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Hyperspectral Image Super-Resolution with Spectral Mixup and Heterogeneous Datasets
This work studies Hyperspectral image (HSI) super-resolution (SR). HSI S...
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Three Ways to Improve Semantic Segmentation with Self-Supervised Depth Estimation
Training deep networks for semantic segmentation requires large amounts ...
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mDALU: Multi-Source Domain Adaptation and Label Unification with Partial Datasets
Object recognition advances very rapidly these days. One challenge is to...
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Cluster, Split, Fuse, and Update: Meta-Learning for Open Compound Domain Adaptive Semantic Segmentation
Open compound domain adaptation (OCDA) is a domain adaptation setting, w...
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Depth Estimation from Monocular Images and Sparse Radar Data
In this paper, we explore the possibility of achieving a more accurate d...
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Improving Point Cloud Semantic Segmentation by Learning 3D Object Proposal Generation
Point cloud semantic segmentation plays an essential role in autonomous ...
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Weakly Supervised 3D Object Detection from Lidar Point Cloud
It is laborious to manually label point cloud data for training high-qua...
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Off-Policy Reinforcement Learning for Efficient and Effective GAN Architecture Search
In this paper, we introduce a new reinforcement learning (RL) based neur...
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T-Basis: a Compact Representation for Neural Networks
We introduce T-Basis, a novel concept for a compact representation of a ...
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Learning Accurate and Human-Like Driving using Semantic Maps and Attention
This paper investigates how end-to-end driving models can be improved to...
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Analogical Image Translation for Fog Generation
Image-to-image translation is to map images from a given style to anothe...
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Map-Guided Curriculum Domain Adaptation and Uncertainty-Aware Evaluation for Semantic Nighttime Image Segmentation
We address the problem of semantic nighttime image segmentation and impr...
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Action Sequence Predictions of Vehicles in Urban Environments using Map and Social Context
This work studies the problem of predicting the sequence of future actio...
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Revisiting Multi-Task Learning in the Deep Learning Era
Despite the recent progress in deep learning, most approaches still go f...
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Quantifying Data Augmentation for LiDAR based 3D Object Detection
In this work, we shed light on different data augmentation techniques co...
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Semantic Object Prediction and Spatial Sound Super-Resolution with Binaural Sounds
Humans can robustly recognize and localize objects by integrating visual...
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Matching Neuromorphic Events and Color Images via Adversarial Learning
The event camera has appealing properties: high dynamic range, low laten...
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Don't Forget The Past: Recurrent Depth Estimation from Monocular Video
Autonomous cars need continuously updated depth information. Thus far, t...
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Self-supervised Object Motion and Depth Estimation from Video
We present a self-supervised learning framework to estimate the individu...
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Semantic Understanding of Foggy Scenes with Purely Synthetic Data
This work addresses the problem of semantic scene understanding under fo...
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Talk2Nav: Long-Range Vision-and-Language Navigation in Cities
Autonomous driving models often consider the goal as fixed at the start ...
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Texture Underfitting for Domain Adaptation
Comprehensive semantic segmentation is one of the key components for rob...
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Learning a Curve Guardian for Motorcycles
Up to 17 through a curve and the main cause of curve accidents can be at...
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Gated CRF Loss for Weakly Supervised Semantic Image Segmentation
State-of-the-art approaches for semantic segmentation rely on deep convo...
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Learning Accurate, Comfortable and Human-like Driving
Autonomous vehicles are more likely to be accepted if they drive accurat...
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Real-time 3D Traffic Cone Detection for Autonomous Driving
Considerable progress has been made in semantic scene understanding of r...
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Semantic Nighttime Image Segmentation with Synthetic Stylized Data, Gradual Adaptation and Uncertainty-Aware Evaluation
This work addresses the problem of semantic segmentation of nighttime im...
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Curriculum Model Adaptation with Synthetic and Real Data for Semantic Foggy Scene Understanding
This work addresses the problem of semantic scene understanding under fo...
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Dark Model Adaptation: Semantic Image Segmentation from Daytime to Nighttime
This work addresses the problem of semantic image segmentation of nightt...
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Model Adaptation with Synthetic and Real Data for Semantic Dense Foggy Scene Understanding
This work addresses the problem of semantic scene understanding under de...
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Unified Hypersphere Embedding for Speaker Recognition
Incremental improvements in accuracy of Convolutional Neural Networks ar...
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Failure Prediction for Autonomous Driving
The primary focus of autonomous driving research is to improve driving a...
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Ensemble Manifold Segmentation for Model Distillation and Semi-supervised Learning
Manifold theory has been the central concept of many learning methods. H...
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Learning Driving Models with a Surround-View Camera System and a Route Planner
For people, having a rear-view mirror and side-view mirrors is vital for...
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Domain Adaptive Faster R-CNN for Object Detection in the Wild
Object detection typically assumes that training and test data are drawn...
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Learning the Synthesizability of Dynamic Texture Samples
A dynamic texture (DT) refers to a sequence of images that exhibit tempo...
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Object Referring in Videos with Language and Human Gaze
We investigate the problem of object referring (OR) i.e. to localize a t...
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Object Referring in Visual Scene with Spoken Language
Object referring has important applications, especially for human-machin...
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Semantic Foggy Scene Understanding with Synthetic Data
This work addresses the problem of semantic foggy scene understanding (S...
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Deep Domain Adaptation by Geodesic Distance Minimization
In this paper, we propose a new approach called Deep LogCORAL for unsupe...
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Speech-Based Visual Question Answering
This paper introduces speech-based visual question answering (VQA), the ...
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PathTrack: Fast Trajectory Annotation with Path Supervision
Progress in Multiple Object Tracking (MOT) has been historically limited...
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Fast Optical Flow using Dense Inverse Search
Most recent works in optical flow extraction focus on the accuracy and n...
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Unsupervised High-level Feature Learning by Ensemble Projection for Semi-supervised Image Classification and Image Clustering
This paper investigates the problem of image classification with limited...
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