Delving Deeper into Convolutional Networks for Learning Video Representations (1511.06432v4)

Abstract: We propose an approach to learn spatio-temporal features in videos from intermediate visual representations we call "percepts" using Gated-Recurrent-Unit Recurrent Networks (GRUs).Our method relies on percepts that are extracted from all level of a deep convolutional network trained on the large ImageNet dataset. While high-level percepts contain highly discriminative information, they tend to have a low-spatial resolution. Low-level percepts, on the other hand, preserve a higher spatial resolution from which we can model finer motion patterns. Using low-level percepts can leads to high-dimensionality video representations. To mitigate this effect and control the model number of parameters, we introduce a variant of the GRU model that leverages the convolution operations to enforce sparse connectivity of the model units and share parameters across the input spatial locations. We empirically validate our approach on both Human Action Recognition and Video Captioning tasks. In particular, we achieve results equivalent to state-of-art on the YouTube2Text dataset using a simpler text-decoder model and without extra 3D CNN features.

• The paper introduces a novel GRU-based model that integrates multi-level CNN percepts to capture both local and global spatio-temporal video patterns.
• The modified GRU with convolutional operations reduces high-dimensionality, enabling efficient learning from both low-level and high-level features.
• Empirical validation shows a 3.4% improvement on UCF101 and a 10% BLEU increase on YouTube2Text, highlighting its impact on video analysis tasks.

Delving Deeper into Convolutional Networks for Learning Video Representations

This paper presents an innovative approach for learning spatio-temporal features in videos, leveraging Gated Recurrent Units (GRUs) in combination with visual "percepts" extracted from various levels of a deep convolutional network. The model is distinctive in its integration of both low-level and high-level percepts from a pre-trained CNN, thus enabling it to capture finer motion details while maintaining high discriminative power.

Methodology

The approach utilizes GRUs to model temporal dependencies across video frames, but introduces a variant of GRU that incorporates convolutional operations. This modification enforces sparse connectivity and parameter sharing across spatial locations, effectively mitigating the high dimensionality typically encountered when using low-level percepts. Through this design, the model captures both local and global spatio-temporal video patterns.

Key to this methodology is the hierarchical use of convolutional maps from the CNN pretrained on ImageNet. By extracting and leveraging visual percepts at multiple resolutions, the model enhances its capacity to recognize actions and generate video captions without relying on additional 3D CNN features.

Empirical Validation

The proposed model is empirically validated on two tasks: Human Action Recognition using the UCF101 dataset, and Video Captioning using the YouTube2Text dataset. The empirical results are significant:

• For action recognition, the model achieves a $3.4\%$ improvement over baseline methods on RGB inputs. This advancement underscores the importance of capturing multi-resolution temporal variations.
• In video captioning, the model improves BLEU scores by $10\%$ compared to traditional methods using only linear classifiers, demonstrating that multi-layer percept utilization offers substantial gains.

Furthermore, the effectiveness of the GRU-RCN is evident when comparing its performance to other recurrent convolution network architectures that typically focus only on high-level percepts.

Implications and Future Directions

The implications of these findings are noteworthy for both theoretical exploration and practical application. By demonstrating that leveraging percepts across different CNN layers can significantly enhance video representation, this work opens avenues for developing more efficient models in video analysis. From an application perspective, these findings could influence the design of systems requiring nuanced understanding of video content, such as autonomous navigation, surveillance, and interactive media technologies.

Future research could explore the integration of this model with larger datasets or adaptative learning techniques. Additionally, an in-depth analysis of the trade-offs between computational complexity and performance could guide future model enhancements, particularly in edge computing scenarios where resources are constrained.

In conclusion, this research contributes to the field of video representation learning by proposing a GRU-based model that effectively integrates convolutional operations for capturing spatio-temporal patterns. The model's performance on standard benchmarks affirms its potential as a robust framework for various video analysis tasks.