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Titlebook: Computer Vision – ECCV 2018; 15th European Confer Vittorio Ferrari,Martial Hebert,Yair Weiss Conference proceedings 2018 Springer Nature Sw

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51#
發(fā)表于 2025-3-30 09:22:59 | 只看該作者
Alain Guggenbühl,Margareta Theeleno apply the Gradient Estimation attacks successfully against real-world classifiers hosted by Clarifai. Further, we evaluate black-box attacks against state-of-the-art defenses based on adversarial training and show that the Gradient Estimation attacks are very effective even against these defenses.
52#
發(fā)表于 2025-3-30 16:19:50 | 只看該作者
Anniek de Ruijter,Tamara K. Herveyifferences with ResNet-50 in its corresponding layers. We conclude that invariance transformations are a major computational component learned by DNNs and we provide a systematic method to study them.
53#
發(fā)表于 2025-3-30 18:16:38 | 只看該作者
54#
發(fā)表于 2025-3-30 20:52:22 | 只看該作者
European Security Community Expansion,ector and by incorporating a face verification network, the attribute-guided network becomes the . which produces high-quality and interesting results on identity transfer. We demonstrate three applications on identity-guided conditional CycleGAN: identity-preserving face superresolution, face swapp
55#
發(fā)表于 2025-3-31 04:04:34 | 只看該作者
Deep Attention Neural Tensor Network for Visual Question Answeringttention module on tensor to select the most discriminative reasoning process for inference. Third, we optimize the proposed DA-NTN by learning a label regression with KL-divergence losses. Such a design enables scalable training and fast convergence over a large number of answer set. We integrate t
56#
發(fā)表于 2025-3-31 07:13:23 | 只看該作者
57#
發(fā)表于 2025-3-31 11:49:10 | 只看該作者
Interpretable Intuitive Physics Model(cube, cone, cylinder, spheres etc.) and test on collisions of unseen combinations of shapes. Furthermore, we demonstrate our model generalizes well even when similar scenes are simulated with different underlying properties.
58#
發(fā)表于 2025-3-31 17:04:14 | 只看該作者
Deep Multi-task Learning to Recognise Subtle Facial Expressions of Mental Statesdatasets without suffering from dataset distribution shift. To advance subtle expression recognition, we contribute a Large-scale Subtle Emotions and Mental States in the Wild?database (LSEMSW). LSEMSW?includes a variety of cognitive states as well as basic emotions. It contains 176K images, manuall
59#
發(fā)表于 2025-3-31 19:59:02 | 只看該作者
60#
發(fā)表于 2025-3-31 23:35:58 | 只看該作者
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