arXiv:2510.25175v1 Announce Type: new 
Abstract: In recent years, test-time adaptive object detection has attracted increasing attention due to its unique advantages in online domain adaptation, which aligns more closely with real-world application scenarios. However, existing approaches heavily rely on source-derived statistical characteristics while making the strong assumption that the source and target domains share an identical category space. In this paper, we propose the first foundation model-powered test-time adaptive object detection method that eliminates the need for source data entirely and overcomes traditional closed-set limitations. Specifically, we design a Multi-modal Prompt-based Mean-Teacher framework for vision-language detector-driven test-time adaptation, which incorporates text and visual prompt tuning to adapt both language and vision representation spaces on the test data in a parameter-efficient manner. Correspondingly, we propose a Test-time Warm-start strategy tailored for the visual prompts to effectively preserve the representation capability of the vision branch. Furthermore, to guarantee high-quality pseudo-labels in every test batch, we maintain an Instance Dynamic Memory (IDM) module that stores high-quality pseudo-labels from previous test samples, and propose two novel strategies-Memory Enhancement and Memory Hallucination-to leverage IDM's high-quality instances for enhancing original predictions and hallucinating images without available pseudo-labels, respectively. Extensive experiments on cross-corruption and cross-dataset benchmarks demonstrate that our method consistently outperforms previous state-of-the-art methods, and can adapt to arbitrary cross-domain and cross-category target data. Code is available at https://github.com/gaoyingjay/ttaod_foundation.

تقدم ورقة جديدة على arXiv نهجًا مبتكرًا للكشف عن الكائنات القابلة للتكيف في وقت الاختبار، والذي يكتسب اهتمامًا متزايدًا بسبب قدرته على التكيف مع سيناريوهات العالم الحقيقي. تتناول هذه الطريقة القيود المفروضة على التقنيات الحالية التي تفترض أن مجالات المصدر والهدف متطابقة. من خلال اقتراح نموذج أساسي، يهدف المؤلفون إلى تحسين قابلية تكيف أنظمة الكشف عن الكائنات، مما يجعلها أكثر فعالية في بيئات متنوعة. هذه الخطوة مهمة لأنها قد تؤدي إلى تحسين الأداء في تطبيقات مختلفة، من المركبات المستقلة إلى أنظمة المراقبة.

Un nuevo artículo en arXiv presenta un enfoque innovador para la detección de objetos adaptativa en tiempo de prueba, que está ganando atención por su capacidad para adaptarse a escenarios del mundo real. Este método aborda las limitaciones de las técnicas existentes que suponen que los dominios de origen y destino son idénticos. Al proponer un modelo fundamental, los autores buscan mejorar la adaptabilidad de los sistemas de detección de objetos, haciéndolos más efectivos en diversos entornos. Este avance es significativo, ya que podría llevar a un mejor rendimiento en diversas aplicaciones, desde vehículos autónomos hasta sistemas de vigilancia.

Un nouvel article sur arXiv présente une approche révolutionnaire de la détection d'objets adaptative en temps de test, qui suscite un intérêt croissant pour sa capacité à s'adapter à des scénarios réels. Cette méthode répond aux limites des techniques existantes qui supposent que les domaines source et cible sont identiques. En proposant un modèle fondamental, les auteurs visent à améliorer l'adaptabilité des systèmes de détection d'objets, les rendant plus efficaces dans des environnements divers. Cette avancée est significative car elle pourrait conduire à de meilleures performances dans diverses applications, des véhicules autonomes aux systèmes de surveillance.

A new paper on arXiv introduces a groundbreaking approach to test-time adaptive object detection, which is gaining traction for its ability to adapt to real-world scenarios. This method addresses the limitations of existing techniques that assume source and target domains are identical. By proposing a foundation model, the authors aim to enhance the adaptability of object detection systems, making them more effective in diverse environments. This advancement is significant as it could lead to improved performance in various applications, from autonomous vehicles to surveillance systems.

Test-Time Adaptive Object Detection with Foundation Model

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Test-Time Adaptive Object Detection with Foundation Model

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