Language-Guided Temporal Grounding in Long Untrimmed Videos with Debiased Cross-Modal Alignment
Keywords:
Temporal Video Grounding, Cross-Modal Alignment, Debiasing, Causal Inference, Long Untrimmed VideosAbstract
The rapid proliferation of multimedia content has necessitated advanced methodologies for parsing and comprehending long untrimmed videos. Language-guided temporal grounding is a pivotal task in this domain, aiming to localize specific video segments that correspond to a given natural language query. Despite significant progress, current models struggle with long untrimmed videos due to confounding biases, such as temporal location bias, semantic spurious correlations, and skewed length distributions within training datasets. These biases often lead to superficial cross-modal alignment, wherein models rely on dataset-specific shortcuts rather than genuinely understanding the multimodal semantics. To address these limitations, this paper proposes a novel framework focused on debiased cross-modal alignment. The proposed architecture integrates a causal intervention module designed to sever the links between confounding background contexts and specific temporal locations. By employing counterfactual reasoning and a debiased contrastive learning objective, the framework enforces the learning of invariant cross-modal representations that generalize robustly across diverse video lengths and complex narrative structures. Extensive theoretical analyses and empirical evaluations demonstrate the efficacy of the proposed approach. Through rigorous ablation studies and comparisons on standard benchmark datasets, the methodology exhibits superior performance, significantly advancing the state of the art in language-guided temporal grounding
References
1. Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016). Rethinking the Inception architecture for computer vision. In Proceedings of CVPR.
2. Chen, L., Yang, Y., & Zhang, S. (2025). Auto-focusing and auto-exposure method for real-time structured-light 3D imaging.Optical Engineering,64(4), 044105-044105.
3. Zhao, Haoyu, et al. "LSTD: Long Short-Term Temporal Diffusion for Video Generation." IEEE Transactions on Multimedia (2026).
4. Xia, Y., & Ma, J. (2025). Grid-guided sparse Laplacian consensus for robust feature matching.IEEE Transactions on Image Processing,34, 1367-1381.
5. Wang, H., Wei, C., Ren, W., Liu, J., Lin, F., & Chen, W. (2026). Rationalrewards: Reasoning rewards scale visual generation both training and test time.arXiv preprint arXiv:2604.11626.
6. Yuan, M., Long, Q., Wu, Q., Li, Z., Zhao, Y., Wang, L., ... & Xu, M. (2026). O-VAD: Industrial Video Anomaly Detection through Object-Centric Tracking and Reasoning. arXiv preprint arXiv:2607.18142.
7. Zhang B, Wang X, Kao H, et al. Consensus Matrix: A Role Specialized Multi Agent Framework for Structured Collaborative Decision Making in Agentic Visual Media Workflows[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2026: 4613 4622.
8. Zhang, J., Zhang, J., Song, Z., Shi, Z., Zhao, C., Shi, Y., ... & Wang, J. (2024). Hoi-m^ 3: Capture multiple humans and objects interaction within contextual environment. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 516-526).
9. Xia, Y., & Ma, J. (2026). Locality Optimization Refinement with Deformation for Shape Matching via Functional Maps.International Journal of Computer Vision,134(2), 76.
10. Li, Q., Luo, T., & Liao, J. (2025). Theory-inspired deep multi-view multi-label learning with incomplete views and noisy labels. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 20706-20715).
11. Huang, H., Zhang, J., Zhang, J., Xu, J., & Wu, Q. (2020). Low-rank pairwise alignment bilinear network for few-shot fine-grained image classification.IEEE Transactions on Multimedia,23, 1666-1680.
12. Qi, Z., Yuan, Y., Ruan, X., Wang, S., Zhang, W., & Huang, Q. (2024). Collaborative debias strategy for temporal sentence grounding in video. IEEE Transactions on Circuits and Systems for Video Technology, 34(11), 10972-10986.
