Diversifying Spatial-Temporal Perception for Video Domain Generalization

Recent advances in deep learning, such as 3D CNNs (e.g., I3D) and Transformer-based models (e.g., Video Swin Transformer), have significantly improved action recognition. However, these models often assume training and testing data share the same distribution, limiting their robustness in real-world applications. Cross-domain challenges, like varying viewpoints, lighting, and backgrounds, cause performance drops. Existing solutions, including feature alignment, adversarial domain adaptation, and data augmentation, partially address the issue but often fail to capture content diversity comprehensively. Researchers are increasingly aware that modeling diverse, invariant content features is crucial for robust generalization, prompting exploration into content-aware architectures that explicitly encode multiple cues across space and time.

Despite progress, current models tend to overfit to source domain cues, such as static backgrounds or specific motion patterns, which do not generalize well. The core challenge is to enable models to perceive and leverage multiple, diverse content cues that are invariant across domains. Without such diversity, models remain vulnerable to domain shifts, leading to poor performance in unseen environments. The difficulty lies in designing mechanisms that can discover, model, and preserve content diversity during training, especially when target domain data is unavailable. This bottleneck hampers the deployment of reliable video understanding systems in real-world, dynamic scenarios.

This paper introduces two main innovations: first, the spatial grouping module employs learnable anchors to cluster spatial features within each frame, discovering multiple spatial cues that reflect different class-related content aspects. Second, the multi-scale relation module explicitly models dependencies across various spatial and temporal scales, capturing dynamic interactions and content variations. These modules work synergistically to promote content diversity, with entropy-based losses ensuring distinct and balanced cue representations. Additionally, the relation discrimination loss guarantees the learned relation features are discriminative across classes and scales. This comprehensive approach significantly enhances the model’s ability to generalize across unseen domains by leveraging diverse, invariant content cues.

• �� Input: Videos divided into N segments, sampling one frame per segment, extracting features via ResNet50.
• �� Spatial grouping: For each frame’s feature map, compute assignment probabilities to K learnable anchor features based on Euclidean distance, using a temperature τ.
• �� Feature aggregation: Aggregate features within each group, applying entropy minimization to enhance confidence and entropy maximization to ensure diversity.
• �� Spatial relation modeling: For each frame, model dependencies between spatial cues at multiple scales, using a linear projection on concatenated relation features.
• �� Temporal relation modeling: Across sampled frames, model dependencies at multiple time scales, capturing dynamic content variations.
• �� Feature fusion: Combine spatial and temporal relation features, modulated by small SE blocks, to form a comprehensive feature vector.
• �� Loss functions: Combine classification loss, entropy losses, and relation discrimination loss to optimize the entire network, promoting content diversity and discriminability.

Experiments utilize three benchmarks: UCF-HMDB, EPIC-Kitchens-DG, and Jester-DG, covering sports, egocentric, and hand gesture recognition. The backbone is ResNet50 pretrained on ImageNet, with N=5 frames sampled per video and K=4 spatial groups. Hyperparameters include learning rate 1e-3, batch size 32, and entropy loss weights. The models are trained with mini-batch SGD, and ablation studies analyze the impact of each module. Cross-validation ensures robustness, with multiple runs reporting mean and standard deviation. Comparisons with state-of-the-art methods demonstrate the superiority of the proposed approach in cross-domain accuracy, with detailed analysis of the contribution of each component.

STDN achieves 60.2% accuracy in UCF→HMDB and 77.1% in HMDB→UCF, outperforming previous methods by over 3%. On the new benchmarks, it reaches 36.8% and 47.3% average accuracy, respectively, significantly better than baselines. Ablation results show that removing spatial grouping or multi-scale relations reduces accuracy by at least 2-4%, confirming their importance. The model’s ability to capture diverse cues leads to improved robustness across different environments, validating the effectiveness of content diversification strategies.

This approach is suitable for surveillance, autonomous driving, and robotics, where models must operate reliably across varying conditions. Its content diversity mechanism reduces reliance on environment-specific cues, making it adaptable to real-world scenarios. Future integration with multi-modal data (audio, depth) could further enhance robustness, enabling deployment in complex, dynamic settings such as smart cities or industrial automation.

The increased computational complexity from multi-scale relation modeling may hinder real-time deployment. The approach’s robustness under extreme occlusion or rapid motion remains to be tested. Current experiments focus on visual data; incorporating additional modalities could improve performance but requires further research. Future work should optimize efficiency and explore unsupervised content discovery to broaden applicability.