I was a DPhil student at the University of Oxford in the visual geometry group (VGG) supervised by Professor Andrew Zisserman. Olivia Wiles. Oct 6, 2022 · Compressed Vision for Efficient Video Understanding. Operating on compressed videos improves efficiency at all pipeline levels -- data transfer, speed and memory -- making it possible to train models faster and on much longer videos. We demonstrate that with our compressed vision pipeline Mar 11, 2023 · In this work, we propose and investigate a new, efficient and more scalable video pipeline – compressed vision – which preserves the ability to use most of the state-of-the-art data processing and machine learning techniques developed for videos. The vast majority of computer vision research, however, still focuses on individual images or short videos Mar 11, 2023 · Operating on compressed videos improves efficiency at all pipeline levels – data transfer, speed and memory – making it possible to train models faster and on much longer videos. springer. To the best of our knowledge, this is the first work to address this We demonstrate that with our compressed vision pipeline, we can train video models more efficiently on popular benchmarks such as Kinetics600 and COIN. Experience and reasoning occur across multiple temporal scales: milliseconds, seconds, hours or days. Below each layer, we write the size of the output tensor for the given input size. I work as a Senior Researcher at DeepMind, focussing on robustness of various forms: robustness to distribution shift and robustness to adversarial examples. . Before that, I studied computer science at the Mar 11, 2023 · In this work, we propose and investigate a new, efficient and more scalable video pipeline – compressed vision – which preserves the ability to use most of the state-of-the-art data processing and machine learning techniques developed for videos. Mar 11, 2023 · In this work, we propose and investigate a new, efficient and more scalable video pipeline – compressed vision – which preserves the ability to use most of the state-of-the-art data processing and machine learning techniques developed for videos. Videos are first compressed using a neural compressor c 𝑐 c to produce codes. Compressed Vision for Efficient Video Understanding . The main contributions of this paper are summarized in four-fold: • We propose CVPT, a novel visual prompt tuning framework, which enables pre-trained raw video models to adapt to compressed video understanding tasks. We demonstrate that with our compressed vision pipeline Mar 11, 2023 · Operating on compressed videos improves efficiency at all pipeline levels – data transfer, speed and memory – making it possible to train models faster and on much longer videos. com We demonstrate that with our compressed vision pipeline, we can train video models more efficiently on popular benchmarks such as Kinetics600 and COIN. The neural codes are directly used to train video tasks t 1 … t T subscript 𝑡 1 … subscript 𝑡 𝑇 t_{1}\dots t_{T}. We also perform proof-of-concept experiments with new tasks defined over hour-long videos at standard frame rates. We demonstrate that with our compressed vision pipeline, we can train video models more efficiently on popular benchmarks such as Kinetics600 and COIN. See full list on link. Figure 1: The compressed vision pipeline. Oct 6, 2022 · Processing compressed signals has, however, the downside of precluding standard augmentation techniques if done naively. Oct 6, 2022 · We demonstrate that with our compressed vision pipeline, we can train video models more efficiently on popular benchmarks such as Kinetics600 and COIN. The vast majority of computer vision research, however, still focuses on individual images or short videos lasting only a few seconds. Fig. Olivia Wiles, Joao Carreira, Iain Barr, Andrew Zisserman, Mateusz Malinowski. Processing compressed signals has, however, the downside of precluding standard augmentation techniques if done naively. Mar 11, 2023 · Operating on compressed videos improves efficiency at all pipeline levels – data transfer, speed and memory – making it possible to train models faster and on much longer videos. These are stored on a disk and the original videos can be discarded. efficient representation learning of compressed videos. . We address that by introducing a small We demonstrate that with our compressed vision pipeline, we can train video models more efficiently on popular benchmarks such as Kinetics600 and COIN. 13: How we modify the standard S3D architecture for smaller compression rates. In comparison to Figure 12, we only change the strides of the first convolution, the first two max pools and modify the output channels in the first two convolutional layers. We address that by introducing a small network that can apply transformations to latent codes corresponding to commonly used augmentations in the original video space. - "Compressed Vision for Efficient Video Understanding" Oct 6, 2022 · We demonstrate that with our compressed vision pipeline, we can train video models more efficiently on popular benchmarks such as Kinetics600 and COIN. Processing such long videos is impossible without using compressed representation. co tp ba em bc in jw fp lo wg