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Found 33 papers, 2 papers with code
Attention based End to end network for Offline Writer Identification on Word level data
However, in scenarios where only a limited number of handwritten samples are available, particularly in the form of word images, there is a significant scope for improvement.
Variable-Pitch-Propeller Mechanism Design, and Development of Heliquad for Mid-flight Flipping and Fault-Tolerant-Control
Heliquad is controlled by a unified non-switching cascaded attitude-rate controller, followed by a unique Neural-Network-based reconfigurable control allocation to approximate nonlinear relationship between the control input and actuator command.
Towards Improved Imbalance Robustness in Continual Multi-Label Learning with Dual Output Spiking Architecture (DOSA)
A novel imbalance-aware loss function is also proposed, improving the multi-label classification performance of the model by making it more robust to data imbalance.
Graph-based Prediction and Planning Policy Network (GP3Net) for scalable self-driving in dynamic environments using Deep Reinforcement Learning
A deep Graph-based Prediction and Planning Policy Network (GP3Net) framework is proposed for non-stationary environments that encodes the interactions between traffic participants with contextual information and provides a decision for safe maneuver for AV.
A Comprehensive Study on Modelling and Control of Autonomous Underwater Vehicle
Both conventional and nonlinear SMC controllers' outcomes are showcased with a lawn-mowing manoeuvre scenario.
Contrastive Learning-Based Spectral Knowledge Distillation for Multi-Modality and Missing Modality Scenarios in Semantic Segmentation
To address this, a novel multi-modal fusion approach called CSK-Net is proposed, which uses a contrastive learning-based spectral knowledge distillation technique along with an automatic mixed feature exchange mechanism for semantic segmentation in optical (EO) and infrared (IR) images.
MRFP: Learning Generalizable Semantic Segmentation from Sim-2-Real with Multi-Resolution Feature Perturbation
Deep neural networks have shown exemplary performance on semantic scene understanding tasks on source domains, but due to the absence of style diversity during training, enhancing performance on unseen target domains using only single source domain data remains a challenging task.
DeepMAO: Deep Multi-scale Aware Overcomplete Network for Building Segmentation in Satellite Imagery
Experimental results on SpaceNet 6 dataset, on both EO and SAR modalities, and the INRIA dataset show that DeepMAO achieves state-of-the-art building segmentation performance, including small and complex-shaped buildings with a negligible increase in the parameter count.
Segmentation
The Semantic Segmentation Of Remote Sensing Imagery
Priority-based DREAM Approach for Highly Manoeuvring Intruders in A Perimeter Defense Problem
In this paper, a Priority-based Dynamic REsource Allocation with decentralized Multi-task assignment (P-DREAM) approach is presented to protect a territory from highly manoeuvring intruders.
Predictive Maneuver Planning with Deep Reinforcement Learning (PMP-DRL) for comfortable and safe autonomous driving
Our proposed approach combines a predictive model and an RL agent to plan for comfortable and safe maneuvers.
A Decentralized Spike-based Learning Framework for Sequential Capture in Discrete Perimeter Defense Problem
The output of MLC-SEFRON contains the labels of segments that a defender has to visit in order to protect the perimeter.
Multi-Modal Domain Fusion for Multi-modal Aerial View Object Classification
Using just aerial view Electro-optical(EO) images for ATR systems may also not result in high accuracy as these images are of low resolution and also do not provide ample information in extreme weather conditions.
Fully Complex-valued Fully Convolutional Multi-feature Fusion Network (FC2MFN) for Building Segmentation of InSAR images
Building segmentation in high-resolution InSAR images is a challenging task that can be useful for large-scale surveillance.
Fully complex-valued deep learning model for visual perception
This paper proposes that operating entirely in the complex domain increases the overall performance of complex-valued models.
Siamese based Neural Network for Offline Writer Identification on word level data
These features are then passed through a deep Convolutional Neural Network (CNN) in which the weights are learned by applying the concept of Similarity learning using Siamese network.
Offline Text-Independent Writer Identification based on word level data
These key points are then passed through a trained CNN network to generate feature maps corresponding to a convolution layer.
Confidence Conditioned Knowledge Distillation
Distillation through CCKD methods improves the resilience of the student models against adversarial attacks compared to the conventional KD method.
Robust EMRAN-aided Coupled Controller for Autonomous Vehicles
Combined with a self-regulating learning scheme for improving generalization performance, the proposed EMRAN-aided control architecture aids a basic PID cruise and Stanley path-tracking controllers in a coupled form.
Online Lifelong Generalized Zero-Shot Learning
Methods proposed in the literature for zero-shot learning (ZSL) are typically suitable for offline learning and cannot continually learn from sequential streaming data.
Spatio-Temporal Look-Ahead Trajectory Prediction using Memory Neural Network
This paper attempts to solve the problem of Spatio-temporal look-ahead trajectory prediction using a novel recurrent neural network called the Memory Neuron Network.
Design and Integration of a Drone based Passive Manipulator for Capturing Flying Targets
In this paper, we present a novel passive single Degree-of-Freedom (DoF) manipulator design and its integration on an autonomous drone to capture a moving target.
Robotics
Design Iterations for Passive Aerial Manipulator
This paper presents the design, development, and prototyping of a novel aerial manipulator for target interception.
Robotics
A Decentralized Multi-UAV Spatio-Temporal Multi-Task Allocation Approach for Perimeter Defense
This paper provides a new solution approach to a multi-player perimeter defense game, in which the intruders' team tries to enter the territory, and a team of defenders protects the territory by capturing intruders on the perimeter of the territory.
Self Regulated Learning Mechanism for Data Efficient Knowledge Distillation
This method is named significance-based distillation.
Generative Replay-based Continual Zero-Shot Learning
Zero-shot learning is a new paradigm to classify objects from classes that are not available at training time.
Meta-Cognition-Based Simple And Effective Approach To Object Detection
Recently, many researchers have attempted to improve deep learning-based object detection models, both in terms of accuracy and operational speeds.
CGAP2: Context and gap aware predictive pose framework for early detection of gestures
Furthermore, the ablation study indicates supplying higher context information to the pose prediction module can be detrimental for anticipatory recognition.
Generalized Continual Zero-Shot Learning
Further, to enhance the reliability, we develop CZSL for a single head continual learning setting where task identity is revealed during the training process but not during the testing.
Full Attitude Intelligent Controller Design of a Heliquad under Complete Failure of an Actuator
The results clearly indicate that the Heliquad with an intelligent controller provides necessary tracking performance even under a complete loss of one actuator.
Scaffolding Reflection in Reinforcement Learning Framework for Confinement Escape Problem
In CEP, an evader's objective is to attempt escaping a confinement region patrolled by multiple pursuers.
Context-Aware Deep Q-Network for Decentralized Cooperative Reconnaissance by a Robotic Swarm
One of the crucial problems in robotic swarm-based operation is to search and neutralize heterogeneous targets in an unknown and uncertain environment, without any communication within the swarm.
Multiagent Systems
Efficient single input-output layer spiking neural classifier with time-varying weight model
The performance of SEF-M is evaluated against state-of-the-art spiking neural network learning algorithms on 10 benchmark datasets from UCI machine learning repository.
A novel method for extracting interpretable knowledge from a spiking neural classifier with time-varying synaptic weights
A set of FSFs corresponding to each output class represents the extracted knowledge from the classifier.
