Search Results for author:
Found 18 papers, 12 papers with code
PØDA: Prompt-driven Zero-shot Domain Adaptation
Domain adaptation has been vastly investigated in computer vision but still requires access to target images at train time, which might be intractable in some uncommon conditions.
Prompt-driven Zero-shot Domain Adaptation
Semantic Segmentation
+2
Cross-task Attention Mechanism for Dense Multi-task Learning
Multi-task learning has recently become a promising solution for a comprehensive understanding of complex scenes.
Ranked #58 on
Semantic Segmentation
on NYU Depth v2
Multi-Head Distillation for Continual Unsupervised Domain Adaptation in Semantic Segmentation
Unsupervised Domain Adaptation (UDA) is a transfer learning task which aims at training on an unlabeled target domain by leveraging a labeled source domain.
CSG0: Continual Urban Scene Generation with Zero Forgetting
With the rapid advances in generative adversarial networks (GANs), the visual quality of synthesised scenes keeps improving, including for complex urban scenes with applications to automated driving.
Multi-Target Adversarial Frameworks for Domain Adaptation in Semantic Segmentation
In this work, we address the task of unsupervised domain adaptation (UDA) for semantic segmentation in presence of multiple target domains: The objective is to train a single model that can handle all these domains at test time.
Semantic Palette: Guiding Scene Generation with Class Proportions
Despite the recent progress of generative adversarial networks (GANs) at synthesizing photo-realistic images, producing complex urban scenes remains a challenging problem.
Cross-modal Learning for Domain Adaptation in 3D Semantic Segmentation
Domain adaptation is an important task to enable learning when labels are scarce.
Confidence Estimation via Auxiliary Models
In this paper, we introduce a novel target criterion for model confidence, namely the true class probability (TCP).
VRUNet: Multi-Task Learning Model for Intent Prediction of Vulnerable Road Users
In this paper we propose a multi-task learning model to predict pedestrian actions, crossing intent and forecast their future path from video sequences.
ESL: Entropy-guided Self-supervised Learning for Domain Adaptation in Semantic Segmentation
While fully-supervised deep learning yields good models for urban scene semantic segmentation, these models struggle to generalize to new environments with different lighting or weather conditions for instance.
Handling new target classes in semantic segmentation with domain adaptation
In this work, we define and address a novel domain adaptation (DA) problem in semantic scene segmentation, where the target domain not only exhibits a data distribution shift w. r. t.
xMUDA: Cross-Modal Unsupervised Domain Adaptation for 3D Semantic Segmentation
In this work, we explore how to learn from multi-modality and propose cross-modal UDA (xMUDA) where we assume the presence of 2D images and 3D point clouds for 3D semantic segmentation.
Zero-Shot Semantic Segmentation
Semantic segmentation models are limited in their ability to scale to large numbers of object classes.
Ranked #1 on
Zero-Shot Learning
on PASCAL Context
DADA: Depth-aware Domain Adaptation in Semantic Segmentation
As a result, the performance of the trained semantic segmentation model on the target domain is boosted.
Ranked #17 on
Image-to-Image Translation
on SYNTHIA-to-Cityscapes
Tube-CNN: Modeling temporal evolution of appearance for object detection in video
Our goal in this paper is to learn discriminative models for the temporal evolution of object appearance and to use such models for object detection.
ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation
Semantic segmentation is a key problem for many computer vision tasks.
Ranked #19 on
Image-to-Image Translation
on GTAV-to-Cityscapes Labels
Memory Warps for Learning Long-Term Online Video Representations
This paper proposes a novel memory-based online video representation that is efficient, accurate and predictive.
Context-aware CNNs for person head detection
First, we leverage person-scene relations and propose a Global CNN model trained to predict positions and scales of heads directly from the full image.
