Search Results for author:
Found 36 papers, 5 papers with code
Auxiliary Tasks Enhanced Dual-affinity Learning for Weakly Supervised Semantic Segmentation
We propose AuxSegNet+, a weakly supervised auxiliary learning framework to explore the rich information from these saliency maps and the significant inter-task correlation between saliency detection and semantic segmentation.
An Intra-BRNN and GB-RVQ Based END-TO-END Neural Audio Codec
Recently, neural networks have proven to be effective in performing speech coding task at low bitrates.
Performance of multilabel machine learning models and risk stratification schemas for predicting stroke and bleeding risk in patients with non-valvular atrial fibrillation
Conclusions Multilabel ML models can outperform clinical risk stratification scores for predicting the risk of major bleeding and death in non-valvular AF patients.
Anti-aliasing Deep Image Classifiers using Novel Depth Adaptive Blurring and Activation Function
In this work, first, we analyse deep features with Fourier transform and show that Depth Adaptive Blurring is more effective, as opposed to monotonic blurring.
A novel network training approach for open set image recognition
Convolutional Neural Networks (CNNs) are commonly designed for closed set arrangements, where test instances only belong to some "Known Known" (KK) classes used in training.
Leveraging Auxiliary Tasks with Affinity Learning for Weakly Supervised Semantic Segmentation
Motivated by the significant inter-task correlation, we propose a novel weakly supervised multi-task framework termed as AuxSegNet, to leverage saliency detection and multi-label image classification as auxiliary tasks to improve the primary task of semantic segmentation using only image-level ground-truth labels.
A Survey of Deep Learning Techniques for Weed Detection from Images
The rapid advances in Deep Learning (DL) techniques have enabled rapid detection, localisation, and recognition of objects from images or videos.
Integrated Generalized Zero-Shot Learning for Fine-Grained Classification
In this paper, we propose to explore global and direct attribute-supervised local visual features for both EL and FS categories in an integrated manner for fine-grained GZSL.
Bidirectional Mapping Coupled GAN for Generalized Zero-Shot Learning
Bidirectional mapping-based generalized zero-shot learning (GZSL) methods rely on the quality of synthesized features to recognize seen and unseen data.
Generalized Zero-Shot Learning
Generative Adversarial Network
Imputation of Missing Data with Class Imbalance using Conditional Generative Adversarial Networks
State-of-the-art imputation approaches, such as Generative Adversarial Imputation Nets (GAIN), model the distribution of observed data to approximate the missing values.
RCNN for Region of Interest Detection in Whole Slide Images
We used 60 WSIs for training the RCNN model and another 12 WSIs for testing.
Robust Image Classification Using A Low-Pass Activation Function and DCT Augmentation
In this work, we instill low-pass filtering into the AF (LP-ReLU) to improve robustness against HFc.
Adversarial Network with Multiple Classifiers for Open Set Domain Adaptation
The proposed multi-classifier structure introduces a weighting module that evaluates distinctive domain characteristics for assigning the target samples with weights which are more representative to whether they are likely to belong to the known and unknown classes to encourage positive transfers during adversarial training and simultaneously reduces the domain gap between the shared classes of the source and target domains.
Unconstrained Matching of 2D and 3D Descriptors for 6-DOF Pose Estimation
We use this concept to directly localize images in a 3D point cloud.
Learning Latent Global Network for Skeleton-based Action Prediction
Human actions represented with 3D skeleton sequences are robust to clustered backgrounds and illumination changes.
Ranked #4 on
Skeleton Based Action Recognition
on SYSU 3D
Automatic Hierarchical Classification of Kelps using Deep Residual Features
Across the globe, remote image data is rapidly being collected for the assessment of benthic communities from shallow to extremely deep waters on continental slopes to the abyssal seas.
Direct Image to Point Cloud Descriptors Matching for 6-DOF Camera Localization in Dense 3D Point Cloud
We use this concept to localize the position and orientation (pose) of the camera of a query image in dense point clouds.
Language Modeling through Long Term Memory Network
Recurrent Neural Networks (RNN), Long Short-Term Memory Networks (LSTM), and Memory Networks which contain memory are popularly used to learn patterns in sequential data.
Enhanced Transfer Learning with ImageNet Trained Classification Layer
Thus, we hypothesize that the presence of this layer is crucial for growing network depth to adapt better to a new task.
Enhancing Semantic Word Representations by Embedding Deeper Word Relationships
Furthermore, the use of 3D visual representations has shown to be capable of representing the similarity and association between words.
A Comprehensive Survey of Deep Learning for Image Captioning
We discuss the foundation of the techniques to analyze their performances, strengths and limitations.
Real Time Surveillance for Low Resolution and Limited-Data Scenarios: An Image Set Classification Approach
Class specific gallery subspaces are used to estimate regression models for each image of the test image set.
Learning clip representations for skeleton-based 3d action recognition
This paper presents a new representation of skeleton sequences for 3D action recognition.
Ranked #60 on
Skeleton Based Action Recognition
on NTU RGB+D 120
Exploiting Layerwise Convexity of Rectifier Networks with Sign Constrained Weights
By introducing sign constraints on the weights, this paper proposes sign constrained rectifier networks (SCRNs), whose training can be solved efficiently by the well known majorization-minimization (MM) algorithms.
Skeletonnet: Mining deep part features for 3-d action recognition
Given a skeleton sequence, the spatial structure of the skeleton joints in each frame and the temporal information between multiple frames are two important factors for action recognition.
Ranked #106 on
Skeleton Based Action Recognition
on NTU RGB+D
A New Representation of Skeleton Sequences for 3D Action Recognition
This paper presents a new method for 3D action recognition with skeleton sequences (i. e., 3D trajectories of human skeleton joints).
Ranked #65 on
Skeleton Based Action Recognition
on NTU RGB+D 120
Efficient Image Set Classification using Linear Regression based Image Reconstruction
We estimate regression models for each test image using the class specific gallery subspaces.
ResFeats: Residual Network Based Features for Image Classification
Deep residual networks have recently emerged as the state-of-the-art architecture in image segmentation and object detection.
Leveraging Structural Context Models and Ranking Score Fusion for Human Interaction Prediction
The structural models, including the spatial and the temporal models, are learned with Long Short Term Memory (LSTM) networks to capture the dependency of the global and local contexts of each RGB frame and each optical flow image, respectively.
Learning deep structured network for weakly supervised change detection
We apply a constrained mean-field algorithm to estimate the pixel-level labels, and use the estimated labels to update the parameters of the CNN in an iterative EM framework.
Contractive Rectifier Networks for Nonlinear Maximum Margin Classification
The contractive constraints ensure that the achieved separating margin in the input space is larger than or equal to the separating margin in the output layer.
Cost Sensitive Learning of Deep Feature Representations from Imbalanced Data
Class imbalance is a common problem in the case of real-world object detection and classification tasks.
A Discriminative Representation of Convolutional Features for Indoor Scene Recognition
To this end, we introduce a new large-scale dataset of 1300 object categories which are commonly present in indoor scenes.
Separating Objects and Clutter in Indoor Scenes
Objects' spatial layout estimation and clutter identification are two important tasks to understand indoor scenes.
Automatic Feature Learning for Robust Shadow Detection
We present a practical framework to automatically detect shadows in real world scenes from a single photograph.
Rotational Projection Statistics for 3D Local Surface Description and Object Recognition
The performance of the proposed LRF, RoPS descriptor and object recognition algorithm was rigorously tested on a number of popular and publicly available datasets.
