93 citations · 224 across the 17 of their papers we have counts for
4 papers · 1 filter
Prismer: A Vision-Language Model with Multi-Task Experts
Shikun Liu, Linxi Fan, Edward Johns +3
Recent vision-language models have shown impressive multi-modal generation capabilities. However, typically they require training huge models on massive datasets. As a more scalabl…
Auto-Lambda: Disentangling Dynamic Task Relationships
Shikun Liu, Stephen James, Andrew J. Davison +1
Understanding the structure of multiple related tasks allows for multi-task learning to improve the generalisation ability of one or all of them. However, it usually requires train…
Shape Adaptor: A Learnable Resizing Module
Shikun Liu, Zhe Lin, Yilin Wang +3
We present a novel resizing module for neural networks: shape adaptor, a drop-in enhancement built on top of traditional resizing layers, such as pooling, bilinear sampling, and st…
Self-Supervised Generalisation with Meta Auxiliary Learning
Shikun Liu, Andrew J. Davison, Edward Johns
Learning with auxiliary tasks can improve the ability of a primary task to generalise. However, this comes at the cost of manually labelling auxiliary data. We propose a new method…