待翻譯：The 10th AI City Challenge
AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：arXiv:2608.17044v1 Announce Type: new Abstract: The 10th AI City Challenge, held with ECCV 2026, marks a decade of community benchmarking for intelligent transportation, smart cities, and physical AI. Since its 2017 start with vehicle detection, classification, and tracking, the challenge has grown into a broad benchmark suite for multi-camera perception, multimodal reasoning, synthetic-to-real learning, generative forecasting, and privacy-preserving evaluation. The 2026 edition continued this growth with 325 registered teams, up from 245 in 2025, and participation from 26 countries and regions, up from 15. Its six primary tracks cover multi-camera 3D perception, transportation safety captioning and VQA, traffic anomaly reasoning, text-based person anomaly search, generative traffic video forecasting, and cross-city object detection. Track 3 further includes two out-of-domain leaderboards, submitted as Tracks 7 and 8, for fisheye traffic-violation understanding and pedestrian situated-intent VQA. This paper summarizes the challenge setup, datasets, evaluation protocols, leaderboard results, and workshop papers. Across tracks, successful systems combine foundation models with geometric grounding, retrieval or reranking, synthetic-data design, domain adaptation, and controlled inference.
AI 服務暫時不可用，以下為來源正文，待恢復後補全翻譯。
--> [Submitted on 17 Aug 2026] Title:The 10th AI City Challenge View a PDF of the paper titled The 10th AI City Challenge, by Zheng Tang and Shuo Wang and David C. Anastasiu and Ming-Ching Chang and Anuj Sharma and Quan Kong and Munkhjargal Gochoo and Jun-Wei Hsieh and Tomasz Kornuta and Zhedong Zheng and Renran Tian and Judah Goldfeder and Fulgencio Navarro and Yuxing Wang and Yizhou Wang and Sameer Satish Pusegaonkar and Anqi Li and Nalin Dadhich and Ridham Kachhadiya and Dhanishtha Patil and Haoquan Liang and Jiajun Li and Han Zhang and Yilin Zhao and Zaid Pervaiz Bhat and Shuyu Yang and Ashutosh Kumar and Rong Wang and Rafael Martin Nieto and Peter Christiansen and Ahmed Abduljawad and Mohanrasu Shanmugam and Nadeem Shaik and Sujit Biswas and Xunlei Wu and Vidya Murali and Rama Chellappa View PDF HTML (experimental) Abstract:The 10th AI City Challenge, held with ECCV 2026, marks a decade of community benchmarking for intelligent transportation, smart cities, and physical AI. Since its 2017 start with vehicle detection, classification, and tracking, the challenge has grown into a broad benchmark suite for multi-camera perception, multimodal reasoning, synthetic-to-real learning, generative forecasting, and privacy-preserving evaluation. The 2026 edition continued this growth with 325 registered teams, up from 245 in 2025, and participation from 26 countries and regions, up from 15. Its six primary tracks cover multi-camera 3D perception, transportation safety captioning and VQA, traffic anomaly reasoning, text-based person anomaly search, generative traffic video forecasting, and cross-city object detection. Track 3 further includes two out-of-domain leaderboards, submitted as Tracks 7 and 8, for fisheye traffic-violation understanding and pedestrian situated-intent VQA. This paper summarizes the challenge setup, datasets, evaluation protocols, leaderboard results, and workshop papers. Across tracks, successful systems combine foundation models with geometric grounding, retrieval or reranking, synthetic-data design, domain adaptation, and controlled inference. Comments: Summary of the 10th AI City Challenge Workshop in conjunction with ECCV 2026 Subjects: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) Cite as: arXiv:2608.17044 [cs.CV] (or arXiv:2608.17044v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2608.17044 arXiv-issued DOI via DataCite (pending registration) Submission history From: Zheng Tang [view email] [v1] Mon, 17 Aug 2026 18:42:49 UTC (13,140 KB) Full-text links: Access Paper: View a PDF of the paper titled The 10th AI City Challenge, by Zheng Tang and Shuo Wang and David C. Anastasiu and Ming-Ching Chang and Anuj Sharma and Quan Kong and Munkhjargal Gochoo and Jun-Wei Hsieh and Tomasz Kornuta and Zhedong Zheng and Renran Tian and Judah Goldfeder and Fulgencio Navarro and Yuxing Wang and Yizhou Wang and Sameer Satish Pusegaonkar and Anqi Li and Nalin Dadhich and Ridham Kachhadiya and Dhanishtha Patil and Haoquan Liang and Jiajun Li and Han Zhang and Yilin Zhao and Zaid Pervaiz Bhat and Shuyu Yang and Ashutosh Kumar and Rong Wang and Rafael Martin Nieto and Peter Christiansen and Ahmed Abduljawad and Mohanrasu Shanmugam and Nadeem Shaik and Sujit Biswas and Xunlei Wu and Vidya Murali and Rama Chellappa View PDF HTML (experimental) TeX Source view license Current browse context: cs.CV new | recent | 2026-08 Change to browse by: cs cs.AI References & Citations NASA ADS Google Scholar Semantic Scholar Loading... Data provided by: Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) 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