{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T06:55:30Z","timestamp":1762325730801,"version":"3.41.2"},"reference-count":26,"publisher":"World Scientific Pub Co Pte Ltd","issue":"13","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Patt. Recogn. Artif. Intell."],"published-print":{"date-parts":[[2023,10]]},"abstract":"<jats:p> Detection transformers (DETR) have provided a novel solution to human\u2013object interaction detection in a set-prediction manner, thanks to expressive learnable queries. However, few studies covered how queries implicitly affect model behaviors, which might contain clues for model improvement. Therefore, we propose two dataset-based analysis tools: score state space and query preference map. They provide data on the distribution of model predictions, which can reveal overall-level and query-level model properties. Starting with our baseline model, we find the model naturally regresses object boxes to overlap human boxes in the edge case of no-object verbs (stand, etc.), even without a related loss. We encourage this by patching the supervision with virtual objects, resulting in more stable query preference. Moreover, we show that two-stage decoders designed for cascade inference do not decouple tasks as intended. We infer this is caused by the empty instances used as negative samples, which suggests a redesign in the matching scheme. Further, we reveal how adding an oracle-query-based teacher model affects query roles with a tiny gain, indicating room for refinement. Our findings demonstrate how a simple focus on query behaviors can provide insights for improving models. <\/jats:p>","DOI":"10.1142\/s0218001423560219","type":"journal-article","created":{"date-parts":[[2023,10,9]],"date-time":"2023-10-09T09:51:52Z","timestamp":1696845112000},"source":"Crossref","is-referenced-by-count":2,"title":["Query Preference Analysis on Cascade Inference Human\u2013Object Interaction Detection Transformer"],"prefix":"10.1142","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3532-8658","authenticated-orcid":false,"given":"Weizhe","family":"Jia","sequence":"first","affiliation":[{"name":"School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, 200444, P. R. China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6039-5030","authenticated-orcid":false,"given":"Shiwei","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, 200444, P. R. 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