The slot attention-based method is widely used in unsupervised object-centric learning, which aims to decompose scenes into interpretable objects and associate them with slots. However, complex backgrounds in the real images can disrupt the model’s focus, leading it to excessively segment background stuff into different regions based on low-level information such as color or texture variations. Consequently, the elaborate segmentation of foreground objects will be neglected, which requires detailed shape or geometric information.To address this issue, we introduce a contrastive learning-based indicator designed to differentiate between foreground and background. Integrating this indicator into the slot attention-based method allows the model to focus more effectively on segmenting foreground objects and minimize background distractions. During the testing phase, we utilize a spectral clustering mechanism to refine the results and mitigate oversegmentation according to the similarity between the slots.Experimental results show that incorporating our method with various state-of-the-art models significantly improves their performance on both simulated data and real-world datasets. Furthermore, multiple sets of ablation experiments confirm the effectiveness of each proposed component. Our code will be made available.