Zobrazeno 1 - 10
of 64
pro vyhledávání: '"Meyer, Simone"'
Autor:
Lingenberg, Tobias, Reuter, Markus, Sudhakaran, Gopika, Gojny, Dominik, Roth, Stefan, Schaub-Meyer, Simone
Simple data augmentation techniques, such as rotations and flips, are widely used to enhance the generalization power of computer vision models. However, these techniques often fail to modify high-level semantic attributes of a class. To address this
Externí odkaz:
http://arxiv.org/abs/2408.14584
Slot attention aims to decompose an input image into a set of meaningful object files (slots). These latent object representations enable various downstream tasks. Yet, these slots often bind to object parts, not objects themselves, especially for re
Externí odkaz:
http://arxiv.org/abs/2407.17929
Attribution maps are one of the most established tools to explain the functioning of computer vision models. They assign importance scores to input features, indicating how relevant each feature is for the prediction of a deep neural network. While m
Externí odkaz:
http://arxiv.org/abs/2407.11910
A long-standing challenge in developing machine learning approaches has been the lack of high-quality labeled data. Recently, models trained with purely synthetic data, here termed synthetic clones, generated using large-scale pre-trained diffusion m
Externí odkaz:
http://arxiv.org/abs/2405.20469
Unsupervised semantic segmentation aims to automatically partition images into semantically meaningful regions by identifying global semantic categories within an image corpus without any form of annotation. Building upon recent advances in self-supe
Externí odkaz:
http://arxiv.org/abs/2404.16818
Video frame interpolation, the task of synthesizing new frames in between two or more given ones, is becoming an increasingly popular research target. However, the current evaluation of frame interpolation techniques is not ideal. Due to the plethora
Externí odkaz:
http://arxiv.org/abs/2403.17128
The field of explainable artificial intelligence (XAI) aims to uncover the inner workings of complex deep neural models. While being crucial for safety-critical domains, XAI inherently lacks ground-truth explanations, making its automatic evaluation
Externí odkaz:
http://arxiv.org/abs/2308.06248
Many convolutional neural networks (CNNs) rely on progressive downsampling of their feature maps to increase the network's receptive field and decrease computational cost. However, this comes at the price of losing granularity in the feature maps, li
Externí odkaz:
http://arxiv.org/abs/2305.09504
Most deep learning methods for video frame interpolation consist of three main components: feature extraction, motion estimation, and image synthesis. Existing approaches are mainly distinguishable in terms of how these modules are designed. However,
Externí odkaz:
http://arxiv.org/abs/2211.14005
The high-quality images yielded by generative adversarial networks (GANs) have motivated investigations into their application for image editing. However, GANs are often limited in the control they provide for performing specific edits. One of the pr
Externí odkaz:
http://arxiv.org/abs/2211.12209