9 citations · 18 across the 10 of their papers we have counts for
12 papers
Adaptive Anchor Label Propagation for Transductive Few-Shot Learning
Michalis Lazarou, Yannis Avrithis, Guangyu Ren +1
Few-shot learning addresses the issue of classifying images using limited labeled data. Exploiting unlabeled data through the use of transductive inference methods such as label pr…
Adaptive manifold for imbalanced transductive few-shot learning
Michalis Lazarou, Yannis Avrithis, Tania Stathaki
Transductive few-shot learning algorithms have showed substantially superior performance over their inductive counterparts by leveraging the unlabeled queries. However, the vast ma…
Towards Automated Polyp Segmentation Using Weakly- and Semi-Supervised Learning and Deformable Transformers
Guangyu Ren, Michalis Lazarou, Jing Yuan +1
Polyp segmentation is a crucial step towards computer-aided diagnosis of colorectal cancer. However, most of the polyp segmentation methods require pixel-wise annotated datasets. A…
Progressive Multi-scale Fusion Network for RGB-D Salient Object Detection
Guangyu Ren, Yanchu Xie, Tianhong Dai +1
Salient object detection(SOD) aims at locating the most significant object within a given image. In recent years, great progress has been made in applying SOD on many vision tasks.…
Dynamic Knowledge Distillation With Noise Elimination for RGB-D Salient Object Detection
Guangyu Ren, Yinxiao Yu, Hengyan Liu +1
RGB-D salient object detection (SOD) demonstrates its superiority on detecting in complex environments due to the additional depth information introduced in the data. Inevitably, a…
Tensor feature hallucination for few-shot learning
Michalis Lazarou, Tania Stathaki, Yannis Avrithis
Few-shot learning addresses the challenge of learning how to address novel tasks given not just limited supervision but limited data as well. An attractive solution is synthetic da…
Few-shot learning via tensor hallucination
Michalis Lazarou, Yannis Avrithis, Tania Stathaki
Few-shot classification addresses the challenge of classifying examples given only limited labeled data. A powerful approach is to go beyond data augmentation, towards data synthes…
Iterative label cleaning for transductive and semi-supervised few-shot learning
Michalis Lazarou, Tania Stathaki, Yannis Avrithis
Few-shot learning amounts to learning representations and acquiring knowledge such that novel tasks may be solved with both supervision and data being limited. Improved performance…
Salient Object Detection Combining a Self-attention Module and a Feature Pyramid Network
Guangyu Ren, Tianhong Dai, Panagiotis Barmpoutis +1
Salient object detection has achieved great improvement by using the Fully Convolution Network (FCN). However, the FCN-based U-shape architecture may cause the dilution problem in…
Coupled Network for Robust Pedestrian Detection with Gated Multi-Layer Feature Extraction and Deformable Occlusion Handling
Tianrui Liu, Wenhan Luo, Lin Ma +3
Pedestrian detection methods have been significantly improved with the development of deep convolutional neural networks. Nevertheless, detecting small-scaled pedestrians and occlu…
Gated Multi-layer Convolutional Feature Extraction Network for Robust Pedestrian Detection
Tianrui Liu, Jun-Jie Huang, Tianhong Dai +2
Pedestrian detection methods have been significantly improved with the development of deep convolutional neural networks. Nevertheless, robustly detecting pedestrians with a large…
SAM-RCNN: Scale-Aware Multi-Resolution Multi-Channel Pedestrian Detection
Tianrui Liu, Mohamed Elmikaty, Tania Stathaki
Convolutional neural networks (CNN) have enabled significant improvements in pedestrian detection owing to the strong representation ability of the CNN features. Recently, aggregat…