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Efficient Conditional GAN Transfer with Knowledge Propagation across Classes
Generative adversarial networks (GANs) have shown impressive results in ...
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Unsupervised Semantic Segmentation by Contrasting Object Mask Proposals
Being able to learn dense semantic representations of images without sup...
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Exploring Cross-Image Pixel Contrast for Semantic Segmentation
Current semantic segmentation methods focus only on mining "local" conte...
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Deep Burst Super-Resolution
While single-image super-resolution (SISR) has attracted substantial int...
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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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Trilevel Neural Architecture Search for Efficient Single Image Super-Resolution
This paper proposes a trilevel neural architecture search (NAS) method f...
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DeFlow: Learning Complex Image Degradations from Unpaired Data with Conditional Flows
The difficulty of obtaining paired data remains a major bottleneck for l...
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Generating Masks from Boxes by Mining Spatio-Temporal Consistencies in Videos
Segmenting objects in videos is a fundamental computer vision task. The ...
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Local Memory Attention for Fast Video Semantic Segmentation
We propose a novel neural network module that transforms an existing sin...
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Learning Accurate Dense Correspondences and When to Trust Them
Establishing dense correspondences between a pair of images is an import...
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Unsupervised Monocular Depth Reconstruction of Non-Rigid Scenes
Monocular depth reconstruction of complex and dynamic scenes is a highly...
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An Efficient Recurrent Adversarial Framework for Unsupervised Real-Time Video Enhancement
Video enhancement is a challenging problem, more than that of stills, ma...
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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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CompositeTasking: Understanding Images by Spatial Composition of Tasks
We define the concept of CompositeTasking as the fusion of multiple, spa...
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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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Scaling Semantic Segmentation Beyond 1K Classes on a Single GPU
The state-of-the-art object detection and image classification methods c...
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Uncalibrated Neural Inverse Rendering for Photometric Stereo of General Surfaces
This paper presents an uncalibrated deep neural network framework for th...
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Quantifying Aleatoric and Epistemic Uncertainty Using Density Estimation in Latent Space
The distribution of a neural network's latent representations has been s...
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Understanding Bird's-Eye View Semantic HD-Maps Using an Onboard Monocular Camera
Autonomous navigation requires scene understanding of the action-space t...
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Learning from Simulation, Racing in Reality
We present a reinforcement learning-based solution to autonomously race ...
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Zero-Pair Image to Image Translation using Domain Conditional Normalization
In this paper, we propose an approach based on domain conditional normal...
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Neural Architecture Search of SPD Manifold Networks
In this paper, we propose a new neural architecture search (NAS) problem...
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Self-Supervised Shadow Removal
Shadow removal is an important computer vision task aiming at the detect...
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Facial Emotion Recognition with Noisy Multi-task Annotations
Human emotions can be inferred from facial expressions. However, the ann...
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LID 2020: The Learning from Imperfect Data Challenge Results
Learning from imperfect data becomes an issue in many industrial applica...
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Self-Supervised Ranking for Representation Learning
We present a new framework for self-supervised representation learning b...
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SMILE: Semantically-guided Multi-attribute Image and Layout Editing
Attribute image manipulation has been a very active topic since the intr...
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Few-Shot Classification By Few-Iteration Meta-Learning
Learning in a low-data regime from only a few labeled examples is an imp...
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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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Commands 4 Autonomous Vehicles (C4AV) Workshop Summary
The task of visual grounding requires locating the most relevant region ...
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GOCor: Bringing Globally Optimized Correspondence Volumes into Your Neural Network
The feature correlation layer serves as a key neural network module in n...
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Plug-and-Play Image Restoration with Deep Denoiser Prior
Recent works on plug-and-play image restoration have shown that a denois...
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Learning Condition Invariant Features for Retrieval-Based Localization from 1M Images
Image features for retrieval-based localization must be invariant to dyn...
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Automated Search for Resource-Efficient Branched Multi-Task Networks
The multi-modal nature of many vision problems calls for neural network ...
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Neural Architecture Search as Sparse Supernet
This paper aims at enlarging the problem of Neural Architecture Search f...
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Reparameterizing Convolutions for Incremental Multi-Task Learning without Task Interference
Multi-task networks are commonly utilized to alleviate the need for a la...
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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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Video Object Segmentation with Episodic Graph Memory Networks
How to make a segmentation model to efficiently adapt to a specific vide...
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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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Modelling the Distribution of 3D Brain MRI using a 2D Slice VAE
Probabilistic modelling has been an essential tool in medical image anal...
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Self-Calibration Supported Robust Projective Structure-from-Motion
Typical Structure-from-Motion (SfM) pipelines rely on finding correspond...
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Mining Cross-Image Semantics for Weakly Supervised Semantic Segmentation
This paper studies the problem of learning semantic segmentation from im...
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The Heterogeneity Hypothesis: Finding Layer-Wise Dissimilated Network Architecture
In this paper, we tackle the problem of convolutional neural network des...
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OpenDVC: An Open Source Implementation of the DVC Video Compression Method
We introduce an open source Tensorflow implementation of the Deep Video ...
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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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SRFlow: Learning the Super-Resolution Space with Normalizing Flow
Super-resolution is an ill-posed problem, since it allows for multiple p...
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Learning for Video Compression with Recurrent Auto-Encoder and Recurrent Probability Model
The past few years have witnessed increasing interests in applying deep ...
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