Source-linked AI summary
Spatio-Temporal Graph for Video Captioning with Knowledge Distillation
Boxiao Pan, Haoye Cai, De-An Huang, Kuan-Hui Lee, Adrien Gaidon, Ehsan Adeli, Juan Carlos Niebles
TL;DR
Video captioning needs to represent object interactions more explicitly because existing approaches may miss spatial and temporal relationships and remain sensitive to spurious correlations. The paper introduces a spatio-temporal object graph with interpretable links and distills its information into global scene features. Experiments on MSVD and MSR-VTT report a significant boost on MSVD and competitive results on MSR-VTT, while testing uses only the scene branch.
Problem
Existing video-captioning methods do not explicitly model spatial and temporal object interactions, limiting interpretable and visually grounded scene understanding.
Method
The model constructs an interpretable spatio-temporal object graph and uses object-aware knowledge distillation to inject local interaction information into a global scene branch.
Results
The approach achieves a significant boost over state-of-the-art methods on MSVD and competitive results on MSR-VTT.
Takeaways & Limitations
Object interactions can be used as privileged training information, with distilled features enabling sentence generation from only the scene branch at test time.
Takeaways & Limitations
The method is most comparable to MARN because it uses the same ResNet-101 and I3D scene features and excludes reinforcement-learning methods from comparison.
Abstract
from arXiv · showhide
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.
1. Introduction
Video captioning methods often miss interpretable spatial and temporal object interactions, limiting visual grounding. The paper introduces a spatio-temporal graph and object-aware knowledge distillation to model these interactions while providing global scene context.
- Research gap: Understanding captions such as “A cat jumps into a box” requires identifying relevant entities, their transformation, and distractors.The example emphasizes recognizing the cat and box while ignoring the television and bed.
- Research gap: Existing video-captioning methods fail to explicitly capture interactions among objects in space and time.Pixel-level methods rely on hierarchical networks, while object-based methods may ignore cross-object interaction or temporal transformation.
- Approach: Object-aware knowledge distillation injects interaction information into a global scene branch through language-logit alignment.The object branch supplies privileged information, while soft logit regularization addresses noisy object representations caused by variable object counts.
- Approach: The proposed model represents videos as spatio-temporal graphs whose nodes are objects and whose edges encode spatial and temporal relationships.Adjacency matrices incorporate spatial layout and temporal transformation to build interpretable connections, followed by graph convolution.
- Results: The model reports significant improvement over state-of-the-art methods on MSVD and competitive results on MSR-VTT.The contribution summary specifically characterizes the gains as a significant boost on MSVD and competitive performance on MSR-VTT.
2. Related Work
Video-understanding research has progressed from global and temporal feature modeling toward local object features, but prior approaches incompletely model spatial and temporal interactions. This paper uses graph-based object relations and knowledge distillation within a two-branch captioning framework.
- General Video Classification: General video-classification methods model space-time patterns using 3D convolutions, optical flow, or temporal segment sampling.These approaches establish common strategies for spatio-temporal video representation.
- Proposed Framework: The proposed two-branch architecture trains an object branch for interaction information and a scene branch for global context, retaining only the scene branch at test time.This design uses object information during training while avoiding object detection during sentence generation.
- Knowledge Distillation: Knowledge distillation transfers information through logit distributions and can use training-only privileged information unavailable at test time.The paper builds on this framework to transfer object information into another representation.
- Video Captioning: Video-captioning systems evolved from template-based models and global frame pooling to attention mechanisms and Transformer decoders.Transformer decoding is adopted because it is described as faster and easier to train.
- Video Captioning: Prior local-object methods use spatial pooling or trajectory modeling but omit either temporal or spatial object interactions.The proposed approach instead models both interaction types explicitly.
3. Method
The method combines a spatio-temporal object graph with a separate scene branch, using object-aware knowledge distillation to regularize scene representations for video captioning.
