The field of computer vision is witnessing significant advancements in self-supervised learning and clustering techniques. Researchers are exploring new approaches to improve the efficiency and effectiveness of these methods, enabling them to tackle complex tasks with greater accuracy. One notable direction is the development of more sophisticated masking strategies for autoencoders, which can enhance their ability to learn meaningful representations. Additionally, innovations in mini-batch training and anchor-based clustering are allowing for more scalable and robust solutions. Noteworthy papers include:

• A paper that proposes a self-guided masked autoencoder, which internally generates informed masks to boost its learning process.
• A paper that introduces a mini-batch training strategy for deep subspace clustering networks, enabling scalable training with high-resolution images.
• A paper that presents LargeMvC-Net, a deep unfolding network for large-scale multi-view clustering, which achieves state-of-the-art performance on several benchmarks.
• A paper that proposes MZNet, a U-shaped network for moiré removal, which effectively eliminates artifacts and achieves competitive results on benchmark datasets.