118 citations · 185 across the 8 of their papers we have counts for
7 papers · 1 filter
Optimizing ViViT Training: Time and Memory Reduction for Action Recognition
Shreyank N Gowda, Anurag Arnab, Jonathan Huang
In this paper, we address the challenges posed by the substantial training time and memory consumption associated with video transformers, focusing on the ViViT (Video Vision Trans…
PaLI-X: On Scaling up a Multilingual Vision and Language Model
Xi Chen, Josip Djolonga, Piotr Padlewski +40
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 t…
End-to-End Spatio-Temporal Action Localisation with Video Transformers
Alexey Gritsenko, Xuehan Xiong, Josip Djolonga +5
The most performant spatio-temporal action localisation models use external person proposals and complex external memory banks. We propose a fully end-to-end, purely-transformer ba…
Scaling Vision Transformers to 22 Billion Parameters
Mostafa Dehghani, Josip Djolonga, Basil Mustafa +39
The scaling of Transformers has driven breakthrough capabilities for language models. At present, the largest large language models (LLMs) contain upwards of 100B parameters. Visio…
Beyond Transfer Learning: Co-finetuning for Action Localisation
Anurag Arnab, Xuehan Xiong, Alexey Gritsenko +6
Transfer learning is the predominant paradigm for training deep networks on small target datasets. Models are typically pretrained on large ``upstream'' datasets for classification…
End-to-end Generative Pretraining for Multimodal Video Captioning
Paul Hongsuck Seo, Arsha Nagrani, Anurag Arnab +1
Recent video and language pretraining frameworks lack the ability to generate sentences. We present Multimodal Video Generative Pretraining (MV-GPT), a new pretraining framework fo…