Venue: ICLR 2023 TinyPapers

Reviews:

final_score: rating: Clear, Correct, and Reproducible (CCR): a submission which meets the reviewing criteria
review_content: summary_of_contributions: This paper adapts the object detector for new category using a combination of a few labeled images and weakly labeled data. The proposed iterative fine-tuning framework cycles through predicting pseudo-labels, filtering them using weak labels, and fine-tuning the model on this data.
strengths_and_weaknesses: Strong aspects:

1. The paper addresses an important problem in object detection: detecting novel classes with limited labeled data.

2. The paper provides a clear overview of the proposed framework with a well-designed figure.

3. The proposed iterative fine-tuning framework can significantly reduce the labeling effort by using a few labeled images and weakly labeled data.

4. The paper includes detailed explanations of the weak label types used in the experiments and the pseudo-label filter method used to filter bounding boxes using weak labels.
suggested_changes: 1. In Abstract, it is claimed that "training object detectors for new tasks". Regarding the setting in this paper, it might be more appropriate to use terms such as "for new classes" or "for new image domain".
review_title: null

final_score: rating: Clear, Correct, and Reproducible (CCR): a submission which meets the reviewing criteria
review_content: summary_of_contributions: A framework for leveraging weakly labeled image to improve object detection on novel classes are proposed.
strengths_and_weaknesses: S1: I think overall this paper is very clear, especially in the experiments section.

S2: The paper also discusses in-depth on the implementation details and the used dataset, making it reproducible.

W1: the only experiment is on only one novel classes (tomatoes), It would be interesting and comprehensive to try the proposed framework on more classes.

W2: The comparison with other methods are lacking, for example, I think one baseline to compare to can be a semi-supervised object detection baseline which use the weakly labeled data as the unlabeled dataset.
suggested_changes: C1: Adding a comparison with semi-supervised object detection methods, and maybe another possible related detection method.

C2: I would suggest to extend the experiments on more classes other than just tomatoes.
review_title: null

Meta-review: metareview: This paper proposed a framework for object detection of new categories using a few labeled examples and weakly labeled data.
The two reviewers both found the paper to be CCR, the method is presented clearly with detailed explanations of the implementation details.
The weaknesses found by reviewer are, the experiments are only limited to one class, and missing baselines in semi-supervised object detection.
Overall this paper is CCR, but it is recommended that the author to address some of the concerns by the reviewers in the final version.

confidence: 5: The area chair is absolutely certain
Decision: Invite to present