SegMix: Co-occurrence Driven Mixup for Semantic Segmentation and Adversarial Robustness
This addresses improved accuracy and robustness in semantic segmentation for computer vision applications, representing a novel approach rather than an incremental improvement.
The paper tackles interference from competing hypotheses in semantic segmentation by blending images based on categorical clustering or co-occurrence likelihood, training a network to segment and separate them, which boosts segmentation performance and adversarial robustness.
In this paper, we present a strategy for training convolutional neural networks to effectively resolve interference arising from competing hypotheses relating to inter-categorical information throughout the network. The premise is based on the notion of feature binding, which is defined as the process by which activations spread across space and layers in the network are successfully integrated to arrive at a correct inference decision. In our work, this is accomplished for the task of dense image labelling by blending images based on (i) categorical clustering or (ii) the co-occurrence likelihood of categories. We then train a feature binding network which simultaneously segments and separates the blended images. Subsequent feature denoising to suppress noisy activations reveals additional desirable properties and high degrees of successful predictions. Through this process, we reveal a general mechanism, distinct from any prior methods, for boosting the performance of the base segmentation and saliency network while simultaneously increasing robustness to adversarial attacks.