Search Results for author:
Found 16 papers, 4 papers with code
A Multimodal Approach for Cross-Domain Image Retrieval
With the latest AI technology, millions of high quality images are being generated by the public, which are constantly motivating the research community to push the limits of generative models to create more complex and realistic images.
Detecting and Triaging Spoofing using Temporal Convolutional Networks
As algorithmic trading and electronic markets continue to transform the landscape of financial markets, detecting and deterring rogue agents to maintain a fair and efficient marketplace is crucial.
Image edge enhancement for effective image classification
A widely popular embraced method to improve the classification performance of neural networks is to incorporate data augmentations during the training process.
Adaptive Anchor Label Propagation for Transductive Few-Shot Learning
Our novel algorithm, Adaptive Anchor Label Propagation}, outperforms the standard label propagation algorithm by as much as 7% and 2% in the 1-shot and 5-shot settings respectively.
Adaptive manifold for imbalanced transductive few-shot learning
Our method exploits the underlying manifold of the labeled support examples and unlabeled queries by using manifold similarity to predict the class probability distribution per query.
Towards Automated Polyp Segmentation Using Weakly- and Semi-Supervised Learning and Deformable Transformers
Also, our framework can be utilized to fine-tune models trained on natural image segmentation datasets drastically improving their performance for polyp segmentation and impressively demonstrating superior performance to fully supervised fine-tuning.
Dynamic Knowledge Distillation With Noise Elimination for RGB-D Salient Object Detection
RGB-D salient object detection (SOD) demonstrates its superiority on detecting in complex environments due to the additional depth information introduced in the data.
Tensor feature hallucination for few-shot learning
We follow a different approach and investigate how a simple and straightforward synthetic data generation method can be used effectively.
Progressive Multi-scale Fusion Network for RGB-D Salient Object Detection
We further introduce a mask-guided refinement module(MGRM) to complement the high-level semantic features and reduce the irrelevant features from multi-scale fusion, leading to an overall refinement of detection.
Few-shot learning via tensor hallucination
Few-shot classification addresses the challenge of classifying examples given only limited labeled data.
A novel shape matching descriptor for real-time hand gesture recognition
In this work we present a novel shape matching methodology for real-time hand gesture recognition.
Iterative label cleaning for transductive and semi-supervised few-shot learning
Few-shot learning amounts to learning representations and acquiring knowledge such that novel tasks may be solved with both supervision and data being limited.
Salient Object Detection Combining a Self-attention Module and a Feature Pyramid Network
Salient object detection has achieved great improvement by using the Fully Convolution Network (FCN).
Coupled Network for Robust Pedestrian Detection with Gated Multi-Layer Feature Extraction and Deformable Occlusion Handling
Ablation studies have validated the effectiveness of both the proposed gated multi-layer feature extraction sub-network and the deformable occlusion handling sub-network.
Gated Multi-layer Convolutional Feature Extraction Network for Robust Pedestrian Detection
In this paper, we propose a gated multi-layer convolutional feature extraction method which can adaptively generate discriminative features for candidate pedestrian regions.
SAM-RCNN: Scale-Aware Multi-Resolution Multi-Channel Pedestrian Detection
Convolutional neural networks (CNN) have enabled significant improvements in pedestrian detection owing to the strong representation ability of the CNN features.
