Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Shevchenko, Violetta"'
Autor:
Saratchandran, Hemanth, Ramasinghe, Sameera, Shevchenko, Violetta, Long, Alexander, Lucey, Simon
Implicit Neural Representations (INRs) have gained popularity for encoding signals as compact, differentiable entities. While commonly using techniques like Fourier positional encodings or non-traditional activation functions (e.g., Gaussian, sinusoi
Externí odkaz:
http://arxiv.org/abs/2402.05427
Modelling dynamical systems is an integral component for understanding the natural world. To this end, neural networks are becoming an increasingly popular candidate owing to their ability to learn complex functions from large amounts of data. Despit
Externí odkaz:
http://arxiv.org/abs/2303.05728
Reasoning the 3D structure of a non-rigid dynamic scene from a single moving camera is an under-constrained problem. Inspired by the remarkable progress of neural radiance fields (NeRFs) in photo-realistic novel view synthesis of static scenes, exten
Externí odkaz:
http://arxiv.org/abs/2302.13543
The availability of clean and diverse labeled data is a major roadblock for training models on complex tasks such as visual question answering (VQA). The extensive work on large vision-and-language models has shown that self-supervised learning is ef
Externí odkaz:
http://arxiv.org/abs/2206.14355
The limits of applicability of vision-and-language models are defined by the coverage of their training data. Tasks like vision question answering (VQA) often require commonsense and factual information beyond what can be learned from task-specific d
Externí odkaz:
http://arxiv.org/abs/2101.06013
We present a novel mechanism to embed prior knowledge in a model for visual question answering. The open-set nature of the task is at odds with the ubiquitous approach of training of a fixed classifier. We show how to exploit additional information p
Externí odkaz:
http://arxiv.org/abs/2005.01239