78 citations · 198 across the 9 of their papers we have counts for
4 papers · 2 filters
Generalized Coarse-to-Fine Visual Recognition with Progressive Training
Xutong Ren, Lingxi Xie, Chen Wei +6
Computer vision is difficult, partly because the desired mathematical function connecting input and output data is often complex, fuzzy and thus hard to learn. Coarse-to-fine (C2F)…
Rethinking Monocular Depth Estimation with Adversarial Training
Richard Chen, Faisal Mahmood, Alan Yuille +1
Monocular depth estimation is an extensively studied computer vision problem with a vast variety of applications. Deep learning-based methods have demonstrated promise for both sup…
Resisting Large Data Variations via Introspective Transformation Network
Yunhan Zhao, Ye Tian, Charless Fowlkes +2
Training deep networks that generalize to a wide range of variations in test data is essential to building accurate and robust image classifiers. One standard strategy is to apply…
Improving Transferability of Adversarial Examples with Input Diversity
Cihang Xie, Zhishuai Zhang, Yuyin Zhou +4
Though CNNs have achieved the state-of-the-art performance on various vision tasks, they are vulnerable to adversarial examples --- crafted by adding human-imperceptible perturbati…