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Found 20 papers, 4 papers with code
LiT-4-RSVQA: Lightweight Transformer-based Visual Question Answering in Remote Sensing
Visual question answering (VQA) methods in remote sensing (RS) aim to answer natural language questions with respect to an RS image.
Multi-Modal Fusion Transformer for Visual Question Answering in Remote Sensing
To make the intrinsic information of each RS image easily accessible, visual question answering (VQA) has been introduced in RS.
Advanced Deep Learning Architectures for Accurate Detection of Subsurface Tile Drainage Pipes from Remote Sensing Images
In this study, we introduce two DL-based models: i) improved U-Net architecture; and ii) Visual Transformer-based encoder-decoder in the framework of tile drainage pipe detection.
On the Effects of Different Types of Label Noise in Multi-Label Remote Sensing Image Classification
In this paper, we investigate three different noise robust CV SLC methods and adapt them to be robust for multi-label noise scenarios in RS.
Unsupervised Contrastive Hashing for Cross-Modal Retrieval in Remote Sensing
To address this problem, in this paper we introduce a novel unsupervised cross-modal contrastive hashing (DUCH) method for text-image retrieval in RS.
An Unsupervised Cross-Modal Hashing Method Robust to Noisy Training Image-Text Correspondences in Remote Sensing
The proposed CHNR includes two training phases: i) meta-learning phase that uses a small portion of clean (i. e., reliable) data to train the noise detection module in an adversarial fashion; and ii) the main training phase for which the trained noise detection module is used to identify noisy correspondences while the hashing module is trained on the noisy multi-modal training set.
Deep Unsupervised Contrastive Hashing for Large-Scale Cross-Modal Text-Image Retrieval in Remote Sensing
To address this problem, in this paper we introduce a novel deep unsupervised cross-modal contrastive hashing (DUCH) method for RS text-image retrieval.
A Consensual Collaborative Learning Method for Remote Sensing Image Classification Under Noisy Multi-Labels
The proposed CCML identifies, ranks and corrects training images with noisy multi-labels through four main modules: 1) discrepancy module; 2) group lasso module; 3) flipping module; and 4) swap module.
Informative and Representative Triplet Selection for Multilabel Remote Sensing Image Retrieval
The proposed method selects a small set of the most representative and informative triplets based on two main steps.
Multi-Label Noise Robust Collaborative Learning for Remote Sensing Image Classification
To address this problem, the publicly available thematic products, which can include noisy labels, can be used to annotate RS images with zero-labeling cost.
Generative Models for Novelty Detection: Applications in abnormal event and situational change detection from data series
Novelty detection is one of the fundamental requirements of a good classification or identification system since sometimes the test data contains observations that were not known at the training time.
Hierarchy of GANs for learning embodied self-awareness model
In this work, a hierarchical model is introduced by means of a cross-modal Generative Adversarial Networks (GANs) processing high dimensional visual data.
Learning Multi-Modal Self-Awareness Models for Autonomous Vehicles from Human Driving
This paper presents a novel approach for learning self-awareness models for autonomous vehicles.
A Multi-perspective Approach To Anomaly Detection For Self-aware Embodied Agents
This paper focuses on multi-sensor anomaly detection for moving cognitive agents using both external and private first-person visual observations.
Abnormal Event Detection in Videos using Generative Adversarial Nets
In this paper we address the abnormality detection problem in crowded scenes.
Ranked #4 on
Abnormal Event Detection In Video
on UCSD Ped2
Training Adversarial Discriminators for Cross-channel Abnormal Event Detection in Crowds
Abnormal crowd behaviour detection attracts a large interest due to its importance in video surveillance scenarios.
Efficient Convolutional Neural Network with Binary Quantization Layer
We use binary encoding of CNN features to overcome the difficulty of the clustering on the high-dimensional CNN feature space.
Plug-and-Play CNN for Crowd Motion Analysis: An Application in Abnormal Event Detection
In this paper, we show that keeping track of the changes in the CNN feature across time can facilitate capturing the local abnormality.
CNN-aware Binary Map for General Semantic Segmentation
To the best of our knowledge our method is the first attempt on general semantic image segmentation using CNN.
Action Recognition with Image Based CNN Features
Action recognition tasks usually relies on complex handcrafted structures as features to represent the human action model.
