Beyond DINO features, there are several other self-supervised learning techniques that could be leveraged to enhance the object-centric representations learned by the model. One such technique is contrastive learning, which has shown promising results in learning robust visual representations. By training the model to maximize agreement between augmented views of the same image while minimizing agreement with views from other images, contrastive learning can help the model capture discriminative object features. Another technique is rotation prediction, where the model learns to predict the rotation angle of an image patch. This task encourages the model to capture invariant object features that are agnostic to the orientation of the object. Additionally, generative modeling approaches like generative adversarial networks (GANs) can be used to learn object-centric representations by generating realistic object samples and enforcing consistency between the generated samples and the input images. By incorporating these diverse self-supervised learning techniques, the model can learn more comprehensive and robust object-centric representations, improving its performance in unsupervised object discovery tasks.