{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,21]],"date-time":"2025-11-21T06:30:00Z","timestamp":1763706600319},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,8]]},"abstract":"<jats:p>In the realm of autonomous driving, real-time perception or streaming perception remains under-explored. This research introduces DAMO-StreamNet, a novel framework that merges the cutting-edge elements of the YOLO series with a detailed examination of spatial and temporal perception techniques. DAMO-StreamNet's main inventions include: (1) a robust neck structure employing deformable convolution, bolstering receptive field and feature alignment capabilities; (2) a dual-branch structure synthesizing short-path semantic features and long-path temporal features, enhancing the accuracy of motion state prediction; (3) logits-level distillation facilitating efficient optimization, which aligns the logits of teacher and student networks in semantic space; and (4) a real-time prediction mechanism that updates the features of support frames with the current frame, providing smooth streaming perception during inference. Our testing shows that DAMO-StreamNet surpasses current state-of-the-art methodologies, achieving 37.8% (normal size (600, 960)) and 43.3% (large size (1200, 1920)) sAP without requiring additional data. This study not only establishes a new standard for real-time perception but also offers valuable insights for future research. The source code is at https:\/\/github.com\/zhiqic\/DAMO-StreamNet.<\/jats:p>","DOI":"10.24963\/ijcai.2023\/90","type":"proceedings-article","created":{"date-parts":[[2023,8,11]],"date-time":"2023-08-11T08:31:30Z","timestamp":1691742690000},"page":"810-818","source":"Crossref","is-referenced-by-count":15,"title":["DAMO-StreamNet: Optimizing Streaming Perception in Autonomous Driving"],"prefix":"10.24963","author":[{"given":"Jun-Yan","family":"He","sequence":"first","affiliation":[{"name":"DAMO Academy, Alibaba Group"}]},{"given":"Zhi-Qi","family":"Cheng","sequence":"additional","affiliation":[{"name":"Carnegie Mellon University"}]},{"given":"Chenyang","family":"Li","sequence":"additional","affiliation":[{"name":"DAMO Academy, Alibaba Group"}]},{"given":"Wangmeng","family":"Xiang","sequence":"additional","affiliation":[{"name":"DAMO Academy, Alibaba Group"}]},{"given":"Binghui","family":"Chen","sequence":"additional","affiliation":[{"name":"DAMO Academy, Alibaba Group"}]},{"given":"Bin","family":"Luo","sequence":"additional","affiliation":[{"name":"DAMO Academy, Alibaba Group"}]},{"given":"Yifeng","family":"Geng","sequence":"additional","affiliation":[{"name":"DAMO Academy, Alibaba Group"}]},{"given":"Xuansong","family":"Xie","sequence":"additional","affiliation":[{"name":"DAMO Academy, Alibaba Group"}]}],"member":"10584","event":{"number":"32","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"acronym":"IJCAI-2023","name":"Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}","start":{"date-parts":[[2023,8,19]]},"theme":"Artificial Intelligence","location":"Macau, SAR China","end":{"date-parts":[[2023,8,25]]}},"container-title":["Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2023,8,11]],"date-time":"2023-08-11T08:34:40Z","timestamp":1691742880000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2023\/90"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2023,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2023\/90","relation":{},"subject":[],"published":{"date-parts":[[2023,8]]}}}