Given the variety of the visual world there is not one true scale for recognition: objects may appear at drastically different sizes across the visual field. Rather than enumerate variations across filter channels or pyramid levels, dynamic models locally predict scale and adapt receptive fields accordingly. The degree of variation and diversity of inputs makes this a difficult task. Existing methods either learn a feedforward predictor, which is not itself totally immune to the scale variation it is meant to counter, or select scales by a fixed algorithm, which cannot learn from the given task and data. We extend dynamic scale inference from feedforward prediction to iterative optimization for further adaptivity. We propose a novel entropy minimization objective for inference and optimize over task and structure parameters to tune the model to each input. Optimization during inference improves semantic segmentation accuracy and generalizes better to extreme scale variations that cause feedforward dynamic inference to falter.
Summary:
The following work proposes a test-time optimization over scales to improve semantic segmentation. Specifically, at test time, they iteratively optimize over the score and scale parameters of Shellhamer et al 2019, where a Gaussian receptive field is used to allow for dynamic scale adaptation of each convolutional layer. They optimize the parameters with respect to an entropy minimization objective. Experiments on PASCAL VOC, reported at multiple scales, demonstrate improvements in IOU over the vanilla architecture.
Strengths:
-The work was well-motivated
-Formulation is pretty elegant and outperforms the baseline
Weaknesses:
While I liked the somewhat elegant formulation of dynamic test-time scaling proposed by the following work, I don't think this work introduced many novel results nor insights
-Multiscale test-time inference is already standard in state-of-the-art architectures such as DeepLab. Specifically, DeepLab runs inference at multiple scales, then max pools the logit responses across all scales. (see Table 3 of https://arxiv.org/pdf/1511.03339.pdf)
-Using entropy as a measure of network uncertainty is a good idea, but also not a novel finding.
-COCO and Cityscapes would probably have been better choices for datasets with larger variations in scale
Improvements:
-Try comparing against the multiscale logit-max-pooling inference procedure as a baseline -- or demonstrate that the proposed technique can further improve upon the results of the logit-pooling technique.
-Some details weren't very well explained. Specifically, what is the $\theta_{score}$ task parameter for?
** Post Rebuttal Response
I'd like to thank the authors for clarifying some points in their response. Overall, I maintain that I think the optimization-based scale inference solution they present is interesting from an implementation standpoint, but the findings in this work did not yield sufficient new insight for the task. While I agree that their approach is fairly different from common approaches such as discrete image pyramids, a thorough quantitative comparison of these differences would make this work a lot stronger. As such, I maintain my original rating.