Zobrazeno 1 - 10
of 114
pro vyhledávání: '"Zhai, Xiaohua"'
Autor:
Beyer, Lucas, Steiner, Andreas, Pinto, André Susano, Kolesnikov, Alexander, Wang, Xiao, Salz, Daniel, Neumann, Maxim, Alabdulmohsin, Ibrahim, Tschannen, Michael, Bugliarello, Emanuele, Unterthiner, Thomas, Keysers, Daniel, Koppula, Skanda, Liu, Fangyu, Grycner, Adam, Gritsenko, Alexey, Houlsby, Neil, Kumar, Manoj, Rong, Keran, Eisenschlos, Julian, Kabra, Rishabh, Bauer, Matthias, Bošnjak, Matko, Chen, Xi, Minderer, Matthias, Voigtlaender, Paul, Bica, Ioana, Balazevic, Ivana, Puigcerver, Joan, Papalampidi, Pinelopi, Henaff, Olivier, Xiong, Xi, Soricut, Radu, Harmsen, Jeremiah, Zhai, Xiaohua
PaliGemma is an open Vision-Language Model (VLM) that is based on the SigLIP-So400m vision encoder and the Gemma-2B language model. It is trained to be a versatile and broadly knowledgeable base model that is effective to transfer. It achieves strong
Externí odkaz:
http://arxiv.org/abs/2407.07726
Autor:
Fan, Yue, Xian, Yongqin, Zhai, Xiaohua, Kolesnikov, Alexander, Naeem, Muhammad Ferjad, Schiele, Bernt, Tombari, Federico
Building generalized models that can solve many computer vision tasks simultaneously is an intriguing direction. Recent works have shown image itself can be used as a natural interface for general-purpose visual perception and demonstrated inspiring
Externí odkaz:
http://arxiv.org/abs/2407.00503
Autor:
Pouget, Angéline, Beyer, Lucas, Bugliarello, Emanuele, Wang, Xiao, Steiner, Andreas Peter, Zhai, Xiaohua, Alabdulmohsin, Ibrahim
We study cultural and socioeconomic diversity in contrastive vision-language models (VLMs). Using a broad range of benchmark datasets and evaluation metrics, we bring to attention several important findings. First, the common filtering of training da
Externí odkaz:
http://arxiv.org/abs/2405.13777
Autor:
Wan, Bo, Tschannen, Michael, Xian, Yongqin, Pavetic, Filip, Alabdulmohsin, Ibrahim, Wang, Xiao, Pinto, André Susano, Steiner, Andreas, Beyer, Lucas, Zhai, Xiaohua
Image captioning has been shown as an effective pretraining method similar to contrastive pretraining. However, the incorporation of location-aware information into visual pretraining remains an area with limited research. In this paper, we propose a
Externí odkaz:
http://arxiv.org/abs/2403.19596
Autor:
Alabdulmohsin, Ibrahim, Wang, Xiao, Steiner, Andreas, Goyal, Priya, D'Amour, Alexander, Zhai, Xiaohua
Publikováno v:
ICLR 2024
We study the effectiveness of data-balancing for mitigating biases in contrastive language-image pretraining (CLIP), identifying areas of strength and limitation. First, we reaffirm prior conclusions that CLIP models can inadvertently absorb societal
Externí odkaz:
http://arxiv.org/abs/2403.04547
Autor:
Naeem, Muhammad Ferjad, Xian, Yongqin, Zhai, Xiaohua, Hoyer, Lukas, Van Gool, Luc, Tombari, Federico
Image-Text pretraining on web-scale image caption datasets has become the default recipe for open vocabulary classification and retrieval models thanks to the success of CLIP and its variants. Several works have also used CLIP features for dense pred
Externí odkaz:
http://arxiv.org/abs/2310.13355
Autor:
Chen, Xi, Wang, Xiao, Beyer, Lucas, Kolesnikov, Alexander, Wu, Jialin, Voigtlaender, Paul, Mustafa, Basil, Goodman, Sebastian, Alabdulmohsin, Ibrahim, Padlewski, Piotr, Salz, Daniel, Xiong, Xi, Vlasic, Daniel, Pavetic, Filip, Rong, Keran, Yu, Tianli, Keysers, Daniel, Zhai, Xiaohua, Soricut, Radu
This paper presents PaLI-3, a smaller, faster, and stronger vision language model (VLM) that compares favorably to similar models that are 10x larger. As part of arriving at this strong performance, we compare Vision Transformer (ViT) models pretrain
Externí odkaz:
http://arxiv.org/abs/2310.09199
Autor:
Tschannen, Michael, Kumar, Manoj, Steiner, Andreas, Zhai, Xiaohua, Houlsby, Neil, Beyer, Lucas
Contrastive pretraining on image-text pairs from the web is one of the most popular large-scale pretraining strategies for vision backbones, especially in the context of large multimodal models. At the same time, image captioning on this type of data
Externí odkaz:
http://arxiv.org/abs/2306.07915
Autor:
Chen, Xi, Djolonga, Josip, Padlewski, Piotr, Mustafa, Basil, Changpinyo, Soravit, Wu, Jialin, Ruiz, Carlos Riquelme, Goodman, Sebastian, Wang, Xiao, Tay, Yi, Shakeri, Siamak, Dehghani, Mostafa, Salz, Daniel, Lucic, Mario, Tschannen, Michael, Nagrani, Arsha, Hu, Hexiang, Joshi, Mandar, Pang, Bo, Montgomery, Ceslee, Pietrzyk, Paulina, Ritter, Marvin, Piergiovanni, AJ, Minderer, Matthias, Pavetic, Filip, Waters, Austin, Li, Gang, Alabdulmohsin, Ibrahim, Beyer, Lucas, Amelot, Julien, Lee, Kenton, Steiner, Andreas Peter, Li, Yang, Keysers, Daniel, Arnab, Anurag, Xu, Yuanzhong, Rong, Keran, Kolesnikov, Alexander, Seyedhosseini, Mojtaba, Angelova, Anelia, Zhai, Xiaohua, Houlsby, Neil, Soricut, Radu
We present the training recipe and results of scaling up PaLI-X, a multilingual vision and language model, both in terms of size of the components and the breadth of its training task mixture. Our model achieves new levels of performance on a wide-ra
Externí odkaz:
http://arxiv.org/abs/2305.18565
Autor:
Kossen, Jannik, Collier, Mark, Mustafa, Basil, Wang, Xiao, Zhai, Xiaohua, Beyer, Lucas, Steiner, Andreas, Berent, Jesse, Jenatton, Rodolphe, Kokiopoulou, Efi
We introduce Three Towers (3T), a flexible method to improve the contrastive learning of vision-language models by incorporating pretrained image classifiers. While contrastive models are usually trained from scratch, LiT (Zhai et al., 2022) has rece
Externí odkaz:
http://arxiv.org/abs/2305.16999