reviews: 
Comments: The authors propose a self-supervised cross-image object-level bootstrapping method for dense visual representation learning. Specifically, the authors first use clustering algorithms to cluster dense features to obtain object features. Afterwards, the authors use object features from one view to retrieve object-level nearest neighbors from the memory bank, and then enforce consistency between each retrieved neighbor and its corresponding object feature from another view. Extensive experiments on multiple dense downstream tasks demonstrate the superiority of the proposed cross-image object-level learning paradigm.
Strengths: -	The paper is well-motivated. The authors focus on two kinds of self-supervision in SSL: (i) cross-image self-supervision, and (ii) object-level self-supervision. Compared with cross-view self-supervision, cross-image self-supervision can provide more natural and diverse inter-image invariance. Compared with image-level self-supervision, object-level self-supervision is finer-grained and more suitable for scene-centric datasets. Based on this motivation, the authors extend existing cross-image SSL from the image level to the object level.

-	The paper is generally well-written and easy to follow.

-	The authors conduct extensive experiments and demonstrate promising results on various dense downstream tasks.
Weaknesses: -	The authors claim that CrIBo is the first SSL approach that explicitly enforces cross-image consistency at the object/object-part level. However, some prior SSL works (e.g., [1]) have already performed cross-image SSL at the object/object-part level. Specifically, [1] enforces cross-image object-level consistency with KNN-retrieved cross-image object-instance pairs. The main difference is that [1] uses region proposal algorithms to produce region-level object features, whereas CrIBo uses clustering algorithms to produce pixel-level object features. Therefore, I suggest the authors discuss the differences with [1] and modify the claim accordingly.

-	For cross-image self-supervision, there are several closely related works (e.g., [2, 3, 4]) that should also be discussed in Sec. 2.

-	It seems that CrIBo does not perform very well in the fine-tuning evaluation. For example, as shown in Table 3, CrIBo performs worse than MAE when pre-trained on IN1K. Using a larger ViT-B/16 backbone even degrades the performance. Could the authors provide some explanations on this?

-	The authors use the clustering algorithm in [5]. Could the authors provide the justification on this? What if other clustering algorithms (e.g., k-means) are used? Since the quality of object representations depends on the clustering algorithms, an ablation study on different clustering algorithms would be an interesting experiment to explore.

-	It seems that CrIBo tends to induce heavy computational costs. A computational cost comparison (e.g., training time, GPU memory) with previous methods is preferred.
Recommendation: 8: accept, good paper
Reviewer confidence: 5: You are absolutely certain about your assessment. You are very familiar with the related work and checked the math/other details carefully.
Soundness/correctness: 3 good
Clarity: 3 good