34 citations · 58 across the 7 of their papers we have counts for
7 papers
Instance-level Few-shot Learning with Class Hierarchy Mining
Anh-Khoa Nguyen Vu, Thanh-Toan Do, Nhat-Duy Nguyen +3
Few-shot learning is proposed to tackle the problem of scarce training data in novel classes. However, prior works in instance-level few-shot learning have paid less attention to e…
Instance-Dependent Noisy Label Learning via Graphical Modelling
Arpit Garg, Cuong Nguyen, Rafael Felix +2
Noisy labels are unavoidable yet troublesome in the ecosystem of deep learning because models can easily overfit them. There are many types of label noise, such as symmetric, asymm…
Where Shall I Touch? Vision-Guided Tactile Poking for Transparent Object Grasping
Jiaqi Jiang, Guanqun Cao, Aaron Butterworth +2
Picking up transparent objects is still a challenging task for robots. The visual properties of transparent objects such as reflection and refraction make the current grasping meth…
A4T: Hierarchical Affordance Detection for Transparent Objects Depth Reconstruction and Manipulation
Jiaqi Jiang, Guanqun Cao, Thanh-Toan Do +1
Transparent objects are widely used in our daily lives and therefore robots need to be able to handle them. However, transparent objects suffer from light reflection and refraction…
Multi-Modal Mutual Information Maximization: A Novel Approach for Unsupervised Deep Cross-Modal Hashing
Tuan Hoang, Thanh-Toan Do, Tam V. Nguyen +1
In this paper, we adopt the maximizing mutual information (MI) approach to tackle the problem of unsupervised learning of binary hash codes for efficient cross-modal retrieval. We…
Binary Hashing with Semidefinite Relaxation and Augmented Lagrangian
Thanh-Toan Do, Anh-Dzung Doan, Duc-Thanh Nguyen +1
This paper proposes two approaches for inferencing binary codes in two-step (supervised, unsupervised) hashing. We first introduce an unified formulation for both supervised and un…