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Found 16 papers, 2 papers with code
Continual Learning for Image-Based Camera Localization
For several emerging technologies such as augmented reality, autonomous driving and robotics, visual localization is a critical component.
MOFA: A Model Simplification Roadmap for Image Restoration on Mobile Devices
Extensive experiments demonstrate that our approach decreases runtime by up to 13% and reduces the number of parameters by up to 23%, while increasing PSNR and SSIM on several image restoration datasets.
Full-Frame Scene Coordinate Regression for Image-Based Localization
In this paper, instead of in a patch-based manner, we propose to perform the scene coordinate regression in a full-frame manner to make the computation efficient at test time and, more importantly, to add more global context to the regression process to improve the robustness.
Scene Coordinate Regression with Angle-Based Reprojection Loss for Camera Relocalization
Image-based camera relocalization is an important problem in computer vision and robotics.
Hierarchical Scene Coordinate Classification and Regression for Visual Localization
In this work, we present a new hierarchical scene coordinate network to predict pixel scene coordinates in a coarse-to-fine manner from a single RGB image.
Can You Trust Your Pose? Confidence Estimation in Visual Localization
Camera pose estimation in large-scale environments is still an open question and, despite recent promising results, it may still fail in some situations.
Infrastructure Assisted Constrained Connected Automated Vehicle Trajectory Optimization on Curved Roads: A Spatial Formulation on a Curvilinear Coordinate
This paper presents an infrastructure assisted constrained connected automated vehicles (CAVs) trajectory optimization method on curved roads.
Digging Into Self-Supervised Learning of Feature Descriptors
Fully-supervised CNN-based approaches for learning local image descriptors have shown remarkable results in a wide range of geometric tasks.
Your "Attention" Deserves Attention: A Self-Diversified Multi-Channel Attention for Facial Action Analysis
In this paper, we propose a compact model to enhance the representational and focusing power of neural attention maps and learn the "inter-attention" correlation for refined attention maps, which we term the "Self-Diversified Multi-Channel Attention Network (SMA-Net)".
An EEG-Based Multi-Modal Emotion Database with Both Posed and Authentic Facial Actions for Emotion Analysis
Emotion is an experience associated with a particular pattern of physiological activity along with different physiological, behavioral and cognitive changes.
Knowledge-Spreader: Learning Facial Action Unit Dynamics with Extremely Limited Labels
Recent studies on the automatic detection of facial action unit (AU) have extensively relied on large-sized annotations.
Multimodal Channel-Mixing: Channel and Spatial Masked AutoEncoder on Facial Action Unit Detection
Recent studies have focused on utilizing multi-modal data to develop robust models for facial Action Unit (AU) detection.
Weakly-Supervised Text-driven Contrastive Learning for Facial Behavior Understanding
This is because such pairs inevitably encode the subject-ID information, and the randomly constructed pairs may push similar facial images away due to the limited number of subjects in facial behavior datasets.
HSCNet++: Hierarchical Scene Coordinate Classification and Regression for Visual Localization with Transformer
In this work, we present a new hierarchical scene coordinate network to predict pixel scene coordinates in a coarse-to-fine manner from a single RGB image.
ReactioNet: Learning High-Order Facial Behavior from Universal Stimulus-Reaction by Dyadic Relation Reasoning
Diverse visual stimuli can evoke various human affective states, which are usually manifested in an individual's muscular actions and facial expressions.
Knowledge-Spreader: Learning Semi-Supervised Facial Action Dynamics by Consistifying Knowledge Granularity
By formulating SSL as a Progressive Knowledge Distillation (PKD) problem, we aim to infer cross-domain information, specifically from spatial to temporal domains, by consistifying knowledge granularity within Teacher-Students Network.
