2 citations · 4 across the 4 of their papers we have counts for
5 papers
Tunable Soft Equivariance with Guarantees
Md Ashiqur Rahman, Lim Jun Hao, Jeremiah Jiang +2
Equivariance is a fundamental property in computer vision models, yet strict equivariance is rarely satisfied in real-world data, which can limit a model's performance. Controlling…
Shift-Equivariant Complex-Valued Convolutional Neural Networks
Quentin Gabot, Teck-Yian Lim, Jérémy Fix +3
Convolutional neural networks have shown remarkable performance in recent years on various computer vision problems. However, the traditional convolutional neural network architect…
Local Scale Equivariance with Latent Deep Equilibrium Canonicalizer
Md Ashiqur Rahman, Chiao-An Yang, Michael N. Cheng +4
Scale variation is a fundamental challenge in computer vision. Objects of the same class can have different sizes, and their perceived size is further affected by the distance from…
Making Vision Transformers Truly Shift-Equivariant
Renan A. Rojas-Gomez, Teck-Yian Lim, Minh N. Do +1
For computer vision, Vision Transformers (ViTs) have become one of the go-to deep net architectures. Despite being inspired by Convolutional Neural Networks (CNNs), ViTs' output re…
Learnable Polyphase Sampling for Shift Invariant and Equivariant Convolutional Networks
Renan A. Rojas-Gomez, Teck-Yian Lim, Alexander G. Schwing +2
We propose learnable polyphase sampling (LPS), a pair of learnable down/upsampling layers that enable truly shift-invariant and equivariant convolutional networks. LPS can be train…