Adaptive Temporal Segment Selection for Long-Form Video Question Answering
Keywords:
video question answering, long-form video understanding, temporal segment selection, adaptive sampling, video-language models, computational efficiency, system architectureAbstract
The rapid proliferation of long-form video content across domains such as surveillance, education, entertainment, and telemedicine has created an urgent demand for robust video question answering systems capable of processing extended temporal sequences. Traditional video question answering architectures, predominantly designed for short clips of a few seconds, suffer from fundamental scalability limitations when confronted with videos lasting minutes or hours. This paper introduces and systematically evaluates the paradigm of adaptive temporal segment selection as a structural solution to the computational and informational bottlenecks inherent in long-form video question answering. Rather than processing entire video streams uniformly, adaptive segment selection dynamically identifies and prioritizes temporally localized regions of relevance conditioned on the semantic content of a natural language query. This paper presents a comprehensive architectural framework that integrates lightweight temporal saliency estimation, hierarchical memory compression, and query-conditioned attention mechanisms to enable efficient reasoning over extended video durations. We discuss the trade-offs between segmentation granularity, computational budget, and answer accuracy, drawing comparisons with alternative approaches including uniform sampling, dense frame processing, and memory-augmented networks. Deployment considerations are analyzed with respect to infrastructure requirements, energy efficiency, and latency constraints in real-time and edge computing environments. Furthermore, we examine the robustness of adaptive selection strategies under distributional shifts, noisy annotations, and adversarial perturbations. Fairness implications are considered, particularly regarding biased temporal attention across demographic groups or activity types. Policy recommendations are offered for the governance of automated video analysis systems in high-stakes applications such as public safety and clinical decision support. Through cross-domain case illustrations spanning autonomous driving, educational lecture analysis, and sports broadcast understanding, we demonstrate that adaptive temporal segment selection offers a principled pathway toward scalable, interpretable, and resource-conscious long-form video question answering. The paper concludes with forward-looking perspectives on self-supervised temporal grounding, multimodal fusion, and the integration of causal reasoning into temporal selection mechanisms.
References
1. Xu, J., Mei, T., Yao, T., & Rui, Y. (2017). MSR-VTT: A large video description dataset for bridging video and language. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 5288-5296.
2. Li, L., Chen, Y. C., Cheng, Y., Gan, Z., Yu, L., & Liu, J. (2020). HERA: A hierarchical framework for video-language understanding. Advances in Neural Information Processing Systems, 33, 15073-15084.
3. Yu, Z., Xu, D., Yu, J., Yu, T., Zhao, Z., Zhuang, Y., & Tao, D. (2019). ActivityNet-QA: A dataset for understanding complex web videos via question answering. Proceedings of the AAAI Conference on Artificial Intelligence, 33, 9127-9134.
4. Korbar, B., Tran, D., & Torresani, L. (2019). SCSampler: Sampling salient clips from video for efficient action recognition. Proceedings of the IEEE International Conference on Computer Vision, 6232-6242.
5. Geiger, A., Lenz, P., & Urtasun, R. (2012). Are we ready for autonomous driving? The KITTI vision benchmark suite. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 3354-3361.
6. Gao, J., Sun, C., Yang, Z., & Nevatia, R. (2017). TALL: Temporal activity localization via language query. Proceedings of the IEEE International Conference on Computer Vision, 5267-5275.
7. Zhang, H., Sun, Y., Jiang, Y. G., & Ngo, C. W. (2021). Event-guided video question answering with hierarchical temporal reasoning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(10), 6738-6753.
8. Wu, C. Y., Feichtenhofer, C., Fan, H., He, K., Krahenbuhl, P., & Girshick, R. (2019). Long-term feature banks for detailed video understanding. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 284-293.
9. Kumar, A., Irsoy, O., Ondruska, P., Iyyer, M., Bradbury, J., Gulrajani, I., ... & Socher, R. (2016). Ask me anything: Dynamic memory networks for natural language processing. Proceedings of the International Conference on Machine Learning, 1378-1387.
10. Piergiovanni, A. J., & Ryoo, M. S. (2019). Temporal segment networks for action recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 869-878.
11. Fan, L., Huang, W., Gan, C., Ermon, S., Gong, B., & Huang, J. (2018). End-to-end learning of decision trees for action recognition. Advances in Neural Information Processing Systems, 31.
12. Jin, H., Yi, H., Zhao, W., Luo, J., Ye, S., Guan, Z., ... & Yu, T. (2026). HY-Himmel Technical Report: Hierarchical Interleaved Multi-stream Motion Encoding for Long Video Understanding. arXiv preprint arXiv:2605.08158.
13. Sharir, G., & Shashua, A. (2018). On the expressive power of overlapping architectures of deep learning. Proceedings of the International Conference on Learning Representations.
14. Hendrycks, D., & Gimpel, K. (2017). A baseline for detecting misclassified and out-of-distribution examples in neural networks. Proceedings of the International Conference on Learning Representations.
15. Singh, A., & Singh, P. (2022). Automated analysis of educational lecture videos: A survey. ACM Computing Surveys, 55(4), 1-38.
16. Han, S., Mao, H., & Dally, W. J. (2016). Deep compression: Compressing deep neural networks with pruning, trained quantization and Huffman coding. Proceedings of the International Conference on Learning Representations.
17. Lane, N. D., Bhattacharya, S., Georgiev, P., Forlivesi, C., & Kawsar, F. (2016). An early resource characterization of deep learning on wearables, smartphones and Internet-of-Things devices. Proceedings of the International Workshop on Mobile Computing Systems and Applications, 7-12.
18. Strubell, E., Ganesh, A., & McCallum, A. (2019). Energy and policy considerations for deep learning in NLP. Proceedings of the Annual Meeting of the Association for Computational Linguistics, 3645-3650.
19. Recht, B., Roelofs, R., Schmidt, L., & Shankar, V. (2019). Do ImageNet classifiers generalize to ImageNet? Proceedings of the International Conference on Machine Learning, 5389-5400.
20. Buolamwini, J., & Gebru, T. (2018). Gender shades: Intersectional accuracy disparities in commercial gender classification. Proceedings of the Conference on Fairness, Accountability, and Transparency, 77-91.
21. European Commission. (2021). Proposal for a regulation laying down harmonised rules on artificial intelligence (Artificial Intelligence Act). COM(2021) 206 final.
22. Ramanathan, V., Huang, J., Abu-El-Haija, S., Gorban, A., Murphy, K., & Fei-Fei, L. (2016). Detecting events and key actors in multi-person videos. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 3043-3053.
23. Pearl, J. (2019). The seven tools of causal inference, with reflections on machine learning. Communications of the ACM, 62(3), 54-60.
24. Alayrac, J. B., Donahue, J., Luc, P., Miech, A., Barr, I., Hasson, Y., ... & Zisserman, A. (2022). Flamingo: A visual language model for few-shot learning. Advances in Neural Information Processing Systems, 35, 23716-23736.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Computer Science and Engineering Transactions
This work is licensed under a Creative Commons Attribution 4.0 International License.
This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
