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A Unified Architecture of Semantic Segmentation and Hierarchical Generative Adversarial Networks for Expression Manipulation
Rumeysa Bodur, Binod Bhattarai, Tae-Kyun Kim
Editing facial expressions by only changing what we want is a long-standing research problem in Generative Adversarial Networks (GANs) for image manipulation. Most of the existing…
EvDistill: Asynchronous Events to End-task Learning via Bidirectional Reconstruction-guided Cross-modal Knowledge Distillation
Lin Wang, Yujeong Chae, Sung-Hoon Yoon +2
Event cameras sense per-pixel intensity changes and produce asynchronous event streams with high dynamic range and less motion blur, showing advantages over conventional cameras. A…
SeCGAN: Parallel Conditional Generative Adversarial Networks for Face Editing via Semantic Consistency
Jiaze Sun, Binod Bhattarai, Zhixiang Chen +1
Semantically guided conditional Generative Adversarial Networks (cGANs) have become a popular approach for face editing in recent years. However, most existing methods introduce se…
Visual Transformer for Task-aware Active Learning
Razvan Caramalau, Binod Bhattarai, Tae-Kyun Kim
Pool-based sampling in active learning (AL) represents a key framework for an-notating informative data when dealing with deep learning models. In this paper, we present a novel pi…
Label Geometry Aware Discriminator for Conditional Generative Networks
Suman Sapkota, Bidur Khanal, Binod Bhattarai +2
Multi-domain image-to-image translation with conditional Generative Adversarial Networks (GANs) can generate highly photo realistic images with desired target classes, yet these sy…
Learning Feature Aggregation for Deep 3D Morphable Models
Zhixiang Chen, Tae-Kyun Kim
3D morphable models are widely used for the shape representation of an object class in computer vision and graphics applications. In this work, we focus on deep 3D morphable models…
Geometry-based Distance Decomposition for Monocular 3D Object Detection
Xuepeng Shi, Qi Ye, Xiaozhi Chen +3
Monocular 3D object detection is of great significance for autonomous driving but remains challenging. The core challenge is to predict the distance of objects in the absence of ex…
Adversarial Imitation Learning with Trajectorial Augmentation and Correction
Dafni Antotsiou, Carlo Ciliberto, Tae-Kyun Kim
Deep Imitation Learning requires a large number of expert demonstrations, which are not always easy to obtain, especially for complex tasks. A way to overcome this shortage of labe…