Published in The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2026
Accepted to CVPR Findings 2026, but resubmitted to another conferece with major revision.
Published in The Fourteenth International Conference on Learning Representations (ICLR), 2026
A lightweight, real-time accident predictor trained via novel temporally shifted distillation, combining efficient spatial encoding and recurrent temporal modeling, running on edge devices.
Recommended citation: P. Patera, Y.-T. Chen, W.-H. Fang, "Lightweight Spatio-Temporal Modeling via Temporally Shifted Distillation for Real-Time Accident Anticipation,"The Fourteenth International Conference on Learning Representations (ICLR), Rio de Janeiro, Brazil, 2026, pp. 1-20, url: https://openreview.net/forum?id=8zzfTSVds2
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Published in IEEE International Conference on Image Processing (ICIP), 2024
This study uses Parameter-Efficient Transfer Learning (PEFTL) and Dilated Neighborhood Attention (DNA) to adapt pretrained CLIP-ViT for traffic accident anticipation. By utilizing novel Spatial and Temporal Adapters with cross-attention, the model captures long-range dependencies more effectively, achieving state-of-the-art earliness and accuracy on the DAD and CCD datasets.
Recommended citation: P. Patera, Y. -T. Chen and W. -H. Fang, "Spatio-Temporal Adaptation With Dilated Neighbourhood Attention For Accident Anticipation," 2024 IEEE International Conference on Image Processing (ICIP), Abu Dhabi, United Arab Emirates, 2024, pp. 2452-2458, doi: 10.1109/ICIP51287.2024.10647316.
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Published in IEEE Transactions on Circuits and Systems for Video Technology, 2025
MASTTA is a parameter-efficient, multi-modal framework that improves traffic accident anticipation by fine-tuning CLIP-based adapters for visual and textual data. By utilizing novel Temporal and Spatial Adapters alongside a Text Adapter, the model captures complex spatio-temporal interactions and aligns them in a joint embedding space. This synergy allows for more accurate, long-range context modeling, outperforming state-of-the-art methods in both earliness and correctness on the DAD and CCD benchmarks.
Recommended citation: P. Patera, Y. -T. Chen and W. -H. Fang, "A Multi-Modal Architecture With Spatio-Temporal-Text Adaptation for Video-Based Traffic Accident Anticipation," in IEEE Transactions on Circuits and Systems for Video Technology, vol. 35, no. 9, pp. 8989-9002, Sept. 2025, doi: 10.1109/TCSVT.2025.3552895.
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Published in US patent 2025, 2025
This method computes a unique hash by combining facial and voice representations into distinct fingerprints. It extracts these features from multimedia recordings and uses a similarity measure to compare them for identification or verification.
Recommended citation: K. Ekštein, M. Konopík, F. Pártl, P. Patera, "System and method for identification, authentication, and verification of a person based upon a short audio-visual recording of the person", US patent 2025/0184148.
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