To extend the proposed method to handle a variable number of objects in an image, we can introduce a dynamic slot allocation mechanism. Instead of fixing the number of slots beforehand, the model can learn to dynamically adjust the number of slots based on the complexity and diversity of objects present in the image. This can be achieved by incorporating a mechanism that allows the model to generate or remove slots as needed during the inference phase. One approach could be to introduce a gating mechanism that decides whether to activate or deactivate a slot based on the relevance of the slot to the current image context. By dynamically adjusting the number of slots, the model can adapt to images with varying numbers of objects, improving its flexibility and performance in handling different scenarios.