Duplicate task settings. The proposed new task, cross-supervised object detection, is almost the same as the task defined in (Hoffman et al. 2014, Tang et al. 2016, Uijlings et al. 2018). Both of these previous works study the task of training object detectors on the combination of base class images with instance-level annotations and novel class image with only image-level annotations. The work (Uijlings et al. 2018) also conducts experiments on COCO which contains multi-objects in images. In addition, the work (Khandelwal et al. 2020) unifies the setting of training object detectors on the combination of fully-labeled data and weakly-labeled data, and conducts experiments on multi-object datasets PASCAL VOC and COCO. The task proposed by this paper could be treated as a special case of the task studied in (Khandelwal et al. 2020). We should avoid duplicate task settings.
Limited novelty. The novelty of the proposed method is limited. Combining recognition head and detection head is not new in weakly supervised object detection. The weakly supervised object detection networks (Yang et al. 2019, Zeng et al. 2019) also generate pseudo instance-level annotations from recognition head to train detection head (i.e., head with bounding box classification and regression) for weakly-labeled data.
Review summary: In summary, I would like to give a rejection to this paper due to the duplicate task settings and limited novelty.
Khandelwal et al., Weakly-supervised Any-shot Object Detection, 2020
---------- Post rebuttal ----------
After discussions with authors and reading other reviews, I acknowledge the contribution that this paper advances the performance of cross-supervised object detection.
However, I would like to keep my original reject score. The reasons are as follows.
Extending datasets from PASCAL VOC to COCO is not a significant change comparing to previous tasks. The general object detection papers also evaluated on PASCAL VOC only about five years ago and now evaluate mainly on COCO. With the development of computer vision techniques, it is natural to try more challenging datasets. So although this paper claims that this paper focuses on more challenging datasets, there is no significant difference between the tasks studied in previous works like [a] and this paper.
In addition, apart from ImageNet, the work [b] also evaluates their method on the Open Images dataset which is even larger and more challenging than COCO. The difference between the tasks studied in [b] and this paper is only that, [b] adds a constraint that weakly-labeled classes have semantic correlations with fully-labeled classes and this paper doesn't. This difference is also minor.
Therefore, the task itself cannot be one of the main contributions of this paper (especially the most important contribution of this paper). I would like to suggest the authors change their title / introduction / main paper by 1) giving lower wights to the task parts 2) giving higher weights to intuitions of why previous works fail on challenging datasets like COCO and motivations of the proposed method.
[a] YOLO9000: Better, Faster, Stronger, In CVPR, 2017
[b] Detecting 11K Classes: Large Scale Object Detection without Fine-Grained Bounding Boxes, In ICCV, 2019
2) giving higher weights to intuitions of why previous works fail on challenging datasets like COCO and motivations of the proposed method. [a] YOLO9000: Better, Faster, Stronger, In CVPR, 2017 [b] Detecting 11K Classes: Large Scale Object Detection without Fine-Grained Bounding Boxes, In ICCV, 2019