research
∙
09/15/2023
Leveraging the Power of Data Augmentation for Transformer-based Tracking
Due to long-distance correlation and powerful pretrained models, transfo...
share
research
∙
09/12/2023
IBAFormer: Intra-batch Attention Transformer for Domain Generalized Semantic Segmentation
Domain generalized semantic segmentation (DGSS) is a critical yet challe...
research
∙
08/16/2023
DeDoDe: Detect, Don't Describe – Describe, Don't Detect for Local Feature Matching
Keypoint detection is a pivotal step in 3D reconstruction, whereby sets ...
research
∙
07/04/2023
Augment Features Beyond Color for Domain Generalized Segmentation
Domain generalized semantic segmentation (DGSS) is an essential but high...
research
∙
06/29/2023
End-to-end Reinforcement Learning for Online Coverage Path Planning in Unknown Environments
Coverage path planning is the problem of finding the shortest path that ...
research
∙
06/07/2023
Align, Distill, and Augment Everything All at Once for Imbalanced Semi-Supervised Learning
Addressing the class imbalance in long-tailed semi-supervised learning (...
research
∙
05/27/2023
GMSF: Global Matching Scene Flow
We tackle the task of scene flow estimation from point clouds. Given a s...
research
∙
05/24/2023
Deep Equivariant Hyperspheres
This paper presents an approach to learning nD features equivariant unde...
research
∙
05/24/2023
RoMa: Revisiting Robust Losses for Dense Feature Matching
Dense feature matching is an important computer vision task that involve...
research
∙
04/05/2023
High-fidelity Pseudo-labels for Boosting Weakly-Supervised Segmentation
The task of image-level weakly-supervised semantic segmentation (WSSS) h...
research
∙
01/25/2023
Flow-guided Semi-supervised Video Object Segmentation
We propose an optical flow-guided approach for semi-supervised video obj...
research
∙
01/21/2023
Raw or Cooked? Object Detection on RAW Images
Images fed to a deep neural network have in general undergone several ha...
research
∙
12/06/2022
Evidential Deep Learning for Class-Incremental Semantic Segmentation
Class-Incremental Learning is a challenging problem in machine learning ...
research
∙
11/26/2022
TetraSphere: A Neural Descriptor for O(3)-Invariant Point Cloud Classification
Rotation invariance is an important requirement for the analysis of 3D p...
research
∙
06/10/2022
Balanced Product of Experts for Long-Tailed Recognition
Many real-world recognition problems suffer from an imbalanced or long-t...
research
∙
03/24/2022
Video Instance Segmentation via Multi-scale Spatio-temporal Split Attention Transformer
State-of-the-art transformer-based video instance segmentation (VIS) app...
research
∙
03/23/2022
Activation-Based Sampling for Pixel- to Image-Level Aggregation in Weakly-Supervised Segmentation
Classification networks can be used to localize and segment objects in i...
research
∙
03/02/2022
Visual Feature Encoding for GNNs on Road Networks
In this work, we present a novel approach to learning an encoding of vis...
research
∙
02/01/2022
Deep Kernelized Dense Geometric Matching
Dense geometric matching is a challenging computer vision task, requirin...
research
∙
12/20/2021
Learning to integrate vision data into road network data
Road networks are the core infrastructure for connected and autonomous v...
research
∙
12/06/2021
DoodleFormer: Creative Sketch Drawing with Transformers
Creative sketching or doodling is an expressive activity, where imaginat...
research
∙
12/06/2021
Visual Object Tracking with Discriminative Filters and Siamese Networks: A Survey and Outlook
Accurate and robust visual object tracking is one of the most challengin...
research
∙
10/07/2021
Dense Gaussian Processes for Few-Shot Segmentation
Few-shot segmentation is a challenging dense prediction task, which enta...
research
∙
06/15/2021
Is this Harmful? Learning to Predict Harmfulness Ratings from Video
Automatically identifying harmful content in video is an important task ...
