{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T02:39:01Z","timestamp":1773801541152,"version":"3.50.1"},"reference-count":0,"publisher":"Association for the Advancement of Artificial Intelligence (AAAI)","issue":"10","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["AAAI"],"abstract":"<jats:p>Video object detection is a fundamental yet challenging task in computer vision. Recently, DETR-based methods have gained prominence in this domain owing to their powerful global modeling capabilities. However, these methods are still confronted with two key limitations: frame-agnostic initialization of object queries and scale-agnostic attention mechanisms, which hinder their capability to capture the appearance variations of dynamic objects and model the temporal consistency across frames. To alleviate these limitations, we propose a multiscale-aware transformer diffusion network (MSTDiff), a novel framework designed for the video object detection task, including two technical improvements over existing methods. First, we design a diffusion-driven adaptive query module, which models the object query distribution through a diffusion process conditioned on input frames, enabling an adaptive and content-aware initialization of object queries. Second, we develop a multiscale-aware transformer encoder module, which combines multi-head convolutional units with attention mechanisms to enhance multi-scale feature representations while preserving global dependence modeling. We conduct extensive experiments on the public ImageNet VID dataset, and the results demonstrate that our MSTDiff achieves 87.7% mAP with ResNet-101, outperforming most previous state-of-the-art video object detection methods.<\/jats:p>","DOI":"10.1609\/aaai.v40i10.37798","type":"journal-article","created":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T23:40:34Z","timestamp":1773790834000},"page":"8475-8483","source":"Crossref","is-referenced-by-count":0,"title":["MSTDiff: Multiscale-Aware Transformer Diffusion Network for Video Object Detection"],"prefix":"10.1609","volume":"40","author":[{"given":"Qiang","family":"Qi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenqi","family":"Shang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiao","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanjie","family":"Liang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuyuan","family":"Lin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"9382","published-online":{"date-parts":[[2026,3,14]]},"container-title":["Proceedings of the AAAI Conference on Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/37798\/41760","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/download\/37798\/41760","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T23:40:34Z","timestamp":1773790834000},"score":1,"resource":{"primary":{"URL":"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/37798"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,14]]},"references-count":0,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2026,3,17]]}},"URL":"https:\/\/doi.org\/10.1609\/aaai.v40i10.37798","relation":{},"ISSN":["2374-3468","2159-5399"],"issn-type":[{"value":"2374-3468","type":"electronic"},{"value":"2159-5399","type":"print"}],"subject":[],"published":{"date-parts":[[2026,3,14]]}}}