1. An object recognition method based on weakly supervised learning, the method performed by an object recognition apparatus and comprising: extracting a plurality of feature maps from a training target image given classification result of an object of interest; generating an activation map for each of the object of interest by accumulating the feature maps; calculating a representative value of each of the object of interest by aggregating activation values included in a corresponding activation map; determining an error by comparing classification result determined using the representative value of each of the object of interest with the given classification result; and updating a convolutional neural network (CNN)-based object recognition model by back-propagating the error, wherein the generating of the activation map comprises: determining an activation value at a first location in the activation map using feature values at the first location in the feature maps; and determining an activation value at a second location in the activation map using feature values at the second location in the feature maps, and wherein the activation map is an activation map of a first object of interest, and the determining of the activation value at the first location in the activation map comprises adjusting the activation value at the first location in the activation map of the first object of interest to increase a difference between the activation value at the first location in the activation map of the first object of interest and an activation value at the first location in an activation map of a second object of interest different from the first object of interest.