Zobrazeno 1 - 10
of 398
pro vyhledávání: '"Liwicki, Marcus"'
Autor:
Chippa, Meenakshi Subhash, Chhipa, Prakash Chandra, De, Kanjar, Liwicki, Marcus, Saini, Rajkumar
Perspective distortion (PD) leads to substantial alterations in the shape, size, orientation, angles, and spatial relationships of visual elements in images. Accurately determining camera intrinsic and extrinsic parameters is challenging, making it h
Externí odkaz:
http://arxiv.org/abs/2410.03686
Handwritten Text Generation (HTG) conditioned on text and style is a challenging task due to the variability of inter-user characteristics and the unlimited combinations of characters that form new words unseen during training. Diffusion Models have
Externí odkaz:
http://arxiv.org/abs/2409.06065
The evaluation of generative models for natural image tasks has been extensively studied. Similar protocols and metrics are used in cases with unique particularities, such as Handwriting Generation, even if they might not be completely appropriate. I
Externí odkaz:
http://arxiv.org/abs/2409.02683
Autor:
Khan, Muhammad Saif Ullah, Sinha, Sankalp, Stricker, Didier, Liwicki, Marcus, Afzal, Muhammad Zeshan
Reconstructing texture-less surfaces poses unique challenges in computer vision, primarily due to the lack of specialized datasets that cater to the nuanced needs of depth and normals estimation in the absence of textural information. We introduce "S
Externí odkaz:
http://arxiv.org/abs/2406.15831
In the Multi-task Learning (MTL) framework, every task demands distinct feature representations, ranging from low-level to high-level attributes. It is vital to address the specific (feature/parameter) needs of each task, especially in computationall
Externí odkaz:
http://arxiv.org/abs/2406.03048
Autor:
Chhipa, Prakash Chandra, De, Kanjar, Chippa, Meenakshi Subhash, Saini, Rajkumar, Liwicki, Marcus
The challenge of Out-Of-Distribution (OOD) robustness remains a critical hurdle towards deploying deep vision models. Vision-Language Models (VLMs) have recently achieved groundbreaking results. VLM-based open-vocabulary object detection extends the
Externí odkaz:
http://arxiv.org/abs/2405.14874
Autor:
Chhipa, Prakash Chandra, Chippa, Meenakshi Subhash, De, Kanjar, Saini, Rajkumar, Liwicki, Marcus, Shah, Mubarak
Perspective distortion (PD) causes unprecedented changes in shape, size, orientation, angles, and other spatial relationships of visual concepts in images. Precisely estimating camera intrinsic and extrinsic parameters is a challenging task that prev
Externí odkaz:
http://arxiv.org/abs/2405.02296
Autor:
Aehle, Max, Arsini, Lorenzo, Barreiro, R. Belén, Belias, Anastasios, Bury, Florian, Cebrian, Susana, Demin, Alexander, Dickinson, Jennet, Donini, Julien, Dorigo, Tommaso, Doro, Michele, Gauger, Nicolas R., Giammanco, Andrea, Gray, Lindsey, González, Borja S., Kain, Verena, Kieseler, Jan, Kusch, Lisa, Liwicki, Marcus, Maier, Gernot, Nardi, Federico, Ratnikov, Fedor, Roussel, Ryan, de Austri, Roberto Ruiz, Sandin, Fredrik, Schenk, Michael, Scarpa, Bruno, Silva, Pedro, Strong, Giles C., Vischia, Pietro
In this article we examine recent developments in the research area concerning the creation of end-to-end models for the complete optimization of measuring instruments. The models we consider rely on differentiable programming methods and on the spec
Externí odkaz:
http://arxiv.org/abs/2310.05673
Model sparsification in deep learning promotes simpler, more interpretable models with fewer parameters. This not only reduces the model's memory footprint and computational needs but also shortens inference time. This work focuses on creating sparse
Externí odkaz:
http://arxiv.org/abs/2308.12114
We replace the multiplication and sigmoid function of the conventional recurrent gate with addition and ReLU activation. This mechanism is designed to maintain long-term memory for sequence processing but at a reduced computational cost, thereby open
Externí odkaz:
http://arxiv.org/abs/2308.05629