reviews: 
Comments: This work introduces Cross-Image Object-Level Bootstrapping (CrIBo), a self-supervised visual representation pre-training approach that enforces object-level representation consistency across different images. Different from existing self-supervised pre-training strategies that only considers image-level representation consistency or cross-view object-level representation consistency, this work also aligns representations of semantically similar objects from different images. By introducing this cross-image object-level self-supervision, CrIBo pre-trains Vision Transformers (ViT) with improved visual representation quality, as demonstrated by the semantic segmentation experiments in various settings, including nearest neighbor retrieval, linear probing, and fine-tuning.
Strengths: - For the first time, this method introduces representation consistency of semantically similar objects from different images into self-supervised pre-training. This would be helpful for performing pre-training on image datasets of complex scenes.

- By leveraging the proposed cycle-consistency condition, trivial matchings between objects can be avoided and the visual encoder is enforced to learn more abstract semantic representations. This design can be helpful for future self-supervised pre-training approaches.

- The writing and figure illustrations are clear. I enjoyed reading this paper.
Weaknesses: - Inadequate experiments: The experiments only consider the semantic segmentation task, which is just one example of object-centric visual recognition tasks. It is strongly suggested to show the generalizability of CrIBO to object detection, instance segmentation, panoptic segmentation, etc.

- Efficiency comparison: Since CrIBO introduces additional operations like clustering and NN retrieval, it is suggested to compare CrIBO and previous methods in terms of the training efficiency. For example, time per epoch may be listed in Table 1.

- Fixed-$K$ clustering: The clustering algorithm (Sec 3.2.1), which comes from a previous work CrOC, is adopted here to identify $K$ objects and $K$ object-level representations in each view. My concern with this clustering algorithm (and other similar methods like K-means as in ODIN [1]) is that it always generates a fixed number of objects per image, regardless of the contents in the image under consideration. One simple image may contain less than $K$ objects; another complex scene may consist of much more than $K$ objects. Thus, it may be complicated to decide an optimal $K$ for a given pre-training dataset. Furthermore, even though an optimal $K$ is found, it can be sub-optimal for some images in the dataset. Would it be possible to improve over such fixed-$K$ clustering algorithms?

- Early-stage training signals: As introduced in Sec 3.2.2, a cycle-consistency criterion is applied to decide whether a pair of object representations is adopted in training. I would assume that at the early training stage, the representations are nearly random since the visual encoder is just initialized. Therefore, there may be only few or even zero object pairs that can pass the test. If I understand Figure 5 correctly, it supports this assumption. Would this phenomenon significantly reduce the available training signals and slow down the early-stage learning?

[1] Olivier J. Hénaff, Skanda Koppula, Evan Shelhamer, Daniel Zoran, Andrew Jaegle, Andrew Zisserman, João Carreira, Relja Arandjelović. Object discovery and representation networks. In ECCV, 2022.
Recommendation: 6: marginally above the acceptance threshold
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: 4 excellent