no code implementations • 5 Feb 2024 • Otniel-Bogdan Mercea, Alexey Gritsenko, Cordelia Schmid, Anurag Arnab
Here, we outperform a prior adaptor-based method which could only scale to a 1 billion parameter backbone, or fully-finetuning a smaller backbone, with the same GPU and less training time.
no code implementations • ICCV 2023 • Georg Heigold, Matthias Minderer, Alexey Gritsenko, Alex Bewley, Daniel Keysers, Mario Lučić, Fisher Yu, Thomas Kipf
Our model is end-to-end trainable on video data and enjoys improved temporal consistency compared to tracking-by-detection baselines, while retaining the open-world capabilities of the backbone detector.
1 code implementation • 12 Jul 2023 • Mostafa Dehghani, Basil Mustafa, Josip Djolonga, Jonathan Heek, Matthias Minderer, Mathilde Caron, Andreas Steiner, Joan Puigcerver, Robert Geirhos, Ibrahim Alabdulmohsin, Avital Oliver, Piotr Padlewski, Alexey Gritsenko, Mario Lučić, Neil Houlsby
The ubiquitous and demonstrably suboptimal choice of resizing images to a fixed resolution before processing them with computer vision models has not yet been successfully challenged.
1 code implementation • NeurIPS 2023 • Matthias Minderer, Alexey Gritsenko, Neil Houlsby
However, with OWL-ST, we can scale to over 1B examples, yielding further large improvement: With an L/14 architecture, OWL-ST improves AP on LVIS rare classes, for which the model has seen no human box annotations, from 31. 2% to 44. 6% (43% relative improvement).
Ranked #1 on Zero-Shot Object Detection on LVIS v1.0 minival (using extra training data)
no code implementations • 24 Apr 2023 • Alexey Gritsenko, Xuehan Xiong, Josip Djolonga, Mostafa Dehghani, Chen Sun, Mario Lučić, Cordelia Schmid, Anurag Arnab
The most performant spatio-temporal action localisation models use external person proposals and complex external memory banks.
Ranked #1 on Action Recognition on AVA v2.1 (using extra training data)
1 code implementation • 10 Feb 2023 • Mostafa Dehghani, Josip Djolonga, Basil Mustafa, Piotr Padlewski, Jonathan Heek, Justin Gilmer, Andreas Steiner, Mathilde Caron, Robert Geirhos, Ibrahim Alabdulmohsin, Rodolphe Jenatton, Lucas Beyer, Michael Tschannen, Anurag Arnab, Xiao Wang, Carlos Riquelme, Matthias Minderer, Joan Puigcerver, Utku Evci, Manoj Kumar, Sjoerd van Steenkiste, Gamaleldin F. Elsayed, Aravindh Mahendran, Fisher Yu, Avital Oliver, Fantine Huot, Jasmijn Bastings, Mark Patrick Collier, Alexey Gritsenko, Vighnesh Birodkar, Cristina Vasconcelos, Yi Tay, Thomas Mensink, Alexander Kolesnikov, Filip Pavetić, Dustin Tran, Thomas Kipf, Mario Lučić, Xiaohua Zhai, Daniel Keysers, Jeremiah Harmsen, Neil Houlsby
The scaling of Transformers has driven breakthrough capabilities for language models.
Ranked #1 on Zero-Shot Transfer Image Classification on ObjectNet
no code implementations • 5 Oct 2022 • Jonathan Ho, William Chan, Chitwan Saharia, Jay Whang, Ruiqi Gao, Alexey Gritsenko, Diederik P. Kingma, Ben Poole, Mohammad Norouzi, David J. Fleet, Tim Salimans
We present Imagen Video, a text-conditional video generation system based on a cascade of video diffusion models.
Ranked #1 on Video Generation on LAION-400M
no code implementations • 8 Jul 2022 • Anurag Arnab, Xuehan Xiong, Alexey Gritsenko, Rob Romijnders, Josip Djolonga, Mostafa Dehghani, Chen Sun, Mario Lučić, Cordelia Schmid
Transfer learning is the predominant paradigm for training deep networks on small target datasets.
2 code implementations • 12 May 2022 • Matthias Minderer, Alexey Gritsenko, Austin Stone, Maxim Neumann, Dirk Weissenborn, Alexey Dosovitskiy, Aravindh Mahendran, Anurag Arnab, Mostafa Dehghani, Zhuoran Shen, Xiao Wang, Xiaohua Zhai, Thomas Kipf, Neil Houlsby
Combining simple architectures with large-scale pre-training has led to massive improvements in image classification.
Ranked #1 on One-Shot Object Detection on MS COCO
3 code implementations • 7 Apr 2022 • Jonathan Ho, Tim Salimans, Alexey Gritsenko, William Chan, Mohammad Norouzi, David J. Fleet
Generating temporally coherent high fidelity video is an important milestone in generative modeling research.
no code implementations • 10 Dec 2021 • Yang Li, Gang Li, Xin Zhou, Mostafa Dehghani, Alexey Gritsenko
Our model consists of a multimodal Transformer encoder that jointly encodes UI images and structures, and performs UI object detection when the UI structures are absent in the input.
1 code implementation • CVPR 2022 • Mostafa Dehghani, Alexey Gritsenko, Anurag Arnab, Matthias Minderer, Yi Tay
Scenic is an open-source JAX library with a focus on Transformer-based models for computer vision research and beyond.