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• The sub-description principal component contrastive fusion strategy removes interfering textual components via singular value decomposition (SVD).method 90%
• The SDDF-L model, based on YOLOv8-L, achieves an AP of 56.4, a recall of 76.4, and an AP50 of 60.7 on the OVCOD-D benchmark.result 95%
• The proposed SDDF method achieves an AP of 56.4 on the OVCOD-D benchmark under the open-set evaluation setting.result 95%
• A new benchmark named OVCOD-D is constructed by augmenting camouflaged object images with fine-grained textual descriptions.method 95%
• A sub-description principal component contrastive fusion strategy is designed to mitigate interference from confusing and overly decorative modifiers in textual descriptions.method 95%
• A specificity-guided regional weak alignment and dynamic focusing method is proposed to address the challenge of high visual similarity between camouflaged objects and their background.method 95%
• Open-vocabulary detectors face significant challenges when dealing with camouflaged objects, leading to a substantial performance decline compared to general object detection datasets.limitation 90%
• The sub-description principal component contrastive fusion strategy removes interfering textual components via singular value decomposition (SVD).method 90%
• The SDDF-L model, based on YOLOv8-L, achieves an AP of 56.4, a recall of 76.4, and an F1 score of 60.7 on the OVCOD-D benchmark.result 95%
• The proposed SDDF method achieves an AP of 56.4 on the OVCOD-D benchmark under the open-set evaluation setting.result 95%
• A new benchmark named OVCOD-D is constructed by augmenting camouflaged object images with fine-grained textual descriptions.method 95%
• A sub-description principal component contrastive fusion strategy is designed to mitigate interference from confusing and overly decorative modifiers in textual descriptions.method 95%

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