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Given the variety of the visual world there is not one true scale forrecognition: objects may appear at drastically different sizes across thevisual field. Rather than enumerate variations across filter channels orpyramid levels, dynamic models locally predict scale and adapt receptive fieldsaccordingly. The degree of variation and diversity of inputs makes this adifficult task. Existing methods either learn a feedforward predictor, which isnot itself totally immune to the scale variation it is meant to counter, orselect scales by a fixed algorithm, which cannot learn from the given task anddata. We extend dynamic scale inference from feedforward prediction toiterative optimization for further adaptivity. We propose a novel entropyminimization objective for inference and optimize over task and structureparameters to tune the model to each input. Optimization during inferenceimproves semantic segmentation accuracy and generalizes better to extreme scalevariations that cause feedforward dynamic inference to falter.

Dynamic Scale Inference by Entropy Minimization