1 citations · 1 across the 5 of their papers we have counts for
6 papers
Structured Initialization for Vision Transformers
Jianqiao Zheng, Xueqian Li, Hemanth Saratchandran +1
Convolutional Neural Networks (CNNs) inherently encode strong inductive biases, enabling effective generalization on small-scale datasets. In this paper, we propose integrating thi…
Structured Initialization for Attention in Vision Transformers
Jianqiao Zheng, Xueqian Li, Simon Lucey
The training of vision transformer (ViT) networks on small-scale datasets poses a significant challenge. By contrast, convolutional neural networks (CNNs) have an architectural ind…
Self-Supervised Multi-Frame Neural Scene Flow
Dongrui Liu, Daqi Liu, Xueqian Li +5
Neural Scene Flow Prior (NSFP) and Fast Neural Scene Flow (FNSF) have shown remarkable adaptability in the context of large out-of-distribution autonomous driving. Despite their su…
Fast Kernel Scene Flow
Xueqian Li, Simon Lucey
In contrast to current state-of-the-art methods, such as NSFP [25], which employ deep implicit neural functions for modeling scene flow, we present a novel approach that utilizes c…
Convolutional Initialization for Data-Efficient Vision Transformers
Jianqiao Zheng, Xueqian Li, Simon Lucey
Training vision transformer networks on small datasets poses challenges. In contrast, convolutional neural networks (CNNs) can achieve state-of-the-art performance by leveraging th…
Multi-Body Neural Scene Flow
Kavisha Vidanapathirana, Shin-Fang Chng, Xueqian Li +1
The test-time optimization of scene flow - using a coordinate network as a neural prior - has gained popularity due to its simplicity, lack of dataset bias, and state-of-the-art pe…