Thomas Mensink | DeepAI

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∙ 06/15/2023

Encyclopedic VQA: Visual questions about detailed properties of fine-grained categories

We propose Encyclopedic-VQA, a large scale visual question answering (VQ...

0 Thomas Mensink, et al. ∙

∙ 05/17/2023

Infinite Class Mixup

Mixup is a widely adopted strategy for training deep networks, where add...

0 Thomas Mensink, et al. ∙

∙ 06/09/2022

The Missing Link: Finding label relations across datasets

Computer Vision is driven by the many datasets which can be used for tra...

0 Jasper Uijlings, et al. ∙

∙ 04/04/2022

How stable are Transferability Metrics evaluations?

Transferability metrics is a maturing field with increasing interest, wh...

0 Andrea Agostinelli, et al. ∙

∙ 11/25/2021

Transferability Metrics for Selecting Source Model Ensembles

We address the problem of ensemble selection in transfer learning: Given...

0 Andrea Agostinelli, et al. ∙

∙ 11/24/2021

Transferability Estimation using Bhattacharyya Class Separability

Transfer learning has become a popular method for leveraging pre-trained...

0 Michal Pándy, et al. ∙

∙ 03/24/2021

Factors of Influence for Transfer Learning across Diverse Appearance Domains and Task Types

Transfer learning enables to re-use knowledge learned on a source task t...

12 Thomas Mensink, et al. ∙

∙ 11/09/2020

EDEN: Multimodal Synthetic Dataset of Enclosed GarDEN Scenes

Multimodal large-scale datasets for outdoor scenes are mostly designed f...

16 Hoang-An Le, et al. ∙

∙ 09/17/2020

Novel View Synthesis from Single Images via Point Cloud Transformation

In this paper the argument is made that for true novel view synthesis of...

0 Hoang-An Le, et al. ∙

∙ 09/03/2020

Multi-Loss Weighting with Coefficient of Variations

Many interesting tasks in machine learning and computer vision are learn...

0 Rick Groenendijk, et al. ∙

∙ 08/14/2020

PointMixup: Augmentation for Point Clouds

This paper introduces data augmentation for point clouds by interpolatio...

10 Yunlu Chen, et al. ∙

∙ 06/23/2020

Post-hoc Calibration of Neural Networks

Calibration of neural networks is a critical aspect to consider when inc...

23 Amir Rahimi, et al. ∙

∙ 06/23/2020

Calibration of Neural Networks using Splines

Calibrating neural networks is of utmost importance when employing them ...

46 Kartik Gupta, et al. ∙

∙ 05/20/2020

Range Conditioned Dilated Convolutions for Scale Invariant 3D Object Detection

This paper presents a novel 3D object detection framework that processes...

4 Alex Bewley, et al. ∙

∙ 10/29/2019

On the Benefit of Adversarial Training for Monocular Depth Estimation

In this paper we address the benefit of adding adversarial training to t...

11 Rick Groenendijk, et al. ∙

∙ 10/03/2019

3D Neighborhood Convolution: Learning Depth-Aware Features for RGB-D and RGB Semantic Segmentation

A key challenge for RGB-D segmentation is how to effectively incorporate...

30 Yunlu Chen, et al. ∙

∙ 12/05/2018

Unsupervised Generation of Optical Flow Datasets from Videos in the Wild

Dense optical flow ground truth of non-rigid motion for real-world image...

6 Hoang-An Le, et al. ∙

∙ 07/19/2018

Three for one and one for three: Flow, Segmentation, and Surface Normals

Optical flow, semantic segmentation, and surface normals represent diffe...

0 Hoang-An Le, et al. ∙

∙ 04/16/2018

IterGANs: Iterative GANs to Learn and Control 3D Object Transformation

We are interested in learning visual representations which allow for 3D ...

0 Ysbrand Galama, et al. ∙

∙ 01/30/2018

The New Modality: Emoji Challenges in Prediction, Anticipation, and Retrieval

Over the past decade, emoji have emerged as a new and widespread form of...

0 Spencer Cappallo, et al. ∙

∙ 04/08/2016

Online Open World Recognition

As we enter into the big data age and an avalanche of images have become...

0 Rocco De Rosa, et al. ∙

∙ 10/06/2015

Active Transfer Learning with Zero-Shot Priors: Reusing Past Datasets for Future Tasks

How can we reuse existing knowledge, in the form of available datasets, ...

0 Efstratios Gavves, et al. ∙

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