research
∙
04/08/2021
A Bayesian Approach to Reinforcement Learning of Vision-Based Vehicular Control
In this paper, we present a state-of-the-art reinforcement learning meth...
research
∙
03/30/2021
Deep Gaussian Processes for Few-Shot Segmentation
Few-shot segmentation is a challenging task, requiring the extraction of...
research
∙
02/13/2021
Normalized Convolution Upsampling for Refined Optical Flow Estimation
Optical flow is a regression task where convolutional neural networks (C...
research
∙
12/07/2020
Learning Video Instance Segmentation with Recurrent Graph Neural Networks
Most existing approaches to video instance segmentation comprise multipl...
research
∙
06/11/2020
Embed Me If You Can: A Geometric Perceptron
Solving geometric tasks using machine learning is a challenging problem....
research
∙
06/05/2020
Uncertainty-Aware CNNs for Depth Completion: Uncertainty from Beginning to End
The focus in deep learning research has been mostly to push the limits o...
research
∙
03/25/2020
Learning What to Learn for Video Object Segmentation
Video object segmentation (VOS) is a highly challenging problem, since t...
research
∙
02/27/2020
Learning Fast and Robust Target Models for Video Object Segmentation
Video object segmentation (VOS) is a highly challenging problem since th...
research
∙
09/19/2019
Flexible Disaster Response of Tomorrow -- Final Presentation and Evaluation of the CENTAURO System
Mobile manipulation robots have high potential to support rescue forces ...
research
∙
05/27/2019
Unsupervised Learning of Anomaly Detection from Contaminated Image Data using Simultaneous Encoder Training
Anomaly detection in high-dimensional data, such as images, is a challen...
research
∙
04/18/2019
Discriminative Online Learning for Fast Video Object Segmentation
We address the highly challenging problem of video object segmentation. ...
research
∙
11/28/2018
A Generative Appearance Model for End-to-end Video Object Segmentation
One of the fundamental challenges in video object segmentation is to fin...
research
∙
11/19/2018
ATOM: Accurate Tracking by Overlap Maximization
While recent years have witnessed astonishing improvements in visual tra...
research
∙
11/05/2018
Confidence Propagation through CNNs for Guided Sparse Depth Regression
Generally, convolutional neural networks (CNNs) process data on a regula...
research
∙
05/30/2018
Propagating Confidences through CNNs for Sparse Data Regression
In most computer vision applications, convolutional neural networks (CNN...
research
∙
04/18/2018
Unveiling the Power of Deep Tracking
In the field of generic object tracking numerous attempts have been made...
research
∙
04/04/2018
Density Adaptive Point Set Registration
Probabilistic methods for point set registration have demonstrated compe...
research
∙
06/09/2017
DCCO: Towards Deformable Continuous Convolution Operators
Discriminative Correlation Filter (DCF) based methods have shown competi...
research
∙
05/09/2017
Deep Projective 3D Semantic Segmentation
Semantic segmentation of 3D point clouds is a challenging problem with n...
research
∙
12/20/2016
Deep Motion Features for Visual Tracking
Robust visual tracking is a challenging computer vision problem, with ma...
research
∙
12/14/2016
Scale Coding Bag of Deep Features for Human Attribute and Action Recognition
Most approaches to human attribute and action recognition in still image...
research
∙
11/28/2016
ECO: Efficient Convolution Operators for Tracking
In recent years, Discriminative Correlation Filter (DCF) based methods h...
research
∙
09/20/2016
Discriminative Scale Space Tracking
Accurate scale estimation of a target is a challenging research problem ...
research
∙
09/20/2016
Adaptive Decontamination of the Training Set: A Unified Formulation for Discriminative Visual Tracking
Tracking-by-detection methods have demonstrated competitive performance ...
research
∙
08/19/2016
Learning Spatially Regularized Correlation Filters for Visual Tracking
Robust and accurate visual tracking is one of the most challenging compu...
research
∙
08/12/2016
