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Found 18 papers, 8 papers with code
One-Class Convolutional Neural Network
We present a novel Convolutional Neural Network (CNN) based approach for one class classification.
Active Authentication using an Autoencoder regularized CNN-based One-Class Classifier
Generally, an active authentication problem is modelled as a one class classification problem due to the unavailability of data from the impostor users.
Deep CNN-based Multi-task Learning for Open-Set Recognition
We propose a novel deep convolutional neural network (CNN) based multi-task learning approach for open-set visual recognition.
C2AE: Class Conditioned Auto-Encoder for Open-set Recognition
It refers to the problem of identifying the unknown classes during testing, while maintaining performance on the known classes.
Prior-based Domain Adaptive Object Detection for Hazy and Rainy Conditions
Adverse weather conditions such as haze and rain corrupt the quality of captured images, which cause detection networks trained on clean images to perform poorly on these images.
Anomaly Detection-Based Unknown Face Presentation Attack Detection
Anomaly detection-based spoof attack detection is a recent development in face Presentation Attack Detection (fPAD), where a spoof detector is learned using only non-attacked images of users.
One-Class Classification: A Survey
One-Class Classification (OCC) is a special case of multi-class classification, where data observed during training is from a single positive class.
Medical Transformer: Gated Axial-Attention for Medical Image Segmentation
The proposed Medical Transformer (MedT) is evaluated on three different medical image segmentation datasets and it is shown that it achieves better performance than the convolutional and other related transformer-based architectures.
Ranked #1 on
Medical Image Segmentation
on Brain US
MeGA-CDA: Memory Guided Attention for Category-Aware Unsupervised Domain Adaptive Object Detection
Existing approaches for unsupervised domain adaptive object detection perform feature alignment via adversarial training.
Federated Learning-based Active Authentication on Mobile Devices
Using FL/SL frameworks, we can alleviate the lack of negative data problem by training a user authentication model over multiple user data distributed across devices.
Unsupervised Domain Adaptation of Object Detectors: A Survey
Recent advances in deep learning have led to the development of accurate and efficient models for various computer vision applications such as classification, segmentation, and detection.
Image Fusion Transformer
Furthermore, we show the effectiveness of the proposed ST fusion strategy with an ablation analysis.
Adversarially Robust One-class Novelty Detection
To this end, we propose a defense strategy that manipulates the latent space of novelty detectors to improve the robustness against adversarial examples.
Instance Relation Graph Guided Source-Free Domain Adaptive Object Detection
The Source-Free Domain Adaptation (SFDA) setting aims to alleviate these concerns by adapting a source-trained model for the target domain without requiring access to the source data.
Towards Online Domain Adaptive Object Detection
To the best of our knowledge, this is the first work to address online and offline adaptation settings for object detection.
Spatio-Temporal Pixel-Level Contrastive Learning-based Source-Free Domain Adaptation for Video Semantic Segmentation
Unsupervised Domain Adaptation (UDA) of semantic segmentation transfers labeled source knowledge to an unlabeled target domain by relying on accessing both the source and target data.
Utilizing Patch-level Category Activation Patterns for Multiple Class Novelty Detection
For any recognition system, the ability to identify novel class samples during inference is an important aspect of the system’s robustness.
Multiple Class Novelty Detection Under Data Distribution Shift
To this end, we consider the problem of multiple class novelty detection under dataset distribution shift to improve the novelty detection performance.
