for a wide range of supervisory signals such as, video level action labels, single temporal point, one GT bounding box, temporal bounds etc. The method is experimentally evaluated on the UCF-101-24 and DALY action detection datasets. Paper Strengths: - The paper is clear and easy to understand. - The problem formulation is interesting and described with enough details. - The experimental results are interesting and promising, clearly demonstrate the significance of varying level of supervision on the detection performance - Table 1. - On DALY dataset, as expected, the detection performance increases with access to more supervision. - The proposed approach outperforms the SOA [46] by a large margin of 18% (video mAP) on DALY dataset at all levels of supervision. - On UCF-101-24, the proposed approach outperforms the SOA [46] when bounding box annotations are available at any level, i.e., Temp.+1 BB, Temp. + 3 BBs, Fully Spervised (cf. Table 1). - The visuals are helpful, support well the paper, and the qualitative experiments (in supplementary material) are interesting and convincing. Paper Weaknesses: I haven't noticed any major weakness in this paper, however would like to mention that - on UCF-101-24, the proposed method has drop in performance as compared to the SOA [46] when supervision level is "Temporal + spatial points". This work addresses one of the major problems associated with action detection approaches based on fully supervised learning, i.e., these methods require dense frame level GT bounding box annotations and their labels, which is impractical for large scale video datasets and also highly expensive. The proposed unified action detection framework provides a way to train a ML model with weak supervision at various levels, contributing significantly to address the aforementioned problem. Thus, I vote for a clear accept.
- The experimental results are interesting and promising, clearly demonstrate the significance of varying level of supervision on the detection performance - Table 1.