activity
20162023
most citedOn the Benefit of Adversarial Training for Monocular Depth Estimation

29 citations · 36 across the 5 of their papers we have counts for

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12 papers · 1 filter

cs.CV2023

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

Thomas Mensink, Jasper Uijlings, Lluis Castrejon +6

We propose Encyclopedic-VQA, a large scale visual question answering (VQA) dataset featuring visual questions about detailed properties of fine-grained categories and instances. It…

cs.CV2023

Infinite Class Mixup

Thomas Mensink, Pascal Mettes

Mixup is a widely adopted strategy for training deep networks, where additional samples are augmented by interpolating inputs and labels of training pairs. Mixup has shown to impro…

cs.CV2023118 cited

Scaling Vision Transformers to 22 Billion Parameters

Mostafa Dehghani, Josip Djolonga, Basil Mustafa +39

The scaling of Transformers has driven breakthrough capabilities for language models. At present, the largest large language models (LLMs) contain upwards of 100B parameters. Visio…

cs.CV20201 cited

EDEN: Multimodal Synthetic Dataset of Enclosed GarDEN Scenes

Hoang-An Le, Thomas Mensink, Partha Das +2

Multimodal large-scale datasets for outdoor scenes are mostly designed for urban driving problems. The scenes are highly structured and semantically different from scenarios seen i…

cs.CV2020

Novel View Synthesis from Single Images via Point Cloud Transformation

Hoang-An Le, Thomas Mensink, Partha Das +1

In this paper the argument is made that for true novel view synthesis of objects, where the object can be synthesized from any viewpoint, an explicit 3D shape representation isdesi…

cs.CV2020

Multi-Loss Weighting with Coefficient of Variations

Rick Groenendijk, Sezer Karaoglu, Theo Gevers +1

Many interesting tasks in machine learning and computer vision are learned by optimising an objective function defined as a weighted linear combination of multiple losses. The fina…