139 citations · 717 across the 57 of their papers we have counts for
11 papers · 1 filter
Few-Shot Object Detection via Synthetic Features with Optimal Transport
Anh-Khoa Nguyen Vu, Thanh-Toan Do, Vinh-Tiep Nguyen +3
Few-shot object detection aims to simultaneously localize and classify the objects in an image with limited training samples. However, most existing few-shot object detection metho…
CamoFA: A Learnable Fourier-based Augmentation for Camouflage Segmentation
Minh-Quan Le, Minh-Triet Tran, Trung-Nghia Le +2
Camouflaged object detection (COD) and camouflaged instance segmentation (CIS) aim to recognize and segment objects that are blended into their surroundings, respectively. While se…
Optimal Transport Model Distributional Robustness
Van-Anh Nguyen, Trung Le, Anh Tuan Bui +2
Distributional robustness is a promising framework for training deep learning models that are less vulnerable to adversarial examples and data distribution shifts. Previous works h…
Instance-dependent Noisy-label Learning with Graphical Model Based Noise-rate Estimation
Arpit Garg, Cuong Nguyen, Rafael Felix +2
Deep learning faces a formidable challenge when handling noisy labels, as models tend to overfit samples affected by label noise. This challenge is further compounded by the presen…
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…
The Art of Camouflage: Few-Shot Learning for Animal Detection and Segmentation
Thanh-Danh Nguyen, Anh-Khoa Nguyen Vu, Nhat-Duy Nguyen +5
Camouflaged object detection and segmentation is a new and challenging research topic in computer vision. There is a serious issue of lacking data on concealed objects such as camo…
Robotic Perception of Transparent Objects: A Review
Jiaqi Jiang, Guanqun Cao, Jiankang Deng +2
Transparent object perception is a rapidly developing research problem in artificial intelligence. The ability to perceive transparent objects enables robots to achieve higher leve…
PASS: Peer-Agreement based Sample Selection for training with Noisy Labels
Arpit Garg, Cuong Nguyen, Rafael Felix +2
The prevalence of noisy-label samples poses a significant challenge in deep learning, inducing overfitting effects. This has, therefore, motivated the emergence of learning with no…
MaskDiff: Modeling Mask Distribution with Diffusion Probabilistic Model for Few-Shot Instance Segmentation
Minh-Quan Le, Tam V. Nguyen, Trung-Nghia Le +3
Few-shot instance segmentation extends the few-shot learning paradigm to the instance segmentation task, which tries to segment instance objects from a query image with a few annot…
Flat Seeking Bayesian Neural Networks
Van-Anh Nguyen, Tung-Long Vuong, Hoang Phan +3
Bayesian Neural Networks (BNNs) provide a probabilistic interpretation for deep learning models by imposing a prior distribution over model parameters and inferring a posterior dis…
Task Weighting in Meta-learning with Trajectory Optimisation
Cuong Nguyen, Thanh-Toan Do, Gustavo Carneiro
Developing meta-learning algorithms that are un-biased toward a subset of training tasks often requires hand-designed criteria to weight tasks, potentially resulting in sub-optimal…