A learning method for learning parameters of convolutional neural network, CNN, by using multiple video frames for detecting or segmenting an object from video frames, wherein the CNN is configured to backpropagate a loss value obtained based on a detecting or segmenting result and ground truth, GT, comprising steps of: (a) a learning device applying at least one convolutional operation to a (t-k)-th input video frame corresponding to a (t-k)-th video frame and applying at least one convolutional operation to a t-th input video frame corresponding to a t-th video frame following the (t-k)-th video frame, to thereby obtain a (t-k)-th feature map corresponding to the (t-k)-th video frame and a t-th feature map corresponding to the t-th video frame; (b) the learning device calculating a first loss by using each of at least one distance value between each of pixels in the (t-k)-th feature map and each of pixels in the t-th feature map; and (c) the learning device backpropagating the first loss to minimize the first loss, wherein the first loss is calculated by multiplying (i) each of the at least one distance value between each of the features in the (t-k)-th feature map and each of the features in the t-th feature map and (ii) its corresponding first loss weight, and wherein the first loss weight indicates a degree of common region between a receptive field of the (t-k)-th feature map and a receptive field of the t-th feature map, the receptive field being a pixel area used to calculate a particular feature in the (t-k)-th feature map, wherein, at the step of (b), the learning device (i) calculates a (2-1)-th loss based on a difference between a (t-k)-th ground truth (GT) value and a (t-k)-th output value, which is generated by using the (t-k)-th feature map, and (ii) calculates a (2-2)-th loss based on a difference between a t-th GT value and a t-th output value, which is generated by using the t-th feature map, and wherein, at the step of (c), the learning device backpropagates the (2-1)-th loss and the (2-2)-th loss to minimize them and thereby optimize the at least one parameter of the CNN. (a) a learning device applying at least one convolutional operation to a (t-k)-th input video frame corresponding to a (t-k)-th video frame and applying at least one convolutional operation to a t-th input video frame corresponding to a t-th video frame following the (t-k)-th video frame, to thereby obtain a (t-k)-th feature map corresponding to the (t-k)-th video frame and a t-th feature map corresponding to the t-th video frame; (b) the learning device calculating a first loss by using each of at least one distance value between each of pixels in the (t-k)-th feature map and each of pixels in the t-th feature map; and (c) the learning device backpropagating the first loss to minimize the first loss, wherein the first loss is calculated by multiplying (i) each of the at least one distance value between each of the features in the (t-k)-th feature map and each of the features in the t-th feature map and (ii) its corresponding first loss weight, and wherein the first loss weight indicates a degree of common region between a receptive field of the (t-k)-th feature map and a receptive field of the t-th feature map, the receptive field being a pixel area used to calculate a particular feature in the (t-k)-th feature map, wherein, at the step of (b), the learning device (i) calculates a (2-1)-th loss based on a difference between a (t-k)-th ground truth (GT) value and a (t-k)-th output value, which is generated by using the (t-k)-th feature map, and (ii) calculates a (2-2)-th loss based on a difference between a t-th GT value and a t-th output value, which is generated by using the t-th feature map, and wherein, at the step of (c), the learning device backpropagates the (2-1)-th loss and the (2-2)-th loss to minimize them and thereby optimize the at least one parameter of the CNN. <EOS>