Situational verb recognition with semantic-aware distillation
With the rapid development in the field of computer vision,Situational Verb Recognition stands as a challenging task in image processing,aimed at identifying semantically complex situations within images.A novel method that integrates the CLIP model with knowledge distillation techniques is proposed,which leverages the powerful cross-modal capabilities of CLIP to capture the subtle associations between images and verb,and employs knowledge distillation to map these associations onto the SVR task,thereby enhancing network performance.Experimental results on standard SWiG datasets indicate that the method sur-passes current state-of-the-art techniques in performance while having the smallest parameter count.The primary contribution is the introduction of a SVR framework that combines CLIP with semantic-aware knowledge distillation,validated through extensive experiments on public datasets,confirming the effectiveness of our method.
万方数据
