This invention discloses a rotation-consistent target
perception high-frequency
wildlife re-identification method, relating to the field of board
processing technology. This invention performs rotational modeling only on the target high-frequency tokens or their corresponding two-dimensional feature maps obtained through target-frequency joint selection. This allows rotation robustness learning to focus on identity-related details such as texture, spots, stripes, and contours of the
animal subject. This mechanism reduces the risk of rotational enhancement of background high-
frequency noise. The target-frequency joint selection mechanism suppresses high-
frequency noise from natural backgrounds such as grass, leaves, water ripples, and rocks, improving the model's ability to focus on the
animal subject's discrimination region. The target-frequency joint selection mechanism identifies image patches that are truly relevant to the
animal subject and have significant high-frequency details, and establishes feature-level rotation and consistency constraints only on the target high-frequency tokens, thereby simultaneously suppressing background high-
frequency noise and enhancing robustness against directional changes.