50 citations · 126 across the 12 of their papers we have counts for
3 papers · 1 filter
MAMo: Leveraging Memory and Attention for Monocular Video Depth Estimation
Rajeev Yasarla, Hong Cai, Jisoo Jeong +3
We propose MAMo, a novel memory and attention frame-work for monocular video depth estimation. MAMo can augment and improve any single-image depth estimation networks into video de…
DIFT: Dynamic Iterative Field Transforms for Memory Efficient Optical Flow
Risheek Garrepalli, Jisoo Jeong, Rajeswaran C Ravindran +2
Recent advancements in neural network-based optical flow estimation often come with prohibitively high computational and memory requirements, presenting challenges in their model a…
DistractFlow: Improving Optical Flow Estimation via Realistic Distractions and Pseudo-Labeling
Jisoo Jeong, Hong Cai, Risheek Garrepalli +1
We propose a novel data augmentation approach, DistractFlow, for training optical flow estimation models by introducing realistic distractions to the input frames. Based on a mixin…