153 citations · 251 across the 9 of their papers we have counts for
7 papers · 1 filter
Fine-tuning Image Transformers using Learnable Memory
Mark Sandler, Andrey Zhmoginov, Max Vladymyrov +1
In this paper we propose augmenting Vision Transformer models with learnable memory tokens. Our approach allows the model to adapt to new tasks, using few parameters, while optiona…
Image segmentation via Cellular Automata
Mark Sandler, Andrey Zhmoginov, Liangcheng Luo +3
In this paper, we propose a new approach for building cellular automata to solve real-world segmentation problems. We design and train a cellular automaton that can successfully se…
Non-discriminative data or weak model? On the relative importance of data and model resolution
Mark Sandler, Jonathan Baccash, Andrey Zhmoginov +1
We explore the question of how the resolution of the input image ("input resolution") affects the performance of a neural network when compared to the resolution of the hidden laye…
Information-Bottleneck Approach to Salient Region Discovery
Andrey Zhmoginov, Ian Fischer, Mark Sandler
We propose a new method for learning image attention masks in a semi-supervised setting based on the Information Bottleneck principle. Provided with a set of labeled images, the ma…
Searching for MobileNetV3
Andrew Howard, Mark Sandler, Grace Chu +9
We present the next generation of MobileNets based on a combination of complementary search techniques as well as a novel architecture design. MobileNetV3 is tuned to mobile phone…
CycleGAN, a Master of Steganography
Casey Chu, Andrey Zhmoginov, Mark Sandler
CycleGAN (Zhu et al. 2017) is one recent successful approach to learn a transformation between two image distributions. In a series of experiments, we demonstrate an intriguing pro…