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Found 14 papers, 7 papers with code
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.
Deep Learning-based Multi-Organ CT Segmentation with Adversarial Data Augmentation
In this work, we propose an adversarial attack-based data augmentation method to improve the deep-learning-based segmentation algorithm for the delineation of Organs-At-Risk (OAR) in abdominal Computed Tomography (CT) to facilitate radiation therapy.
Learning Feature Decomposition for Domain Adaptive Monocular Depth Estimation
In this paper, we propose a novel UDA method for MDE, referred to as Learning Feature Decomposition for Adaptation (LFDA), which learns to decompose the feature space into content and style components.
Exploring Adversarially Robust Training for Unsupervised Domain Adaptation
Adversarial Training (AT) has been considered to be the most successful adversarial defense approach.
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.
Error Diffusion Halftoning Against Adversarial Examples
In this paper, we propose a new image transformation defense based on error diffusion halftoning, and combine it with adversarial training to defend against adversarial examples.
Overcomplete Representations Against Adversarial Videos
Adversarial robustness of deep neural networks is an extensively studied problem in the literature and various methods have been proposed to defend against adversarial images.
MultAV: Multiplicative Adversarial Videos
In this paper, we propose a novel attack method against video recognition models, Multiplicative Adversarial Videos (MultAV), which imposes perturbation on video data by multiplication.
Defending Against Multiple and Unforeseen Adversarial Videos
With a multiple BN structure, each BN brach is responsible for learning the distribution of a single perturbation type and thus provides more precise distribution estimations.
Exploring Semantic Segmentation on the DCT Representation
The proposed method has an accuracy close to the RGB model at about the same network complexity.
Ranked #81 on
Semantic Segmentation
on Cityscapes val
Multi-Class Lane Semantic Segmentation using Efficient Convolutional Networks
Several studies leverage a semantic segmentation network to extract robust lane features, but few of them can distinguish different types of lanes.
Incorporating Luminance, Depth and Color Information by a Fusion-based Network for Semantic Segmentation
It includes a sub-network to process depth maps and employs luminance images to assist the depth information in processes.
Ranked #8 on
Real-Time Semantic Segmentation
on Cityscapes val
Efficient Dense Modules of Asymmetric Convolution for Real-Time Semantic Segmentation
Real-time semantic segmentation plays an important role in practical applications such as self-driving and robots.
Ranked #10 on
Semantic Segmentation
on CamVid
Efficient Road Lane Marking Detection with Deep Learning
Lane mark detection is an important element in the road scene analysis for Advanced Driver Assistant System (ADAS).
Ranked #14 on
Semantic Segmentation
on CamVid
