurate action anticipation.

Experimental Evaluation and Results

The RED network was evaluated on several benchmark datasets, namely TVSeries, THUMOS-14, and TV-Human-Interaction. The results demonstrate that the RED network achieves state-of-the-art performance in action anticipation, outperforming previous methods, including those utilizing LSTMs and handcrafted feature-based approaches.

In particular, the RED network showed an appreciable gain over baseline models—such as single-frame and fixed-timeframe anticipation networks—by leveraging its ability to predict a sequence of future states. The incorporation of reinforcement learning into the optimization process provided further improvements, as evidenced by consistent performance gains across different anticipation times.

Implications and Future Directions

The RED network's capacity to anticipate actions accurately and early, while considering a sequence of future frames, offers substantial practical benefits for real-world applications in surveillance and robotics. By making predictions based on multiple frames and games early in the action sequence, this approach could improve the responsiveness and effectiveness of automated systems in dynamic environments.

Theoretically, the introduction of sequence-level supervision in the form of reinforcement learning highlights a compelling direction for advancing anticipation models. Future developments could explore more sophisticated reward structures or alternative sequence-prediction paradigms to enhance anticipatory modeling further. Additionally, investigating the extension of this framework to other domains, such as natural language processing for text anticipation or multi-modal data fusion, could broaden its applicability.

In conclusion, the RED network provides a notable contribution to the field of action anticipation through its innovative use of reinforcement learning and an encoder-decoder architecture, setting a potential standard for future research in predictive video analysis.