Spatio-Temporal Graph for Video Captioning with Knowledge Distillation (2003.13942v1)

Abstract: Video captioning is a challenging task that requires a deep understanding of visual scenes. State-of-the-art methods generate captions using either scene-level or object-level information but without explicitly modeling object interactions. Thus, they often fail to make visually grounded predictions, and are sensitive to spurious correlations. In this paper, we propose a novel spatio-temporal graph model for video captioning that exploits object interactions in space and time. Our model builds interpretable links and is able to provide explicit visual grounding. To avoid unstable performance caused by the variable number of objects, we further propose an object-aware knowledge distillation mechanism, in which local object information is used to regularize global scene features. We demonstrate the efficacy of our approach through extensive experiments on two benchmarks, showing our approach yields competitive performance with interpretable predictions.

Spatio-Temporal Graph for Video Captioning with Knowledge Distillation

The paper presents an innovative approach to video captioning by focusing on the integration of spatio-temporal graph models and the technique of knowledge distillation. The proposed method emphasizes the importance of modeling object interactions in both space and time within videos, which is crucial for generating meaningful captions. The authors argue that traditional video captioning models that neglect explicit modeling of such interactions often lead to suboptimal performance and overfit to spurious data correlations.

Model Overview and Core Contributions

The research introduces a two-branch network structure: an object branch and a scene branch. The object branch is responsible for modeling spatio-temporal interactions via a graph-based approach. Nodes in this graph represent objects detected in video frames, while edges capture spatial and temporal relations between these objects. The spatial graph is constructed based on the normalized Intersection over Union (IoU) of detected object bounding boxes within each frame, while temporal edges are established through cosine similarity, connecting similar objects across consecutive frames. Graph convolution operations are then applied to update object features, ultimately yielding a refined representation capturing interactions among various objects in the video.

Concurrently, the scene branch provides a global view by independently modeling frame sequences using both 2D and 3D scene features derived from ResNet-101 and I3D. This approach aids in situations where limited or no objects are detected in certain frames, offering contextual framing supportive to the object-level information.

A significant contribution of the paper is the implementation of an object-aware knowledge distillation mechanism. This method enhances the learning process within the two-branch network by aligning language logits between branches, thus allowing the object branch to guide the scene branch without sharing noisy features. By soft regularization through distillation, this resolves issues observed in previous models that were prone to noisy feature integration.

Experimental Results and Implications

The authors validate their approach on two standard datasets, MSR-VTT and MSVD, demonstrating that their model consistently outperforms or competes closely with state-of-the-art methods on standard metrics including BLEU, METEOR, ROUGE-L, and CIDEr. Particularly on the MSVD dataset, the method sets new performance benchmarks on several metrics. This reinforces the argument that modeling explicit spatio-temporal relationships and applying knowledge distillation advances the interpretability and quality of generated video captions.

The success of this model in improving caption generation interprets to greater applicability in domains requiring high levels of understanding in video data, such as autonomous monitoring systems, video content indexing, and human-computer interaction interfaces. The distinct ability to incorporate object interactions into the captioning pipeline allows potentially more nuanced interpretations of complex scenes, paving the way for advancements in intelligent multimedia systems.

Future Prospects

The paper suggests that while the proposed framework is tailored for video captioning, it is agnostic to downstream tasks, thereby providing potential utility across various applications requiring structured spatio-temporal data processing. Future research directions could explore extending this conceptual framework to other forms of video understanding tasks like activity recognition or video summarization, potentially integrating more complex object interactions or employing multi-modality inputs. Additionally, ongoing improvements in object detection technologies can further bolster this approach by enhancing the quality and granularity of the derived object features.

In conclusion, the blend of spatio-temporal graph modeling with knowledge distillation presents a promising direction for enriching video captioning solutions and offering fertile ground for further explorations in the field of video understanding.