# Feature Binding with Category-Dependant MixUp for Semantic Segmentation and Adversarial Robustness Source: [Feature Binding with Category-Dependant MixUp for Semantic Segmentation and Adversarial Robustness]() Collection index: [llms.txt]() Data updated: 2025-10-25T22:31:00.251Z Authors: Md Amirul Islam, Matthew Kowal, Konstantinos G. Derpanis, Neil D. B. Bruce Published: 2020-08-13T03:20:01.000Z ## Abstract 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 activation's 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 their class labels, and then training 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 network while simultaneously increasing robustness to adversarial attacks. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()