- Spatio-Temporal Graph: The object branch represents video objects as nodes and models their spatial interactions and temporal transformations with separate graph components.Spatial edges capture interactions within frames, while directed temporal edges connect semantically similar objects across adjacent frames.
- Spatio-Temporal Graph: Spatial adjacency connects objects using normalized intersection-over-union values, while temporal adjacency connects objects across adjacent frames using cosine feature similarity.The spatial graph is frame-specific; the temporal graph remains connected across the video through adjacent-frame links.
- Graph Convolution: Merged spatial and temporal graphs are updated with graph convolutions, then spatially average-pooled to produce the final object features.The initial graph activations are stacked object features transformed into the model dimension, and graph convolution uses degree normalization, residual connections, and ReLU.
- Scene Branch: A separate scene branch supplies global context by combining 2D frame features and 3D clip features, especially when few objects are detected.The scene branch is kept simple to isolate the contribution of the spatio-temporal graph.
- Language Decoder: During training, object and scene features are decoded by separate Transformers, while testing uses only scene features to generate captions.The two branches are trained simultaneously with language cross-entropy losses before distillation regularizes their outputs.
- Object-Aware Knowledge Distillation: Object-aware distillation aligns the two branches through KL divergence between language logits, avoiding hard feature constraints caused by variable object counts.The authors contrast this late fusion of probability distributions with feature concatenation, pooling, graph-node fusion, and L2 feature matching.
4. Experiments and Results
Experiments evaluate the model on MSR-VTT and MSVD using standard captioning metrics, showing strongest gains on MSVD and competitive performance on MSR-VTT. Ablations and qualitative analyses support the value of object-aware distillation, spatial-temporal graph components, prior relational knowledge, and improved visual grounding.
- Experimental Setup: The model is evaluated on MSR-VTT and MSVD using BLEU@4, METEOR, ROUGE-L, and CIDEr.MSR-VTT contains 10,000 clips, while MSVD contains 1,970 clips and English captions from multiple languages.
- Comparison with Existing Methods: On MSVD, the proposed method outperforms all compared methods on 3 out of 4 metrics by a large margin.On MSR-VTT, performance is less outstanding, partly because object detectors generally fail on many animations and competing methods use stronger or differently optimized components.
- Comparison with Existing Methods: On MSR-VTT, the method still provides a clear boost over baselines including MARN and the scene-only model.Direct comparison with some methods is described as unfair because they use more advanced features or directly optimize decoding.
- Ablation Study: Hard feature concatenation and L2 distillation underperform the scene-only baseline, whereas logit alignment makes object information useful and significantly improves performance.The ablation compares Scene Branch Only, Two Branch + Concat, Two Branch + L2, and Our Full Model.
- Ablation Study: Spatial-only and temporal-only graphs are both inferior to the full model, while the full model also outperforms a dense graph using prior spatial and temporal knowledge.These results indicate that the spatial and temporal sub-graphs provide distinct information and that prior relational structure is helpful.
- Qualitative Analysis: Qualitative results show that the full model attends more strongly to key regions and generates captions with finer-grained and larger-scale visual details than Wang et al.Examples include focusing on faces, paddles, running people, black shirts, potatoes, races, and groups of people.
5. Conclusion
The paper concludes that explicit spatio-temporal object interactions improve video captioning and scene understanding. It combines this graph representation with object-aware knowledge distillation to address noisy feature learning and demonstrates effectiveness on two benchmark datasets.
- Conclusion: The proposed graph network explicitly exploits spatiotemporal object interactions for video captioning, which the paper identifies as crucial for scene understanding and description.Objects form graph nodes and their relations are modeled across space and time.
- Conclusion: The two-branch framework uses object-aware knowledge distillation to address noisy feature learning in previous spatio-temporal graph models.The mechanism transfers object-level information to improve scene-feature learning.
- Conclusion: Experiments on two benchmark video captioning datasets demonstrate the effectiveness of the approach.The conclusion summarizes the empirical support without reporting individual metric values.