13. Lv, Q., Kong, W., Li, H., Zeng, J., Qiu, Z., Qu, D., ... & Pang, J. (2025). F1: A vision-language-action model bridging understanding and generation to actions. arXiv preprint arXiv:2509.06951.
14. Yang, Y., Bortins, I., Baldwin, D. P., & Zhang, S. (2024). Calibration of dual resolution dual camera structured light systems.Optics and Lasers in Engineering,182, 108472.
15. Qi, Z., Wang, S., Zhang, W., & Huang, Q. (2024). Uncertainty-boosted robust video activity anticipation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 46(12), 7775-7792.
16. Peng, Qucheng, et al. "3d vision-language gaussian splatting." International Conference on Learning Representations. Vol. 2025. 2025.
17. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., et al. (2015). Going deeper with convolutions. In Proceedings of CVPR.
18. Xia, Y., & Ma, J. (2022). Locality-guided global-preserving optimization for robust feature matching.IEEE Transactions on Image Processing,31, 5093-5108.
19. Li, X., Yang, F., Chen, L., & Cai, H. (2016, July). Saliency transfer: An example-based method for salient object detection. In IJCAI (pp. 3411-3417).
20. Yang, Y., Ma, X., Li, C., Zheng, Z., Zhang, Q., Huang, G., ... & Zhao, Q. (2021). Believe what you see: Implicit constraint approach for offline multi-agent reinforcement learning. Advances in Neural Information Processing Systems, 34, 10299-10312.
21. Jing, G., Gao, P., Hu, Y., Lee, Y., & Zhang, H. (2025, June). ESTI: An Efficient Spatial-Temporal Interaction Network For Video-Based Person Re-Identification. In2025 IEEE International Conference on Multimedia and Expo (ICME)(pp. 1-6). IEEE.
22. Qu, W., Wang, J., Gong, Y., Huang, X., & Xiao, L. (2025, June). An end-to-end robust point cloud semantic segmentation network with single-step conditional diffusion models. In 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 27325-27335). IEEE.
23. Zhang, W., Huang, M., Zhou, Y., Zhang, J., Yu, J., Wang, J., & Xu, L. (2024). Both2hands: Inferring 3d hands from both text prompts and body dynamics. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 2393-2404).
24. Kamal, T., Wenhong, T., Guo, J., Shafiq, M., Shuaihong, J., & Xue, R. (2026). Adaptive Probabilistic Transformer for Medical Image Segmentation. Expert Systems with Applications, 131948.
25. Jing, G., Gao, P., Lee, Y., Hu, Y., & Zhang, H. (2025). 3D-aided pedestrian representation learning for video-based person re-identification. IEEE Transactions on Circuits and Systems for Video Technology, 35 (12), 12830-12845.
26. Mildenhall, B., Srinivasan, P. P., Tancik, M., Barron, J. T., Ramamoorthi, R., & Ng, R. (2020). NeRF: Representing scenes as neural radiance fields for view synthesis. In Proceedings of ECCV.
27. Xia, Y., Lu, Y., Gao, Y., & Ma, J. (2024, March). Locality preserving refinement for shape matching with functional maps. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 38, No. 6, pp. 6207-6215).
28. Liu, C., Ma, L., Zhang, X. T., Zhang, Y., Zhang, H., Yang, X., & Tian, F. (2026). Boosting omni-modal language models: Staged post-training with visually debiased evaluation. arXiv preprint arXiv:2605.12034.
29. Yang, Y., & Zhang, S. (2024). Pixelwise calibration method for a telecentric structured light system.Applied Optics,63(10), 2562-2569.
30. Qu, D., & Ma, Y. (2026). Fourier–Transformer Mixer Network for Efficient Video Scene Graph Prediction.Engineering Proceedings,120(1), 16.
31. Wang, M., Li, J., Li, Z., Luo, C., Chen, B., Xia, S. T., & Wang, Z. (2023, October). Unsupervised anomaly detection with local-sensitive VQVAE and global-sensitive transformers. In 2023 IEEE International Conference on Image Processing (ICIP) (pp. 1080-1084). IEEE.
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