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This work examines how a forced-attention technique can be applied to the task of Video Activity Recognition. The Look & Learn system performs early fusion of critical detected areas of attention with the original raw image data for training a system for video activity recognition, specifically the task of Squat “Quality” Detection. Look & Learn is compared to previous work, USquat, and achieved a 98.96% accuracy on average compared to the USquat system which achieved 93.75% accuracy demonstrating the improvement that can be gained by Look & Learn’s forced-attention technique. Look & Learn is deployed in an Android Application for proof of concept and results presented.
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Lynne Grewe, Ankush Mahajan, Dikshant Pravin Jain, Jake Shahshahani, "Look and learn: forcing attention through input-fusing of important features and raw image data," Proc. SPIE 12547, Signal Processing, Sensor/Information Fusion, and Target Recognition XXXII, 125470R (14 June 2023); https://doi.org/10.1117/12.2663206