Object-aware Aggregation with Bidirectional Temporal Graph for Video Captioning (1906.04375v1)

Abstract: Video captioning aims to automatically generate natural language descriptions of video content, which has drawn a lot of attention recent years. Generating accurate and fine-grained captions needs to not only understand the global content of video, but also capture the detailed object information. Meanwhile, video representations have great impact on the quality of generated captions. Thus, it is important for video captioning to capture salient objects with their detailed temporal dynamics, and represent them using discriminative spatio-temporal representations. In this paper, we propose a new video captioning approach based on object-aware aggregation with bidirectional temporal graph (OA-BTG), which captures detailed temporal dynamics for salient objects in video, and learns discriminative spatio-temporal representations by performing object-aware local feature aggregation on detected object regions. The main novelties and advantages are: (1) Bidirectional temporal graph: A bidirectional temporal graph is constructed along and reversely along the temporal order, which provides complementary ways to capture the temporal trajectories for each salient object. (2) Object-aware aggregation: Learnable VLAD (Vector of Locally Aggregated Descriptors) models are constructed on object temporal trajectories and global frame sequence, which performs object-aware aggregation to learn discriminative representations. A hierarchical attention mechanism is also developed to distinguish different contributions of multiple objects. Experiments on two widely-used datasets demonstrate our OA-BTG achieves state-of-the-art performance in terms of BLEU@4, METEOR and CIDEr metrics.

Overview of Object-aware Aggregation with Bidirectional Temporal Graph for Video Captioning

The paper presents a novel approach to video captioning titled "Object-aware Aggregation with Bidirectional Temporal Graph" (OA-BTG). This research focuses on addressing the complexities in generating accurate and nuanced natural language descriptions of video content by leveraging detailed object information and salient spatio-temporal representations. Video captioning is a challenging task that necessitates not just a global understanding of visual content but also the ability to capture intricate object-level dynamics. The OA-BTG method advances the state of the art by constructing bidirectional temporal graphs that capture these dynamics and employing object-aware aggregation for superior performance in video captioning.

Methodological Details

1. Bidirectional Temporal Graph (BTG):
   • The OA-BTG approach integrates a bidirectional temporal graph to encapsulate object trajectories within videos. This graph operates forward and backward along the temporal order, offering complementary insights into object dynamics. The dual-direction graph ensures no loss of information due to temporal progression, effectively capturing all relevant object actions across frames.
2. Object-aware Aggregation:
   • This process involves learnable VLAD (Vector of Locally Aggregated Descriptors) models applied to both object trajectories and global frame sequences. A hierarchical attention mechanism further refines these representations by distinguishing the varying contributions of different objects to the narrative video depiction.

Experimental Validation

The effectiveness of OA-BTG was validated through experiments on MSVD and MSR-VTT datasets. The model achieved superior results across BLEU@4, METEOR, and CIDEr metrics, underscoring its capability in generating high-quality video captions. Specifically, the model outperformed existing methods such as SeqVLAD and LSTM-GAN, indicating the robustness of bidirectional graphs and object-aware aggregation in capturing fine-grained, dynamic video details.

Implications and Future Directions

The OA-BTG framework significantly enhances the translation of video dynamics into language, providing a robust methodology that aligns with the complex temporal and spatial demands of video data. The introduction of bidirectional temporal graphs marks a pivotal step towards achieving more comprehensive spatio-temporal reasoning within video captioning tasks.

Future research directions may consider further exploration into the construction of more sophisticated graph models that capture interaction between objects across time frames more effectively. Exploring end-to-end frameworks that integrate backward-forward dynamics more holistically within a unified system also presents an enticing avenue. Furthermore, fine-tuning the aggregation mechanisms and potentially incorporating additional modalities could offer even richer descriptions, expanding the applicability and versatility of video captioning solutions in real-world applications.

In summary, the OA-BTG method provides a substantial leap forward in accurately describing video content, opening new pathways for deploying AI-driven video understanding in complex scenarios such as video indexing, summarization, and aiding visually impaired individuals.